CN106845781B - Scene and flow generation system and method for business test - Google Patents
Scene and flow generation system and method for business test Download PDFInfo
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Abstract
The invention provides a system and a method capable of generating scenes and flows for various business tests systematically and reliably, so that business test analysis can be performed reliably and comprehensively. The flow generating system for the business test comprises: a factor library for storing factors as minimum service elements for forming one service; a factor extraction unit that extracts, from a factor library, factors that match keywords extracted from the business description document; a scene generation unit for combining the factors extracted by the factor extraction unit into a scene according to a predetermined factor relation table for specifying whether or not combinable relations exist between the factors; and a scene ranking unit that ranks the scenes generated by the scene generating unit according to a predetermined scene relation table for specifying ranking relations among the scenes in the service, thereby generating a flow.
Description
Technical Field
The present invention relates to a business test analysis system, and more particularly, to a system and method capable of automatically generating scenes and flows for business test.
Background
In order to meet the requirements of business development and risk management, many enterprises develop or introduce business management systems and the like, and the complexity of such business management systems is different due to the business types, business operations and the like, for example, a banking core business system is used as an important component of bank deposit, loan accounting and the like, is a heart of the operation of the banking system, and all business operations related to deposit and loan accounts are completed in the core business system. The main business includes, for example, customer information management, deposit business, loan business, general ledgers, daytime operations on these deposits, loan accounts, and the like.
In addition, with the continuous development and innovation of services, new development of software systems and the like meeting the needs of new services is often required. When a software system is newly developed to meet such business needs, business analysis and business testing are required to be performed on the developed software system before the software system is formally online to verify whether the software system can complete various required functions and adapt to various scenes which may occur when an actual business operation.
The traditional test analysis method is specifically realized as follows: manually analyzing service demands, extracting, sorting and analyzing the demands one by one according to functional points to form a scene demand document, supplementing implicit demands such as general rules of industry and the like, sorting the scenes according to real service and function realization by all the carded item demands, and writing and subsequently executing the flow according to sorting links.
The analysis results of this method are largely dependent on personal work experience and business knowledge capabilities, and the analysis efficiency is low. For testers unfamiliar with the project service field, the quality of service demand excerpts cannot be guaranteed, the implicit demand rule supplement cannot be comprehensive, the test analysis coverage rate cannot reach the standard, and the project quality cannot be guaranteed.
In addition, with the continuous abundance of service types, projects are increased, new functional modules need to be added to an original service system when a new service is created, and when the new added functional modules relate to the original functional modules or data extraction, stability test needs to be performed on the process for manufacturing various tests for the new functions. Because of the mutual intersection of the new functions and the original functions, and the like, the testers are required to comprehensively master the intersection of the new functions and the old functions, and the like when the process is established, and high requirements are put on the business mastering capability of the testers. Therefore, the conventional method cannot meet the test analysis requirements of a large number of projects and combined large-scale projects, and in order to improve the analysis efficiency and reduce the influence of personal factors on the analysis result, there is an urgent need for a generating system and method capable of systematically and reliably writing various scenes and processes for service test analysis so as to reliably and comprehensively perform the scene and process of service test analysis.
Disclosure of Invention
In order to meet the above requirements, the present inventors devised a scenario and flow generation system and method capable of systematically and reliably writing various scenarios and flows for business testing, so that business testing analysis can be reliably and comprehensively performed.
One aspect of the present invention is a scenario generation system for business testing, comprising: a factor library for storing factors as minimum service elements for forming one service; a factor extraction unit that extracts, from a factor library, a factor that matches a keyword extracted from a description document of the present service; and a scene generation unit that combines the factors extracted by the factor extraction unit into a scene according to a predetermined factor relation table for specifying whether or not there is a combinable relation between the factors.
In the scene generation system, the factor may carry identification information and type information indicating the type of the factor; the types at least comprise seven types of products, roles, channels, time, events, conditions and results; the type information of the factor is used to indicate that the factor is at least one of the seven types.
In the actions of the factor extraction unit and the scene generation unit, the factors of the product, role, channel, time, event and condition types in the factors can be divided into a service core layer, a service execution layer, a service generation layer and a service role layer for processing, wherein the service core layer comprises the product type factors, the service execution layer comprises the event and condition type factors, the service generation layer comprises the time and channel type factors, and the service role layer comprises the role type factors.
