JP2019133541A - Separation method, separation apparatus and separation program - Google Patents

Abstract

To enable appropriately separating data accessed by a plurality of tasks from a target system.SOLUTION: A processing unit 1b identifies first data accessed to a first program of a target system 2 and second data accessed to a second program of the system 2. The processing unit 1b outputs the data accessed to both of the first program and the second program as common data referenced or updated by a plurality of tasks.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a separation method, a separation apparatus, and a separation program.

  Currently, computer systems that perform various types of information processing are used. The computer system implements a predetermined function corresponding to the program by executing the program. In a computer system, a large number of programs and data can be used. Several programs may cooperate to realize one function.

  In the operation of a computer system, an existing program may be modified or an existing function may be transferred to another computer system. In this case, a program or data related to the function to be corrected or transferred is extracted, and the extracted program or other program or data related to the program is corrected or transferred. However, the more programs and data that are used and the more complicated the relationships between programs, the more difficult it is to find programs and data that are related to each other.

  Therefore, for example, there is a proposal of a dividing device that treats software as a set of entities that are constituent elements and divides the software into subsets (for example, clusters according to functions, tasks, tasks, etc.) that are easy to understand the software. . The dividing apparatus extracts correspondence information in which the relation source / relationship destination entities are associated from the software source code, and based on the weight related to the dependency specified by the correspondence information, the total weight becomes higher than the expected value. Thus, the entity group is divided into a plurality of clusters.

JP2013-148987A

  As described above, there is a method of dividing an entity group into a plurality of units (for example, clusters of business units) so that the sum of dependency weights between entities (for example, programs and data) is higher than an expected value. It is done. In this method, as a result of the division, data used for processing of the computer system is included in one of the division units together with a program having a relatively strong relationship with the data. Then, for example, there is a problem that it becomes difficult to understand the relationship between the data and another business while the relationship between the data and a single business with a relatively strong relationship is easy to understand.

  In one aspect, an object of the present invention is to appropriately separate data accessed by a plurality of tasks from a target system.

  In one aspect, a separation method is provided for separating corresponding data for each business from a target system. In this separation method, the computer identifies first data accessed by the first program of the target system and second data accessed by the second program of the target system, and the first data Data accessed by both the program and the second program is output as common data that is referred to or updated from a plurality of tasks.

  In one aspect, data accessed by a plurality of tasks can be appropriately separated from the target system.

It is a figure which shows the separation apparatus of 1st Embodiment. It is a figure which shows the example of the information processing system of 2nd Embodiment. It is a figure which shows the hardware example of an analysis server. It is a figure which shows the function example of an analysis server. It is a figure which shows the example of a program asset. It is a figure which shows the example of the calling structure of a program. It is a figure which shows the example of a production | generation of a service component candidate. It is a figure which shows the example of the production | generation result of a service component candidate. It is a figure which shows the example of the classification result of a service component and common data. It is a figure which shows the example of the classification result of a semi-service component. It is a figure which shows the example (continuation) of the classification | category result of a semi-service component. It is a flowchart which shows the process example of an analysis server. It is a flowchart which shows the process example of a program analysis. It is a flowchart which shows the example of a process of common data extraction. It is a flowchart which shows the process example of a service component classification. It is a figure which shows the example of a display of a classification result (the 1). It is a figure which shows the example of a display of a classification result (the 2). It is a figure which shows the comparative example of a classification | category. It is a figure which shows the example of the classification | category by an analysis server. It is a figure which shows the example of a transfer of the one part function of the existing system.

Hereinafter, the present embodiment will be described with reference to the drawings.
[First Embodiment]
FIG. 1 is a diagram illustrating a separation apparatus according to a first embodiment. The separation device 1 is connected to the system 2. The separation device 1 may be connected to the system 2 via a network. The system 2 executes various programs that perform processing related to the user's business. One or more programs cooperate with each other and access one or more data elements related to the business to realize processing related to the business.

  A set of a plurality of programs and data elements used in the system 2 is called a program asset. For example, the program assets include programs X, Y,... And data elements d1, d2,. For example, the program X describes the processing of the business A1. Further, the program Y describes the processing of the business A2. Each of the programs X, Y,... May be an aggregate of a plurality of programs (for example, a group of programs associated with each other by a calling relationship in which a certain program calls another program). The unit of “business” may be called a function, service component, software component, or the like. The separation device 1 is also connected to the display device 3. The display device 3 displays the content of information received from the separation device 1.

  The separation device 1 provides a function of separating corresponding data for each business from a system to be analyzed (for example, the system 2) (or a function for classifying corresponding data for each business). The separation device 1 includes a storage unit 1a and a processing unit 1b.

  The storage unit 1a may be a volatile semiconductor memory such as a RAM (Random Access Memory) or a non-volatile storage such as an HDD (Hard Disk Drive) or a flash memory. The processing unit 1b is, for example, a processor such as a CPU (Central Processing Unit) or a DSP (Digital Signal Processor). However, the processing unit 1b may include an electronic circuit for a specific purpose such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA). The processor executes a program stored in a memory such as a RAM (or the storage unit 1a). A set of processors may be referred to as “multiprocessor” or simply “processor”.

  The storage unit 1a stores a copy of all or part of the program assets stored in the system 2. For example, the storage unit 1a may store some program assets used for the processing of the processing unit 1b.

  The processing unit 1b specifies first data accessed by the first program of the target system and second data accessed by the second program of the target system. Access is the reference or update of data by the corresponding program.

  For example, the data set D1 is the entire data element in the program asset. The processing unit 1b analyzes the code of the program X (first program) of the system 2, and specifies the data elements d1, d2, d3, d4, and d5 accessed by the program X from the code from the data set D1. The first data set D11 is a set having data elements d1, d2, d3, d4, and d5 as elements. The first data set D11 is data (first data) accessed by the program X (first program).

  Further, the processing unit 1b analyzes the code of the program Y (second program) of the system 2 and identifies the data elements d4, d5, d6, d7, and d8 accessed by the program Y from the code from the data set D1. To do. The second data set D12 is a set having data elements d4, d5, d6, d7, and d8 as elements. The second data set D12 is data (second data) accessed by the program Y (second program).

  The processing unit 1b outputs data accessed by both the first program and the second program as common data that is referred to or updated by a plurality of tasks. In the above example, data elements belonging to both the first data set D11 and the second data set D12 are data accessed by both the first program and the second program. Therefore, the processing unit 1b may output data elements belonging to both the first data set D11 and the second data set D12 as common data that is referred to or updated by a plurality of tasks. .

  For example, the processing unit 1b identifies the data elements d4 and d5 accessed by both the programs X and Y by comparing the first data set D11 and the second data set D12. Then, the processing unit 1b determines that the data elements d4 and d5 are common data that is referred to or updated from the business corresponding to the program X and the business corresponding to the program Y. The processing unit 1b outputs information indicating the data element d4 and information indicating the data element d5 (for example, identification information of the data element d4 and identification information of the data element d5) as information indicating common data.

  Further, the processing unit 1b may determine that the data elements d1, d2, and d3 that are updated only from the program X in the first data set D11 are the unique data of the program X. . Then, the processing unit 1b may output information indicating the specific data (for example, identification information of each of the data elements d1, d2, and d3).