The factor extraction unit may extract factors layer by layer in the order of the service core layer, the service execution layer, the service generation layer, and the service role layer.
The factor extracting unit may first extract, from the factor library, factors belonging to types of the service core layer that match keywords extracted from the description document of the service, then extract, layer by layer, factors having combinable relations with the extracted factors belonging to types of the previous layer, referring to the factor relation table, and extract, referring to the factor relation table, factors of result types having combinable relations with the extracted event type factors.
The factor extracting unit may extract, layer by layer, factors belonging to the service execution layer, the service generation layer, and the service role layer, respectively referring to the factor relation table, and remove, as the redundancy item, factors that do not have combinable relation with factors belonging to the type of the layer and the preceding layers.
The scene generating unit may refer to the factor relation table, combine the factors of the layers according to the order of the service role layer, the service generating layer, the service executing layer and the service role layer, and match the corresponding result type factors according to the condition type factors used in the combination to generate the scene.
The factor extraction unit may include: a module for extracting product type factors matched with the keywords extracted from the service description document from the factor library; a module for extracting event type factors having combinable relation with the extracted product type factors by referring to the factor relation table; a module for extracting factors of a condition type, a time type, a channel type and a result type which have combinable relations with the extracted event type factors respectively by referring to the factor relation table; referring to the factor relation table, eliminating the factors which have combinable relation among the three types of factors at different time from the factors of the extracted condition type, time type and channel type; and a module for extracting character type factors having combinable relations with the factors of the condition type, the time type and the channel type at the same time by referring to the factor relation table.
The scene generating unit may combine the extracted factors in order of role type, time type, channel type, event type, condition type, and product type, and refer to the factor relation table, and match the condition type factors used in the combination with the corresponding result type factors to generate the scene.
The scene generating system of the present invention may further include: and the factor maintenance unit is used for generating a new factor corresponding to the keyword and storing the new factor into the factor library when the factor library does not have the factor matched with the keyword extracted from the service description document, and updating a factor relation table for combinable relation between each factor and the new factor according to the description document of the service. The scene generating unit may combine the factor extracted by the factor extracting unit and the new factor generated by the factor maintaining unit into a scene according to the updated factor relation table.
The scene generating system of the present invention may further include: and the scene library is used for storing the scenes generated by the scene generating unit.
Another aspect of the present invention is a flow generation system for service testing, including: a scene generation system as described above; and a scene ordering unit for ordering the scenes generated by the scene generating unit according to a predetermined scene relation table for defining ordering relation among the scenes in the service to form an executable flow.
The flow generation system may further include: and the flow library is used for storing the flow generated by the scene ordering unit.
Still another aspect of the present invention is a scenario generation method for service testing, including: a factor extraction step of extracting, from a factor library, a factor that matches a keyword extracted from a description document of the present service; and a scene generating step, wherein the factors extracted by the factor extracting step are combined into a scene according to a preset factor relation table. Wherein the factor is a minimum service element for forming one service; the factor relation table is used for specifying whether or not there is a combinable relation between the factors.
Still another aspect of the present invention is a flow generation method for service testing, including: a scene generation method as described above; and a scene ordering step of ordering the scenes generated in the scene generating step according to a predetermined scene relation table for defining ordering relations among the scenes in the service to form an executable flow.
By the technical scheme, the generating system and the generating method which can systematically and reliably write out scenes and flows for various business tests can be provided, so that business test analysis can be reliably and comprehensively carried out.
Drawings
Fig. 1 is a schematic diagram for explaining the concept of breaking down a service into scenes and factors.
Fig. 2 is a diagram for explaining the factor type of the present invention.
Fig. 3 is a schematic diagram for explaining the process of factor extraction and scene generation of the present invention.
Fig. 4 is a diagram illustrating an example of factor extraction and its combination into a scenario for writing a business test flow using a bank-related business as an example.
Fig. 5 is a diagram for explaining a procedure of generating a business test flow including a new factor.
Fig. 6 is a functional block diagram for explaining a system for implementing the business test flow generation method of the present invention.
Detailed Description
First, an outline of the scenario and flow generation method for business test of the present invention will be described.