  Further, the processing unit 1b may determine that the data elements d6, d7, and d8 that are updated only from the program Y among the programs X and Y in the second data set D12 are unique data of the program Y. . Then, the processing unit 1b may output information indicating the specific data (for example, identification information of each of the data elements d6, d7, and d8).

  The output screen 4 is an example of a screen output by the separation device 1. The display device 3 receives the information on the output screen 4 from the separation device 1 and displays the output screen 4. The output screen 4 has screen components 4a, 4b, and 4c.

  The screen component 4a is an image for collectively displaying icons indicating the program X and the data elements d1, d2, and d3 (unique data) as one unit. The processing unit 1b displays the business name (for example, “business A1”) realized by the program X and the data elements d1, d2, and d3, the words / phrases related to the corresponding business, etc. ) And the like may be included in the screen component 4a.

  The screen component 4b is an image for collectively displaying icons indicating the program Y and the data elements d6, d7, d8 (unique data) in one unit. The processing unit 1b displays the business name (for example, “business A2”) realized by the program Y and the data elements d6, d7, and d8, the words related to the business, etc. ) Etc. and may be included in the screen component 4b.

  The screen component 4c is an image for collectively displaying icons indicating the data elements d4 and d5 to be referred to or updated from both the programs X and Y as one unit. The screen component 4c is displayed in an area that does not overlap with the areas of the screen components 4a and 4b.

  In the output screen 4, the relationship that the program accesses (refers to / updates) the data is indicated by an arrow. That is, the user can grasp the following contents on the output screen 4. The program X accesses data elements d1, d2, and d3 classified as unique data. The program X accesses the data elements d4 and d5 classified as common data. The program Y accesses the data elements d6, d7, d8 classified as unique data. The program Y accesses the data elements d4 and d5 classified as common data.

  According to the separation device 1, data accessed by the first program of the target system and data accessed by the second program of the target system are specified. Then, the data accessed by both the first program and the second program is output as common data that is referred to or updated from a plurality of tasks by the separation device 1.

Thereby, data accessed by a plurality of tasks can be appropriately separated from the target system.
Here, for example, a method of dividing an entity group into a plurality of units (for example, clusters of business units) so that the sum of dependency weights between entities (for example, programs and data) becomes higher than an expected value is considered. It is done. In this method, as a result of the division, data used for system processing is included in one of the division units together with a program having a relatively strong relationship with the data. Then, for example, while it becomes easy to understand the relationship between the data and a single business having a relatively strong relationship, it is difficult to grasp the relationship between the data and another business.

  On the other hand, according to the separating apparatus 1, data accessed by both the first program and the second program is determined as common data, and information indicating the common data is output. It becomes possible to appropriately grasp the common data to be accessed. As a result, it is possible to extract from the system 2 a task (or a function in which a plurality of tasks are grouped together) having a plurality of programs associated through the common data as a unit.

  Then, for example, when correcting a certain program belonging to the plurality of programs, the user may specify another program associated with the program through common data as a program that may be affected. it can. That is, it becomes possible to appropriately grasp another program that may be affected by the modification of the program.

  Alternatively, the separating apparatus 1 can separate a part of work corresponding to a plurality of related programs and common data from the system 2 and transfer it to another system. That is, a part of business programs in the system 2 can be transferred to another system while suppressing the influence on other business programs.

  As described above, according to the separating apparatus 1, even when there are many complicatedly related programs, the common data and a plurality of related programs can be appropriately separated based on the common data.

[Second Embodiment]
FIG. 2 is a diagram illustrating an example of an information processing system according to the second embodiment. The information processing system according to the second embodiment includes an analysis server 100 and a business server 200. The analysis server 100 and the business server 200 are connected to the network 10. The network 10 is, for example, a LAN (Local Area Network). The network 10 is connected to the network 20. The network 20 is a wide area data communication network such as the Internet, for example. A migration destination server 300 is connected to the network 20.

  The analysis server 100 is a server computer that analyzes software executed on the business server 200. Software is composed of a large number of programs and data. On the other hand, business processing functions realized by software are diverse. Programs and data can be grouped into business units. A program (or program group) and data grouped into functional units are called service components.

  Program and data grouping (service componentization) is useful for partially modifying software and transferring some business functions to other servers. For example, by modifying a program or data relevant to a certain business, the influence on the program or data other than the business can be suppressed.

  However, the programs constituting the software executed by the business server 200 are intricately related to each other, and it is not easy for the user to grasp the relationship between the programs afterwards. Therefore, the analysis server 100 provides a function of classifying programs and data constituting the software according to business and presenting them to the user. The analysis server 100 is an example of the separation device 1 according to the first embodiment.

  The business server 200 is a server computer that performs processing related to a user's business. The processing of the business server 200 is realized by software executed on the business server 200. The business server 200 is an example of the system 2 according to the first embodiment. The business server 200 may be one server computer belonging to the system 2 of the first embodiment.

  The migration destination server 300 is a server computer that is a migration destination of a business (or a program or data corresponding to the business) separated from the business server 200. The migration destination server 300 may be, for example, a virtual computer (virtual machine) realized by a cloud system or a physical computer (physical machine) in an on-premises system.

  FIG. 3 is a diagram illustrating an example of hardware of the analysis server. The analysis server 100 includes a CPU 101, a RAM 102, an HDD 103, an image signal processing unit 104, an input signal processing unit 105, a medium reader 106, and a NIC (Network Interface Card) 107. Each piece of hardware is connected to the analysis server 100 bus. The business server 200 and the migration destination server 300 can also be realized using the same hardware as the analysis server 100.

  The CPU 101 is a processor that executes program instructions. The CPU 101 loads at least a part of the program and data stored in the HDD 103 into the RAM 102 and executes the program. Note that the CPU 101 may include a plurality of processor cores. The analysis server 100 may include a plurality of processors, and the processes described below may be executed in parallel using a plurality of processors or processor cores. A set of processors may be referred to as “multiprocessor” or simply “processor”.

  The RAM 102 is a volatile semiconductor memory that temporarily stores programs executed by the CPU 101 and data used by the CPU 101 for calculations. The analysis server 100 may include a memory of a type other than the RAM, or may include a plurality of memories.

  The HDD 103 is a non-volatile storage device that stores an OS (Operating System), software programs such as middleware and application software, and data. The analysis server 100 may include other types of storage devices such as flash memory and SSD (Solid State Drive), and may include a plurality of nonvolatile storage devices.

  The image signal processing unit 104 outputs an image to the display 11 connected to the analysis server 100 in accordance with a command from the CPU 101. As the display 11, a CRT (Cathode Ray Tube) display, a liquid crystal display, or the like can be used.

  The input signal processing unit 105 acquires an input signal from the input device 12 connected to the analysis server 100 and outputs it to the CPU 101. As the input device 12, a pointing device such as a mouse, a touch panel, a touch pad, a trackball, a keyboard, a remote controller, a button switch, or the like can be used. A plurality of types of input devices may be connected to the analysis server 100.

  The medium reader 106 is a device that reads programs and data recorded on the recording medium 13. As the recording medium 13, for example, a magnetic disk, an optical disk, a magneto-optical disk (MO), a semiconductor memory, or the like can be used. Magnetic disks include flexible disks (FD: Flexible Disk) and HDDs. The optical disc includes a CD (Compact Disc) and a DVD (Digital Versatile Disc).

  For example, the medium reader 106 copies a program or data read from the recording medium 13 to another recording medium such as the RAM 102 or the HDD 103. The read program is executed by the CPU 101, for example. The recording medium 13 may be a portable recording medium and may be used for distributing programs and data. In addition, the recording medium 13 and the HDD 103 may be referred to as computer-readable recording media.

  The NIC 107 is a communication interface that is connected to the network 10 and communicates with the business server 200 and the migration destination server 300 via the network 10. The NIC 107 is connected to a communication device such as a switch or a router with a cable, for example.

  FIG. 4 is a diagram illustrating a function example of the analysis server. The analysis server 100 includes a storage unit 110 and a control unit 120. The storage unit 110 is mounted using a storage area secured in the RAM 102 or the HDD 103, for example. The control unit 120 is implemented using a program, for example.

The storage unit 110 stores various types of information used for the processing of the control unit 120. The storage unit 110 includes a program storage unit 111 and an analysis result storage unit 112.
The program storage unit 111 stores program assets to be analyzed. The program assets include software programs (for example, source programs and object programs) executed on the business server 200.

  The analysis result storage unit 112 stores the analysis result of the program asset by the control unit 120. For example, the analysis result storage unit 112 stores related information indicating a relationship between a program and data, service component candidate information indicating a service component candidate that is a service component candidate, and service component information indicating a specified service component. Further, the analysis result storage unit 112 stores common component information indicating the specified common data and quasi-service component information indicating a quasi-service component associated with the common data.

The control unit 120 controls analysis of program assets by the analysis server 100. The control unit 120 includes a classification unit 121, a candidate generation unit 122, and an analysis unit 123.
The classification unit 121 analyzes a program and acquires a call relationship between programs. The classification unit 121 classifies a plurality of programs into program groups based on the calling relationship between programs. The classifying unit 121 identifies a starting program that has not been called from other programs among the programs, and traces the calling structure of the program by tracking the calling relations included in the starting program and other programs. Create for each program. The classification unit 121 identifies a set of programs (a group of programs) associated with each other based on the calling relationship based on the calling structure. The classification unit 121 generates related information indicating the relationship between the program group and the data, and stores it in the analysis result storage unit 112.

  The candidate generation unit 122 extracts service component candidates from the relationship between each program group classified by the classification unit 121 and the data. The candidate generation unit 122 generates service component candidate information indicating service component candidates and stores the service component candidate information in the analysis result storage unit 112.

  Here, the service component candidate is a group of relatively related programs and data, and is a service component candidate. For example, the candidate generation unit 122 may generate a service component candidate using a clustering technique described in JP2013-148987A. In this technology, it is assumed that an important entity that is a main line for expressing a role that a subset means and dependencies between the entities are included in the subset. Then, the branch and leaf end entities that hinder the understanding of the software's main line and the dependency relationships between entities are neglected when forming the subset. Whether it is important or not is determined by the number of dependencies.

  At this time, the candidate generation unit 122 may determine that the more the code for accessing Read / Write described in the program group for a certain data, the stronger the relation between the program group and the data. . Further, the candidate generation unit 122 may make the write access weight greater than the read access weight.

  The analysis unit 123 acquires a service component and a semi-service component based on the service component candidate generated by the candidate generation unit 122. The analysis unit 123 extracts, as common data, data accessed from other service component candidates among data related to a certain service component candidate. There are two types of common data. Common data accessed from a plurality of service component candidates and updated by at least one service component candidate is called global data. Further, common data that is accessed from a plurality of service component candidates and is not updated by any service component candidate is referred to as master data.

  The analysis unit 123 excludes the common data from the service component candidates to which it currently belongs (puts the common data outside the group). When the association between a certain program group and another program group is lost due to the exclusion of the common data from the service component candidates, the grouping of each corresponding program group is canceled. The analysis unit 123 sets the service component candidates that have not been ungrouped here as service components.

  However, when there are a plurality of program groups associated only by the common data, the analysis unit 123 groups the plurality of program groups and the common data again. The analysis unit 123 sets the group of programs and the group of common data that are grouped again as a semi-service component.

  The analysis unit 123 outputs service component information indicating service components, quasi-service component information indicating quasi-service components, and common data information indicating common data. For example, the analysis unit 123 may display the output content on the display 11. The analysis unit 123 may transmit the output content to a client computer connected to the network 10 and display the output content on the client computer.

  Here, the analysis unit 123 may output the service component and the quasi-service component as a unit to be migrated to the migration destination server 300. For example, the user can easily identify a group of programs and data to be migrated from the business server 200 to the migration destination server 300 by confirming the classification result of the service component and the semi-service component.

  FIG. 5 is a diagram illustrating an example of program assets. The classification unit 121 acquires a call relationship between programs based on the program assets stored in the program storage unit 111. In FIG. 5, the command “CALL” indicates calling another program. The argument of the command “CALL” is the identification name of the called program. The command “READ” indicates data reference (READ). The argument of the command “READ” is the identification name of the reference source data. Further, the command “WRITE” indicates data update (WRITE). The argument of the command “WRITE” is an identification name of the update destination data.

  For example, program A calls program B and program D. Program B calls program C. The program C refers to the data a. Program D calls program F. Program D updates data b. Program E calls program D. The program F does not call other programs and does not refer to / update data.

Note that the program assets shown in FIG. 5 are examples, and the program assets may include other programs.
FIG. 6 is a diagram illustrating an example of a program call structure. The classification unit 121 creates the following call structure for the program resource illustrated in FIG. Among the programs A to F, the programs having no calling source programs are programs A and E. Therefore, the classification unit 121 creates a program call structure starting from each of the programs A and E. FIG. 6A shows a specific example of a call structure. First, consider the case where program A is the starting point.

  When the argument of the command “CALL” is traced starting from the program A, a call structure in which the program A calls the program B and the program B calls the program C is obtained. Further, a call structure in which the program A calls the program D and the program D calls the program F can be obtained. Therefore, the classification unit 121 classifies the programs A, B, C, D, and F that can be traced from the program A into the program group A.

  Next, when the argument of the command “CALL” is traced starting from the program E, a call structure in which the program E calls the program D and the program D calls the program F is obtained. Therefore, the classification unit 121 classifies the program E and the programs D and F that can be traced from the program E into the program group E.

  Subsequently, the classification unit 121 acquires a correspondence relationship between each program group and data. FIG. 6B shows a specific example of a correspondence relationship between a program group and data. The program C refers to the data a. Program D updates data b. Programs C and D belong to program group A. Therefore, the classification unit 121 obtains a correspondence relationship in which the data a is referred to from the program group A and the data b is updated from the program group A. The program D also belongs to the program group E. Therefore, the classification unit 121 obtains a correspondence relationship in which the data b is updated from the program group E.

  The classification unit 121 creates related information indicating the correspondence between the acquired program group and data. FIG. 6C is an example of the related information 112a created for the relationship of FIG. The related information 112a is stored in the analysis result storage unit 112. Here, the reference is indicated by “#read” and the update is indicated by “#write”. For example, the record in the first line of the related information 112a indicates a relationship of referring to the data a from the program group A (“#read”). The record on the second line of the related information 112a indicates a relationship of updating the data b from the program group A (“#write”). The record on the third line of the related information 112a indicates a relationship of updating the data b from the program E (“#write”).