The invention creates a factor scene method. Fig. 1 is a schematic diagram for explaining the concept of breaking down a service into scenes and factors. As shown in fig. 1, first, a service to be tested may be disassembled into a plurality of nodes according to a service operation sequence, where the nodes are staged results in the whole service, and the nodes are connected in series or in parallel to form the whole service through a predetermined scene relation table containing information such as node names, node sequence numbers, service products, flow names, and the like.
Each node may include a plurality of scenes depending on, for example, execution subject, execution time, execution condition, etc., and the scenes may be normal scenes (i.e., scenes that can be successfully executed) or abnormal scenes (i.e., scenes that fail to be executed due to non-compliance with the service handling condition, etc.). Information such as node names where the respective scenes are located, connection relations between the scenes, and business factors related to the scenes is specified in advance in the scene relation table.
Each scenario is composed of, for example, execution bodies, execution channels, execution times, execution conditions, execution events, business products, execution results, and the like, business factors, where the factors can be multiplexed. The service factors are minimum service elements such as service entities, service actions, service rules and the like with practical significance, the factors are endowed with information such as factor identification, attribute, state and the like according to preset rules, and a database is built for classified storage and management.
In addition, the factor database can be edited and maintained by increasing, decreasing, modifying and the like according to the measured business requirements. When the flow for service test is required to be written, the service factors related to the service to be tested are extracted from the factor library according to a preset rule, the arrangement and combination of the related factors are automatically carried out according to a preset factor combination relation, a scene meeting the service test requirement is generated, and finally the scenes of all nodes are connected according to the preset rule to form the flow. When the required factors do not exist in the factor library, the required factors can be supplemented into the factor library, relevant identification, attribute, state and other information are given to the factors, and meanwhile, combinable relations between the new factors and the existing factors are updated.
In summary, the scenario generation system for business testing of the present invention comprises: a factor library for storing factors as minimum service elements for forming one service; a factor extraction unit that extracts, from a factor library, a factor that matches a keyword extracted from a description document of the present service; and a scene generation unit that combines the factors extracted by the factor extraction unit into a scene according to a predetermined factor relation table for specifying whether or not there is a combinable relation between the factors. Wherein the factor is the smallest service element used to form a service. In addition, the scene generating system is combined with the scene ordering unit to form the flow generating system for business test. The scene ordering unit is used for ordering the scenes generated by the scene generating unit according to a predetermined scene relation table for defining ordering relations among the scenes in the service to form an executable flow.
In addition, when there is no factor matched with the keyword extracted from the service description document in the factor library, a new factor corresponding to the keyword is generated and stored in the factor library, and the factor relation table is updated for combinable relation between each factor and the new factor according to the description document of the service. The scene generating unit may combine the factor extracted by the factor extracting unit and the new factor generated by the factor maintaining unit into a scene according to the updated factor relation table.
The following is a detailed description.
Business factor and its classification
The business factor is the smallest business element of a business entity, business action, business rule, etc. that has practical significance. Each business factor has a corresponding data dictionary. The data dictionary may be implemented by several fields, each storing information such as the identity, attribute, and state of the factor. The identification information of the factor may be, for example, the number, name, definition, service to which the factor belongs, or the like; the attribute information of the factor may be, for example, a factor type as described below; the status information of the factor may be, for example, a date of generation, a date of maintenance, a last associated item, validity of the factor (whether or not it is usable), or the like. Each business factor is locatable, updatable, reusable by information in its data dictionary fields.
A database (hereinafter referred to as a factor library) is built for the service factors, and classified storage and management are performed according to the types thereof. To improve efficiency in writing the test flow and ensure comprehensiveness of the elements of the test flow, the service factors are preferably classified as shown in the service factor type explanatory diagram of fig. 2, and specifically, the factor types may include:
"product" is business core content. From a business perspective, it may be a specific business product of a particular type involving a business strip. For example: regular deposit service, demand deposit service, etc.
The "role" is the execution subject of the action occurrence and can also be understood as the operator or user of the system. For example: sponsors, review persons, etc.
"Channel" is a channel or system in which actions may occur. For example: online banking, cell phone banking, weChat banking, etc.
"Time" is the point in time or period of time that an action may occur. For example: non-business hours, holidays to the public, business hours, etc.