  FIG. 7 is a diagram illustrating an example of generating service component candidates. Here, the acquisition result of the correspondence relationship between the program group and the data by the classification unit 121 is the program group X, Y, Z, and the correspondence relationship between the program groups A and E and the data a and b illustrated in FIG. It is assumed that the correspondence relation regarding the data c and d is included. Specifically, the program group E further refers to the data c. Program group X updates data a. The program group X refers to the data c. The program group X refers to the data d. The program group Y updates the data d. The program group Z refers to the data c. The related information 112b indicates a correspondence relationship between the program groups A, E, X, Y, and Z and the data a, b, c, and d. The related information 112 b is created by the classification unit 121 and stored in the analysis result storage unit 112.

The candidate generating unit 122 generates a relationship between each program group and data using the following equation (1).
E (A, B) = 1 / (n × d_in (B)) (1)
Here, E (A, B) indicates the essentiality (weight) of the dependency relationship from the entity A to the entity B. d_in (B) is an entry degree to the entity B. n is 2 when referring to data, and 1 when updating data. The larger E (A, B), the stronger the relationship between entities A and B, and the smaller E (A, B), the weaker the relationship between entities A and B.

  According to Equation (1), it is determined that the relationship between the data and each reference source program is weaker as the number of reference source programs increases for a certain data. Further, it is determined that the relationship between the data updating program and the data is stronger than the relationship between the data referring to the data and the data. Then, the candidate generation unit 122 distributes data accessed from many programs to one of the service component candidates and outputs it.

  For example, since the program groups A and E update the data b, the relationship with the data b is stronger than other data. The program group X updates the data a, but since there are many accesses to other data, the relationship with each data is not so strong. Since the program group Y updates the data d, the relationship with the data d is relatively stronger than other data. The program group Z refers only to the data c and has only a relationship with the data c.

FIG. 8 is a diagram illustrating an example of a service component candidate generation result. The candidate generation unit 122 generates service component candidates 31, 32, and 33 as a result of clustering.
The service component candidate 31 includes program groups A and E and data a and b. The service component candidate ID (IDentifier) of the service component candidate 31 is “1”. The service component candidate 32 includes program groups X and Z and data c. The service component candidate ID of the service component candidate 32 is “2”. The service component candidate 33 includes a program group Y and data d. The service component candidate ID of the service component candidate 33 is “3”.

  The service component candidate information 112c is information indicating the service component candidates 31, 32, and 33. The service component candidate information 112c is generated by the candidate generation unit 122 and stored in the analysis result storage unit 112. The service component candidate information 112c includes a correspondence relationship between a service component candidate ID and a program group or data identifier.

  For example, the service component candidate information 112c includes a record indicating a correspondence relationship between the service component candidate ID “1” and “program group A”. This record indicates that the program group A belongs to the service component candidate 31.

  The service component candidate information 112c includes a record indicating a correspondence relationship between the service component candidate ID “1” and “data a”. This record indicates that the data a belongs to the service component candidate 31.

In the service component candidate information 112c, a record indicating another correspondence relationship between the service component candidate ID and the program group or data identifier is also registered.
The analysis unit 123 sets data updated from other service component candidates among data belonging to a certain service component candidate as common data (global data). The analysis unit 123 removes the common data from the service component candidates to which the corresponding common data belongs. For example, the data a belonging to the service component candidate 31 is updated from the program group X belonging to the service component candidates other than the service component candidate 31, and thus is common data (global data). Therefore, the analysis unit 123 removes the data a from the service component candidate 31.

  That is, the analysis unit 123 is an access destination of the program group X of the service component candidate 32 (second group) among the first data (data a, b) belonging to the service component candidate 31 (first group). When a portion (data a) overlapping with the second data (data a, c) is updated by the program group X (second program), the portion is determined as common data and excluded from the service component candidates 31. To do.

  Further, the analysis unit 123 adds another part (data b) updated by the program group A (first program) other than the common data among the first data belonging to the service component candidate 31 (first group). Output as unique data of the service component candidate 31. The unique data is data that is not updated from outside the service component candidate (or service component) to which the unique data belongs.

  Even if the data a is removed from the service component candidate 31, the program groups A and E still have the relationship of updating the data b. For this reason, after the data a is removed from the service component candidate 31, the programs A and E and the data b belong to the service component candidate 31 (the analysis unit 123 does not divide the service component candidate 31).

  Further, the analysis unit 123 sets, among data belonging to a certain service component candidate, data that is referenced but not updated from a program group in the service component candidate to which the data belongs and a program group in another service component candidate. Master data). The analysis unit 123 removes the common data from the service component candidates to which the corresponding common data belongs. For example, the data c belonging to the service component candidate 32 is common data (master data) because it is referenced from the program group E belonging to the service component candidate 31 different from the service component candidate 32 and is not updated. Therefore, the analysis unit 123 removes the data c from the service component candidate 32. When the data c is removed from the service component candidate 32, the connection between the program groups X and Z is lost. For this reason, the analysis unit 123 divides the service component candidate 32. In this case, the service component candidate 32 is divided into two program groups X and Z.

  The service component candidate 33 does not have common data corresponding to global data and master data (data d is unique data in the service component candidate 33). Therefore, the analysis unit 123 maintains the service component candidate 33 as it is. The analysis unit 123 sets the service component candidates remaining at this time as service components.

  FIG. 9 is a diagram illustrating an example of a classification result of service components and common data. The analysis unit 123 generates service component information 112d indicating the service components 41 and 42 and common data information 112e indicating the common data. The service component information 112d and the common data information 112e are stored in the analysis result storage unit 112.

  The service component 41 includes program groups A and E and data b. The service component ID of the service component 41 is “1”. The data b is updated by the program groups A and E in the service component 41 and is not updated by a program group outside the service component 41. Therefore, the data b is unique data in the service component 41. The service component 42 includes a program group Y and data d. The service component ID of the service component 42 is “2”. The data d is updated by the program group Y in the service component 42 and is not updated by the program group outside the service component 42. Therefore, the data d is unique data in the service component 42.

  The service component information 112d includes a record indicating a correspondence relationship between a service component ID and a program group or data identification name. For example, the service component information 112d includes a record indicating a correspondence relationship between the service component ID “1” and “program group A”. This record indicates that the program group A belongs to the service component 41.

  The service component information 112d includes a record indicating the correspondence between the service component ID “1” and “data b”. This record indicates that data b belongs to the service component 41.

In the service component information 112d, a record indicating another correspondence between the service component ID and the program group or data identifier is also registered.
Data a and c are common data. The common data ID of data a is “c1”. The common data ID of the data c is “c2”. The common data information 112e includes a record indicating the correspondence between the common data ID and the data identification name.

  For example, the common data information 112e includes a record indicating a correspondence relationship between the common data ID “c1” and “data a”. This record indicates that the data a is common data corresponding to the common data ID “c1”.

  The common data information 112e includes a record indicating a correspondence relationship between the common data ID “c2” and “data c”. This record indicates that the data c is common data corresponding to the common data ID “c2”.

  Here, the analysis unit 123 determines whether or not the service component candidate 32 divided into the program groups X and Z can be regrouped as a semi-service component. Specifically, the analysis unit 123 regroups the service component candidates 32 as quasi-service components when all the data inside the divided service component candidates 32 is common data. On the other hand, the analysis unit 123 does not regroup the service component candidates 32 as quasi-service components when all the data inside the divided service component candidates 32 is not common data.

  In the example of FIG. 9, the data c belonging to the service component candidate 32 is common data. In addition, since there is no other data in the service component candidate 32, all the data inside the service component candidate 32 is common data. Therefore, the analysis unit 123 regroups the program groups X and Z and the data c as quasi-service components.

  FIG. 10 is a diagram illustrating an example of the classification result of the semi-service component. The analysis unit 123 specifies the semi-service component 43 in addition to the service components 41 and 42. The semi-service component 43 includes program groups X and Z and data c. The service component ID of the semi-service component 43 is “3”.

  Thus, the analysis unit 123 includes the program groups X and Z (third program and fourth program) and common data (data c) that is referred to by the program groups X and Z but is not updated by the program groups X and Z. ) Are extracted from the program assets of the business server 200 to be analyzed.

  FIG. 11 is a diagram illustrating an example (continued) of the classification result of the semi-service component. The analysis unit 123 further aggregates a program group and data that are particularly related to each other inside the identified service components 41 and 42 and the semi-service component 43. For example, in the semi-service component 43, the program group Z accesses only the data c. In a certain service component, when a certain program group accesses only certain data, the relationship between the program group and the data is considered to be particularly strong. Therefore, the analysis unit 123 aggregates the program group Z and the data c into the sub service component 44. The service component ID of the sub service component 44 is “4”.

  The analysis unit 123 further generates service component information 112 f indicating the service components 41 and 42 and quasi-service component information 112 g indicating the quasi-service component 43 and the sub-service component 44, and stores them in the analysis result storage unit 112.

  The service component information 112f is different from the service component information 112d in that information indicating the display color of a frame surrounding the corresponding service component is included at the end of each record. For example, the service component information 112f includes a record in which the service component ID “1”, “program group A”, and “RED” are associated with each other. This record indicates that the program group A belongs to the service component 41, and when the service component 41 is displayed, a red ("RED") frame line is used.

  The service component information 112f includes a record in which the service component ID “1”, “data b”, and “RED” are associated with each other. This record indicates that the data b belongs to the service component 41 and a red frame is used when the service component 41 is displayed.

In the service component information 112f, a record indicating another correspondence relationship between a service component candidate ID and a program group or data identifier is also registered.
The semi-service component information 112g includes a record in which a service component ID, a program group or data identification name, and a frame line color used when displaying the semi-service component are associated with each other. The quasi-service component information 112g includes a record in which the service component ID “3”, “program group X”, and “GREEN” are associated with each other. This record indicates that the program group X belongs to the semi-service component 43, and when the semi-service component 43 is displayed, a green ("GREEN") frame line is used.

  Here, the record on the second line of the quasi-service component information 112g is a special record representing the nesting relationship of service components. The record indicates a correspondence relationship between the service component ID, the service component ID of the sub service component nested within the service component indicated by the service component ID, and the sub service component ID of the sub service component. .

  For example, the record on the second line of the semi-service component information 112g indicates that the sub-service component 44 corresponding to the service component ID “4” exists inside the semi-service component 43 corresponding to the service component ID “3”. . In addition, the sub service component ID of the sub service component 44 is “SUB1”.

  Further, the quasi-service component information 112g includes a record relating to a program group and data belonging to the sub service component 44. For example, the record in the third line of the semi-service component information 112g is information in which the service component ID “4”, “program group Z”, and “SUB” are associated with each other. This record indicates that the program group Z belongs to the sub service component 44. The setting of “SUB” indicates that the sub service component 44 is defined in a nested manner in any service component. According to the semi-service component information 112g, the analysis unit 123 can grasp that the sub service component 44 is defined in the semi-service component 43 in a nested manner based on the record in the second row.

Similarly, a record indicating that the data c belongs to the sub service component 44 is registered in the semi-service component information 112g.
The analysis unit 123 can display the classification results of the service components 41 and 42, the semi-service component 43, and the sub-service component 44 on the display 11 or the like based on the service component information 112f and the semi-service component information 112g.

Next, the processing procedure of the analysis server 100 will be described.
FIG. 12 is a flowchart illustrating a processing example of the analysis server.
(S10) When the classification unit 121 receives designation of software to be analyzed by the user, the classification unit 121 acquires a plurality of programs constituting the software from the business server 200 and stores them in the program storage unit 111. The classification unit 121 analyzes the plurality of acquired programs and classifies each program into a program group based on the calling relationship between the programs. Details of the program analysis will be described later.

  (S11) The classification unit 121 generates related information between the program group and the data based on the access destination of the data of the program belonging to the program group, and stores it in the analysis result storage unit 112.

  (S12) The candidate generating unit 122 performs clustering of the program group and data by executing the division process based on the related information, and extracts service component candidates. For the clustering, for example, the method disclosed in JP 2013-148987 A can be used. The candidate generation unit 122 generates service component candidate information as a result of clustering, and stores it in the analysis result storage unit 112.

  (S13) The analysis unit 123 extracts common data based on the service component candidate information. The analysis unit 123 generates common data information indicating the common data and stores it in the analysis result storage unit 112. Details of the common data extraction process will be described later.

  (S14) The analysis unit 123 classifies service components based on the common data extraction result. The analysis unit 123 generates service component information and quasi-service component information indicating the service component classification result, and stores them in the analysis result storage unit 112. Further, the analysis unit 123 generates and outputs screen information indicating the service component and the semi-service component based on the service component information and the semi-service component information. For example, the analysis unit 123 outputs the generated screen information to the display 11 and causes the display 11 to display a screen showing service components and semi-service components. Details of the service component classification process will be described later. And the process of the analysis server 100 is complete | finished.

  In the process of extracting service component candidates in step S13, a plurality of groups of program groups and data are extracted based on the relationship between a plurality of program groups (or programs) and data of the analysis target system (business server 200). It can be said that it is processing to do. For example, as illustrated in FIG. 8, the analysis server 100 includes a first program group (program group A) and a first data including first data (data a and b) accessed by the first program group. A group (service component candidate 31) is extracted from the system to be analyzed. The analysis server 100 also includes third data that does not overlap with the first data among the second program group (program group X) and the second data (data a, c) accessed by the second program group. A second group (service component candidate 32) including (data c) is extracted from the system to be analyzed.

FIG. 13 is a flowchart illustrating a processing example of program analysis. This program analysis is executed in step S10 described above.
(S20) The classification unit 121 refers to each analysis target program and obtains a calling relationship between the programs. For example, the classification unit 121 acquires a call relationship in which a certain program calls another program by tracing a command (for example, “CALL”) that calls another program included in the program.

(S21) The classification unit 121 sets a program that is not called by any program as a starting point.
(S22) The classification unit 121 selects one starting program, acquires a calling structure from the starting program, and identifies a program group based on the calling structure.