"Event" is an action that may actually occur. For example: withdrawal, deposit, etc.
"Condition" is a factor that allows/constrains/affects the progress of a business, operation. For example, account conditions, logical element conditions, physical element conditions, institution conditions, compliance conditions, and the like can be classified.
The result is the result generated after the action, including the result obtained after the normal/abnormal flow is finished. The result may be normal, abnormal, or an output formula. For example: success, failure, check formulas for specific data values, etc.
The term "condition" may include, for example, account conditions, logical element conditions, physical element conditions, institution conditions, compliance conditions, and the like for banking related businesses.
Account conditions refer to conditions related to the account itself, such as account status, account type, whether the account balance is sufficient, and the like.
The logical element condition refers to a condition related to a business logical element, for example: the ticket out date cannot be greater than the expiration date, etc.
The physical element condition refers to a condition related to a medium entity, such as a usb key and a ticket.
The organization condition refers to a condition related to an organization related to a business, such as a total line, a branch line and a branch line; beijing and Shanghai, etc.
The compliance conditions refer to conditions related to relevant laws and regulations, such as real-name system account opening.
By classifying the factors, the factors can be separated, the required factors can be quickly searched and extracted according to the requirements, and the factors are arranged and combined according to a factor relation table, which is described later, so that the efficiency of writing scenes and processes is improved, and the comprehensiveness of the elements of the scenes and the processes is ensured.
In addition, the "result" factor represents the result produced by the scene in which the action occurred, representing the end of that scene. In some cases, the "result" factor of one scene may become the "condition" factor of another scene, for example, when the a-scene becomes a precondition for the B-scene execution, the "result" factor of the a-scene will become the "condition" factor of the B-scene, i.e., the "result" factor of the a-scene also becomes part of the B-scene. This will be described later.
It should be noted that, the above classification method covers all the basic elements required by a business action, but when a certain type has only one factor, and there are no multiple factors to choose from, so that multiple combination schemes appear, the type and the factors thereof have no substantial meaning for the possibility of permutation and combination, at this time, the type and the factors thereof can be omitted, and when a complete scene is written, the content of the type and the factors thereof can be automatically added into the stream.
The above factors are stored in a database manner in a computer, and a factor data table can be established to store and manage each factor by type, thereby improving the efficiency of factor extraction.
Extraction of factor (two)
For combinable relations of the factors, a factor relation table is established in advance according to actual business execution rules and the like, wherein the table prescribes which factors can be combined with which factors to form a scene, and factors which can not be combined are not indicated in the factor relation table.
Fig. 3 is a schematic diagram for explaining the process of factor extraction and scene generation of the present invention. Referring to fig. 3, considering the efficiency of screening and extracting service factors according to a factor relation table, the process is divided into four layers of a service core layer, a service execution layer, a service generation layer and a service role layer for factor types in the factor extraction process and the scene generation process for processing. The service core layer comprises service factors of a product type, the service execution layer comprises service factors of an event type and a condition type, the service generation layer comprises service factors of a time type and a channel type, and the service role layer comprises service factors of a role type.
In the process of extracting the factors, the factors are preferably extracted layer by layer according to the sequence of a service core layer, a service execution layer, a service generation layer and a service role layer. Firstly, extracting factors belonging to the type of a business core layer (namely, the type of a product) which are matched with keywords extracted from a description document of the business from a factor library, then extracting factors which have combinable relations with the extracted factors belonging to the type of a previous layer by referring to a factor relation table, and extracting factors of a result type which has combinable relations with the extracted event type factors by referring to the factor relation table. I.e. layer-by-layer, according to combinable relationships between factors of the type of the adjacent layers.
Specifically, firstly, keyword retrieval is carried out on a description document of a service to be detected, keywords reflecting the core content of the service in the description document are extracted, the keywords are matched with factors belonging to the type of the core layer of the service, namely the type of the product, in a factor library, and all relevant factors of the type of the product are screened out.
Next, referring to the factor relation table, factors belonging to the types of the business execution layers, i.e., the "event", "condition" types, having combinable relation with the selected "product" type factors are extracted. In this case, in order to improve efficiency of factor extraction and subsequent scene generation, it is preferable to refer to the factor relation table and remove "event" factors and "condition" factors having no combinable relation therebetween as redundancy items.