  (S23) The classification unit 121 determines whether a call structure has been acquired for all starting programs. If the call structure is acquired for all starting programs, the process proceeds to step S24. If the call structure has not been acquired for all starting programs, the process proceeds to step S22.

  (S24) The classification unit 121 acquires access destination data of a program. For example, the classification unit 121 acquires data that is an access destination of a program by detecting a command (for example, “READ” or “WRITE”) that refers to or updates data included in the program.

  (S25) The classification unit 121 selects one program group, and determines whether there is a program that accesses the corresponding data in the corresponding program group. If there is, the process proceeds to step S26. If not, the process proceeds to step S27.

  (S26) The classification unit 121 records a record (including information indicating whether the access content is READ or WRITE) indicating the relationship between the corresponding program group and the access destination data in the related information.

  (S27) The classification unit 121 determines whether or not the relationship with data has been confirmed for all program groups. When the relationship with the corresponding data is confirmed for all the program groups, the process proceeds to step S28. If the relationship with the corresponding data has not been confirmed for all program groups, the process proceeds to step S25.

  (S28) The classification unit 121 determines whether or not the relationship with the program has been confirmed for all target data. When the relationship with the program is confirmed for all target data, the process proceeds to step S29. If the relationship with the program has not been confirmed for all target data, the process proceeds to step S24.

  (S29) The classification unit 121 outputs related information between a program group and data. For example, the classification unit 121 may cause the display 11 to display related information between the program group and the data (or an image representing the relationship between the program groups based on the related information illustrated in FIG. 7). Then, program analysis ends.

FIG. 14 is a flowchart illustrating a processing example of common data extraction. This common data extraction is executed in step S13 described above.
(S30) The analysis unit 123 refers to the service component candidate information stored in the analysis result storage unit 112, and acquires one service component candidate.

(S31) The analysis unit 123 refers to the related information stored in the analysis result storage unit 112, and specifies update processing (WRITE) for data in the corresponding service component candidate.
(S32) The analysis unit 123 determines whether or not the identified update process is to update the data in the service component candidate from the outside (outside of the service component candidate). In the case of updating from the outside, the process proceeds to step S33. If not updated externally (that is, if the program group inside the service component candidate is to be updated), the process proceeds to step S34.

  (S33) The analysis unit 123 defines the data updated from the outside of the corresponding service component candidate as the common data outside the service component candidate. The analysis unit 123 adds the common data record acquired this time to the common data information stored in the analysis result storage unit 112. The common data in this case is global data. Then, the process proceeds to step S37.

  (S34) The analysis unit 123 refers to the related information stored in the analysis result storage unit 112, and specifies the reference process (READ) for the data in the service component candidate acquired in step S30.

  (S35) The analysis unit 123 determines whether there is data that is only referred to from both inside and outside of the service component candidate among the data in the corresponding service component candidate. If there is data that is only referred to from both inside and outside of the service component candidate, the process proceeds to step S36. If there is no data that can only be referred to from both inside and outside of the service component candidate, the process proceeds to step S37.

  (S36) The analysis unit 123 defines data that is only referred to from both inside and outside the corresponding service component candidate as common data outside the service component candidate. The analysis unit 123 adds the common data record acquired this time to the common data information stored in the analysis result storage unit 112. The common data in this case is master data.

  (S37) The analysis unit 123 determines whether all data relationships (reference and update relationships) in the service component candidate selected in step S30 have been acquired. If all data relationships have been acquired, the process proceeds to step S38. If all data relationships have not been acquired, the process proceeds to step S31.

  (S38) The analysis unit 123 refers to the service component candidate information stored in the analysis result storage unit 112, and determines whether there is an unrelated service component candidate. If there is an irrelevant service component candidate, the process proceeds to step S39. If there is no irrelevant service component candidate, the process proceeds to step S40. Here, the “relationship” in step S38 is “relationship between a plurality of program groups”. “There is a relationship among a plurality of program groups” means that “a plurality of program groups access the same data in the corresponding service component candidate”. “There is no relationship between a plurality of program groups” means “a plurality of program groups do not access the same data in the corresponding service component candidate”.

  (S39) The analysis unit 123 divides the parts within a related range based on the related information and the service part candidate information stored in the analysis result storage unit 112. That is, the analysis unit 123 divides a plurality of program groups belonging to the corresponding service component candidate into units having a relationship of accessing the same data in the service component candidate, and creates a plurality of new service component candidates. . Here, a program group having no relationship of accessing the same data in the corresponding service component candidate is excluded from the service component candidates (groups).

  (S40) The analysis unit 123 determines whether common data extraction processing has been performed for all service component candidates. If the common data extraction process has been performed for all service component candidates, the process proceeds to step S41. If the common data extraction process has not been performed for all service component candidates, the process proceeds to step S30.

  (S41) The analysis unit 123 generates service component information based on extraction of common data from each service component candidate and division result of the service component candidates. For example, the analysis unit 123 adds “RED” information to the end of the service component record in the service component information in order to display the service component determined up to this stage using a red frame. The analysis unit 123 outputs the generated service component information and the common data information acquired in steps S35 and S36. For example, the analysis unit 123 may display service component information and common data information (or an image representing the relationship between the service component illustrated in FIG. 9 or the divided program group and the common data) on the display 11. Then, the common data extraction ends.

  If the update process cannot be specified in step S31, the analysis unit 123 skips step S32 and executes step S34. When the reference process cannot be specified in step S34, the analysis unit 123 skips steps S35 and S36 and executes step S37.

FIG. 15 is a flowchart illustrating a processing example of service component classification. This service component classification is executed in step S14.
(S50) The analysis unit 123 refers to the service component candidate information stored in the analysis result storage unit 112, and acquires a service component candidate.

  (S51) The analysis unit 123 refers to the common data information stored in the analysis result storage unit 112, and determines whether all data of the service component candidates is common data. When all the data of the service component candidates are common data, the process proceeds to step S52. If all the data of the service component candidates are not common data, the process proceeds to step S53.

  (S52) The analysis unit 123 extracts the corresponding service component candidate as a quasi-service component. The analysis unit 123 adds the record of the semi-service component corresponding to the corresponding service component candidate to the semi-service component information stored in the analysis result storage unit 112. The analysis unit 123 adds “GREEN” information to the end of the record of the semi-service component in the semi-service component information in order to display the semi-service component using a green frame.

  (S53) The analysis unit 123 determines whether all service component candidates registered in the service component candidate information have been acquired. If all service component candidates have been acquired, the process proceeds to step S54. If all the service component candidates have not been acquired, the process proceeds to step S50.

(S54) The analysis unit 123 refers to the related information stored in the analysis result storage unit 112, and extracts one piece of data.
(S55) The analysis unit 123 determines whether there is a program group that accesses only the corresponding data based on the related information. If there is a program group that accesses only the corresponding data, the process proceeds to step S56. If there is no program group that accesses only the corresponding data, the process proceeds to step S58. Hereinafter, these program groups and data are referred to as “related program groups and data”.

  (S56) The analysis unit 123 determines whether or not the related program group and data are located across the extracted region. If the extracted region does not straddle, the process proceeds to step S57. When straddling the extracted area, the process proceeds to step S58. Here, the “extracted area” corresponds to a program group and data belonging to a service component or a semi-service component (group). For example, “does not straddle the extracted region” indicates that the related program group and data belong to the same service component or quasi-service component. Further, “crossing the extracted area” indicates that the related program group and data belong to different service components or semi-service components.

  (S57) The analysis unit 123 creates a unit (sub service component) in which the corresponding program group and data are aggregated. For example, the analysis unit 123 adds the sub service component record under the service component or the semi-service component to which the sub service component belongs, of the service component information or the semi-service component information. As described above, the analysis unit 123 adds the information “SUB” to the end of the record corresponding to the program group or data of the semi-service component. The analysis unit 123 also assigns a service component ID and a sub service component ID to the sub service component. Then, the analysis unit 123 also registers a record indicating the relationship between the sub service component and the upper service component or the semi-service component in the service component information or the semi-service component information.

  (S58) The analysis unit 123 determines whether or not all the data included in the related information has been examined for relationships (whether or not the processing in steps S54 to S57 has been performed). If the relationship is examined for all data, the process proceeds to step S59. If the relationship has not been investigated for all data, the process proceeds to step S54.

  (S59) The analysis unit 123 outputs a service component based on the service component information. That is, the analysis unit 123 outputs information indicating a program (or program group) corresponding to a service component and information indicating data. For example, the analysis unit 123 causes the program group and data to be icons of a predetermined size (for example, a figure such as a rectangle), and further causes the display 11 to display an image in which the icon group corresponding to the service component is surrounded by a red frame.

  (S60) The analysis unit 123 outputs the semi-service component based on the semi-service component information. That is, the analysis unit 123 outputs information indicating a program (or program group) corresponding to the semi-service component and information indicating data. For example, the analysis unit 123 causes the display 11 to display an image in which the icon group corresponding to the semi-service component among the icons corresponding to the program group and data displayed in step S59 is surrounded by a green frame. Then, the service component classification ends.

  FIG. 16 is a diagram illustrating a display example (part 1) of the classification result. The screen 50 is displayed on the display 11 based on the screen information output by the analysis unit 123, for example. The screen 50 displays the program and data executed by the business server 200 as a map. One icon indicated by a rectangle indicates one program group or data. Note that the analysis unit 123 may display icons not in units of programs but in units of programs. For example, the analysis unit 123 may display information indicating a program group or a program corresponding to the corresponding icon on the display 11 according to an icon selection operation by the user.

  The screen 50 includes a plurality of areas surrounded by a frame. Frames R1, R2, R3, R4, and R5 are frames indicating service components. For example, the lines of the frames R1, R2, R3, R4, and R5 are displayed in red as described above (however, other colors may be used). The program groups inside the frames R1, R2, R3, R4, and R5 are associated by specific data.

  Frames G1, G2, and G3 are frames indicating quasi-service components. For example, the lines of the frames G1, G2, and G3 are displayed in green as described above (however, other colors may be used as long as they are different from the frames R1, R2, R3, R4, and R5). The data inside the frames G1, G2, G3 is common data.

  As described above, the analysis unit 123 determines whether the type of data associated with the service component or the program belonging to the quasi-service component is specific data or common data. Change the display method of service parts. In addition to changing the color of the frame line as described above, various methods such as changing the line type (for example, whether it is a solid line or a broken line) and the color of the area in the frame to be distinguishable can be considered.

  Each of the frames R1, R2, R3, R4, R5 and the frames G1, G2, G3 includes, for example, a character string extracted from a comment sentence included in the program to which the user belongs (for example, “login”, “list, A character string such as “to do” ”is also displayed. For example, the displayed character string has the highest number of appearances in the program to which it belongs. The user can grasp what functions the program group and data in the corresponding frame correspond to from these character strings.

In particular, since the programs in the frames G1, G2, and G3 correspond to one function, it is possible to collect programs that should actually be handled collectively.
As described above, the analysis server 100 separates the unique data that is not included in the common data from the data accessed by the analysis target system program, and the common data, and easily separates the separated areas from the work. To the user.

  The easiness of extracting service components and semi-service components from the business server 200 is as follows. First, when there is no external reference / update to the data in the frame, the function realized by the program and data in the frame is most easily cut out. Second, when there is no external update to the data in the frame, the function realized by the program and data in the frame is the second most easily cut out. Third, when both reference and update to the data in the frame are from the outside, it is difficult to cut out the function realized by the program and data in the frame.

  As described above, the analysis unit 123 can also display a portion that is easy to cut out on the display 11 and present it to the user in accordance with the presence or absence of external reference or update to the data inside the service component or the semi-service component. For example, the analysis unit 123 causes the display 11 to display information indicating that the functions corresponding to the frames R1, R2, R3, R4, and R5 are easier to cut out than the functions corresponding to the frames G1, G2, and G3. May be presented. This is because the semi-service component may include data (global data) updated from the outside.

  FIG. 17 is a diagram illustrating a display example (part 2) of the classification result. For example, the analysis unit 123 may display only the service component on the display 11 and may not display the quasi-service component on the display 11. In this case, the screen 60 is displayed on the display 11. The screen 60 includes frames R11, R12, R13, R14, and R15. Frames R11, R12, R13, R14, and R15 indicate service components corresponding to the frames R1, R2, R3, R4, and R5, respectively. However, other program groups and data are not grouped and are displayed as independent points (icons not surrounded by a frame). For this reason, in the display of the screen 60, the user cannot appropriately grasp the semi-service component. Therefore, it is preferable that the analysis unit 123 displays the screen 50 of FIG. 16 on the display 11 rather than the screen 60 so that the semi-service component can be appropriately identified.

Next, a comparative example of program and data classification methods will be described.
FIG. 18 is a diagram illustrating a comparative example of classification. Here, in FIG. 18, groups of programs and data are represented by lanes separated by vertical dotted lines. “Function” corresponds to a program group. Further, for example, a function of a screen that provides a GUI (Graphical User Interface) and a function of executing a business process are associated by a Web API (Application Programming Interface).

  For example, although the entire system is divided into a plurality of units as in the classification method of FIG. 18A, a method of including data in any of the division units without distinguishing “common data” is conceivable. In this classification method, data is included in any division unit, and “common data” cannot be clearly distinguished. Since it is not possible to distinguish whether the data included in the division unit is unique data or common data, it is not possible to determine whether it is easy to cut out.

  Also, for example, as in the classification method of FIG. 18B, the “common data” is clarified and the entire system is divided into a plurality of units, but all functions that only access “common data” are presented separately. It is also possible. However, if they are separated into too small units, it becomes difficult to handle individual program groups. In this classification method, although “common data” can be clarified, when there is a common item of accessing the same data, it is not possible to respond to the desire to handle them collectively.

  FIG. 19 is a diagram illustrating an example of classification by the analysis server. The analysis server 100 does not extract a program that accesses only “common data” as one that is handled individually, but creates a unit that is aggregated as one that accesses the same data. The example of FIG. 19 shows a case where a group G10 is created that includes four functions that aggregate a plurality of functions that access only “common data”. Group G10 corresponds to a semi-service component.

  The groups G21, G22, and G23 are program groups and data units separated through “unique data”. Groups G21, G22, and G23 correspond to service components.