Next, referring to the factor relation table, factors belonging to the types of the business generation layer, namely, the "time" and "channel" types, which have combinable relation with the factors of the selected "event" and "condition" types, respectively, are extracted. In this case, in order to improve the efficiency of factor extraction and subsequent scene generation, it is preferable to use a factor relation table to remove, as a redundant item, a "time" factor and a "channel" factor that do not have a combinable relation between the two factors, and to remove, as a redundant item, a factor that does not have a combinable relation between the factors belonging to the type of the business core layer, that is, the "product" type, from among the factors of the "time" and "channel" types.
Then, referring to the factor relation table, the factors of the "character" type having combinable relation with the factors of the selected "time" and "channel" type are extracted, respectively. In this case, in order to increase the efficiency of factor extraction and subsequent scenario generation, it is preferable to eliminate, as a redundant item, factors having no combinable relation with factors belonging to the types of the service execution layer and the service core layer, that is, the "event", "condition", and "product" types, from among the factors of the "role" type, with reference to the factor relation table.
In addition, the factor relation table is referred to, and the factors of the 'result' type which have combinable relation with the selected 'event' type factors are extracted.
In summary, factors are extracted layer by layer in the order of the service core layer, the service execution layer, the service generation layer, and the service role layer. That is, first, the factors belonging to the type of the business core layer, which are matched with the keywords extracted from the description document of the business, are extracted from the factor library, then the factors having combinable relations with the extracted factors belonging to the type of the previous layer are extracted layer by layer with reference to the factor relation table, and the factors of the result type having combinable relations with the extracted event type factors are extracted with reference to the factor relation table. When extracting the factors belonging to the service execution layer, the service generation layer and the service role layer by layer, referring to the factor relation table respectively, and removing the factors which do not have combinable relation with the factors belonging to the type of the layer and the layers before as redundant items.
In addition, because the whole business is essentially formed by a plurality of event strings, in order to further improve the screening and extraction efficiency, the 'event' type factors with combinable relations with the 'event' type factors can be extracted firstly based on the 'product' type factors, then the 'condition', 'time', 'channel', 'result' type factors with combinable relations with the 'event' factors respectively are extracted by referring to a factor relation table, and then the factors with no combinable relations among the 'condition', 'time', 'channel' are removed as redundant items. Then, referring to the factor relation table, the factor of the "role" type having combinable relation with the factor of the remaining "condition", "time", "channel" type is extracted.
(III) Generation of scenes and flows
After extracting all the relevant factors as described above, in order to facilitate execution of the generated scenario, it is preferable to arrange and combine the factors by referring to the factor relation table in the order of the service role layer, the service generation layer, the service execution layer, and the service core layer as shown in fig. 3, and combine the factors into the scenario by matching the corresponding result type factors according to the condition type factors used in the combination.
For ease of understanding, fig. 4 shows an example of scenario generation for a business test, for example, a banking related business. For example, the following factors are stored in the factor library in advance:
"role" person A, person B, public A, public B, institution A … …
"Time" is working day business hours, working day non-business hours, legal holiday non-business hours, securities market time … …
"Channel" is internet bank BS, internet bank CS, mobile phone bank, counter, self-service terminal … …
"Event": deposit, transfer, financial product purchase, open fund redemption, financing … …
"Condition" is that account balance is sufficient, account balance is insufficient, account is abnormal, medium is abnormal, ukey certificate expires … …
"Product" including demand deposit, regular deposit, open fund, expected financial product, net value financial product … …
Successful, failed, CVV verification error, legal holiday forward purchase, legal holiday forward redemption … …
Taking retail business as an example, gray factors in fig. 4 are extracted by the factor extraction process (only two factors are extracted for each type in the example for simplicity of description), and then, various types of factors are combined into the following scene:
Scene 1: person A, in business hours of working day, purchase the expected financial product at the counter, under the condition that the account balance is sufficient, purchase successfully;
scene 2: and the person B redeems the open foundation at the mobile phone bank in the non-business hours of the legal holiday, prompts the legal holiday and redeems with the extension under the condition of sufficient account balance.