  As described above, according to the analysis server 100, it is possible to extract a service component that holds “unique data”. In addition, a plurality of programs that access the same “common data” can be extracted as a unit. A plurality of programs that access the same “common data” are likely to have a common property, and a unit that can handle the plurality of programs collectively can be appropriately extracted. In particular, when separating a large-scale system, groups can be presented so as not to be too fine for the user to handle.

  Further, the analysis server 100 can distinguish and present a unit (service component) separated based on unique data and a unit (quasi-service component) separated based on common data for the group. Further, with respect to the common data, the common data D10 used for separating the semi-service components (common data used for the unit to be separated) and the common data D20 independent from the separated unit can be further classified.

Next, an example in which the functions of the analysis server 100 are used to separate some functions of the business server 200 and migrate to the migration destination server 300 will be described.
FIG. 20 is a diagram illustrating an example of migration of some functions of an existing system. For example, in the business server 200, a sales support system is operating as an existing business system. Many programs and data are used in the sales support system. Here, in FIG. 20, a program is represented by a combination of a character string “PG” and a numerical value.

  For example, the sales support system includes programs PG1, PG2, PG3, PG4, PG5, PG6, and PG7 (ST1). Programs PG1, PG2, PG3, PG4, PG5, PG6, and PG7 access various data. Existing sales support systems often have an integrated structure, and it is not known where they will be affected if a system change is attempted. Therefore, it is difficult to modify and maintain the program.

  Therefore, the analysis server 100 analyzes the program assets of the business server 200 and classifies the programs PG1, PG2, PG3, PG4, PG5, PG6, PG7 and each data into functional units of service components and semi-service components.

  As a result, for example, the analysis server 100 classifies the sales support system into three functions: a sales office management function, a business partner management function, and an accounting processing function (ST2). The sales office management function includes programs PG2 and PG7 and their unique data. The supplier management function includes programs PG1, PG5, PG6 and their unique data. The accounting processing function includes programs PG3 and PG4 and their specific data. Moreover, the analysis server 100 extracts budget / actual data from the program assets of the business server 200 as common data accessed from the sales office management function, the business partner management function, and the accounting processing function.

In this way, the analysis server 100 can classify a plurality of programs included in the program asset into functional units and clarify programs and data related to each function.
By classification according to function, it becomes easy to modify and maintain a program corresponding to each function. For example, when it is desired to change the supplier management function, the programs PG1, PG5, PG6 related to the supplier management function and their unique data are in the affected range. For this reason, the user can determine that the programs PG1, PG5, PG6 and their unique data should be mainly changed by referring to the classification result displayed by the analysis server 100. Further, by narrowing down the influence range of the change, the user's work accompanying the change of the program can be made efficient, and the program can be changed quickly.

  Further, the user can further speed up the system change from the classification result by the analysis server 100. For example, the analysis server 100 transfers a program associated with the unique data and a program associated with the common data to a system to be migrated to a system (migration destination server 300) different from the system to be analyzed (current business server 200). Can also be a candidate. That is, when the frequency of changes in the processing of the supplier management function is high, the supplier management function can be separated from the business server 200 and transferred to the transfer destination server 300 (ST3).

  In this case, the user modifies the program related to the business partner management function, and migrates the programs PG1, PG5, PG6 corresponding to the business partner management function and their unique data from the business server 200 to the migration destination server 300. . The business server 200 stops execution of the supplier management function, and the migration destination server 300 executes the supplier management function. Here, access from the business partner management function to the budget / actual data for the business server 200 is defined by a predetermined interface (for example, WebAPI).

  Thus, by cutting out the supplier management function from the business server 200 to the migration destination server 300, it is possible to further reduce the influence on other functions when the supplier management function is changed. For this reason, it becomes possible to change the system quickly according to the change in the business content, and it is possible to respond quickly to the change in the business content.

  By the way, in recent years, it is preferable that a business system such as a sales support system can be quickly changed in accordance with an environment and external changes (for example, a supplier change, a law revision, a base addition, etc.). In this regard, a software structure called “micro service” is considered that makes it possible to localize the impact of change and speed up the release cycle of individual functions included in the business system. The micro service is a technique for constructing each function belonging to the system as an independent collection of lightweight services, rather than developing the entire system as a single application. In order to provide an independent service, data is managed for each service. In microservices, data used only by individual services is held inside the service, data shared between services is clarified, and an access method to data shared from each service is determined.

  According to the analysis server 100, there is an advantage that programs and data used for each function can be grouped in an appropriate unit that is easy to handle for an existing business system, and a micro service can be realized. User work associated with the microservices of existing business systems can also be saved.

  The information processing according to the first embodiment can be realized by causing the processing unit 1b to execute a program. The information processing according to the second embodiment can be realized by causing the CPU 101 to execute a program. The program can be recorded on a computer-readable recording medium 13.

  For example, the program can be distributed by distributing the recording medium 13 on which the program is recorded. Alternatively, the program may be stored in another computer and distributed via a network. For example, the computer stores (installs) a program recorded in the recording medium 13 or a program received from another computer in a storage device such as the RAM 102 or the HDD 103, and reads and executes the program from the storage device. Good.

DESCRIPTION OF SYMBOLS 1 Separator 1a Memory | storage part 1b Processing part 2 System 3 Display apparatus 4 Output screen 4a, 4b, 4c Screen components D1 Data set D11 1st data set D12 2nd data set

Claims (10)

A separation method for separating corresponding data for each business from a target system,
Identifying first data accessed by a first program of the target system and second data accessed by a second program of the target system;
Outputting data accessed by both the first program and the second program as common data that is referred to or updated by a plurality of tasks;
Separation method.   In the specifying, based on a relationship between a plurality of programs and data of the target system, the first group including the first program and the first data, the second program, and the second program The separation method according to claim 1, wherein a second group including third data that does not overlap with the first data is extracted from the target system.   The portion of the first data that overlaps with the second data is updated by the second program, and the portion is determined as the common data and excluded from the first group. The separation method described.   4. The separation method according to claim 3, wherein the other portion of the first data that is updated by the first program other than the common data is output as the unique data of the first group.   A third group including a third program, a fourth program, and other common data that is referenced by the third and fourth programs but not updated by the third and fourth programs, The separation method according to claim 4, which is extracted from the system.   The separation method according to claim 5, wherein a display method of the group by a display device is changed according to whether the type of data associated with a program belonging to a group is the specific data or the common data.   Of the first and second data accessed by the program of the target system, the unique data not included in the common data and the common data are separated from each other, and the separated area can be easily cut out. The separation method according to claim 1, wherein the separation method is presented to a user as a range.   The separation method according to claim 7, wherein the program associated with the unique data and the program associated with the common data are candidates for migration to a system different from the target system. A separation device that separates the corresponding data for each business from the target system,
A storage unit for storing program assets in the target system;
With reference to the storage unit, the first data accessed by the first program of the target system and the second data accessed by the second program of the target system are identified, and A processing unit that outputs data accessed by both the first program and the second program as common data that is referred to or updated by a plurality of tasks;
Separation device having. A separation program that separates the corresponding data for each business from the target system,
Identifying first data accessed by a first program of the target system and second data accessed by a second program of the target system;
Outputting data accessed by both the first program and the second program as common data that is referred to or updated by a plurality of tasks;
A separation program that causes a computer to execute processing.

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