The combined scenes can be assigned a sequence number based on the sequence of the nodes in the scene relation table, the scenes belonging to the same node are marked with the node number, so that the sequence and the front-back dependency relationship are formed among the scenes of the adjacent nodes, and finally, a flow is formed. The generated scenes can be stored in a scene library for standby, so that when the similar service is tested later, the scenes can be directly extracted from the scene library, the process of re-extracting factors and combining the scenes is saved, and the service testing efficiency can be improved.
In addition, the formed flow can be stored in a flow library for standby, so that when the service test is carried out on similar services, the flow can be directly extracted from the flow library, the process of regenerating the flows is saved, and the service test efficiency can be improved.
(IV) Generation of scenes and flows of new services
When a new service appears as the service is continued to be developed and innovated, new factors that have not been included in the factor library may appear in the service description document of the new service. At this time, keywords are extracted from the business description document, the keywords are matched with factors in the original factor library, corresponding factors are extracted from the factor library according to the method, new factors corresponding to the keywords which are not matched with the factors are generated and stored in the factor library, and meanwhile, according to the business description document, a factor relation table is updated according to combinable relations between the new factors and the original factors.
Fig. 5 is a diagram for explaining a procedure of generating a business test flow including a new factor. First, a scene relation table (not shown) is updated for a new service based on its description document. Then, referring to fig. 5, the new factor is combined with the accumulated history factor related to the service extracted from the factor library according to the updated factor relation table, and a new scene containing the new factor is generated and stored in the scene library. In addition, for scenarios where no new factors are used in the factor combining process, it is possible to directly extract from the accumulated historical scenarios related to the business. And finally, connecting the new scene and the accumulated historical scenes according to the updated scene relation table to form a new flow. The process can be stored in a process library, so that the process can be directly called from the process library when the test of related services is needed later, and the waste of system resources and the reduction of working efficiency caused by repeated generation of the same process are avoided.
(V) System for executing the scenario and procedure generating method
The scene and flow generating method can be realized by combining software and hardware by using a computer and the like. Fig. 6 shows a functional block diagram of a system implementing the scenario and process generation method described above.
As shown in fig. 6, the flow generation system includes an execution unit 100 and a database 200. The execution unit 100 includes a factor extraction unit 110, a scene generation unit 120, a scene sorting unit 130, and a factor maintenance unit 140.
The database 200 includes a factor library 210 storing business factors, a scene library 220 storing generated scenes, a flow library 230 storing generated flows, a factor relation table 240 for specifying combinable relations among various types of factors, a factor data table 250 storing information such as a data dictionary corresponding to each factor by type, and a scene relation table 260 for specifying ordering relations among scenes in a business.
The factor extraction unit 110 includes a measured demand extraction function of extracting keywords from the business description document, and a factor extraction function of extracting factors matching the keywords extracted from the business description document from a factor library according to the factor relation table 240.
The scene generating unit 120 includes a scene generating function of combining the factors extracted by the factor extracting unit 110 into a scene according to the factor relation table 240, and a scene calling function of calling the generated history scene from the scene library.
The scene ordering unit 130 includes a flow generating function of ordering the scenes generated by the scene generating unit 120 in accordance with the scene relation table 260 to generate flows, and a flow calling function of calling the generated history flows from the flow library.
The factor maintenance unit 140 includes a new factor generation function of generating a new factor corresponding to a keyword extracted from the business description document according to the keyword by the factor extraction unit 110 and storing the new factor in the factor library 210, a factor editing function of editing and modifying the factors in the factor library 210, and a factor relationship maintenance function of editing and maintaining combinable relationships among the factors in the factor relationship table 240 according to the description document of the present business.
In the above description, a description has been given of a method of specifying whether or not each factor has a combinable relationship by a factor relationship table, and when there are a large number of factors or the relationship between the factors is very complex, a plurality of factor relationship tables may be prepared to specify the combinable relationship between the factors, and the plurality of tables may also constitute a form of factor relationship library. Similarly, a scene relation table for defining a connection relation between scenes may be a plurality of tables, and may be a form of a scene relation library.
The factor relation table 240 and the factor data table 250 may form one database with the factor library 210, and the scenario relation table 260 may form one database with the scenario library 220. Or they may form a database with the flow library 230.
The scene and flow generating method and system for service test, which are described above, can continuously update the service factor library, the scene library and the flow library, and can build up resources. Because the minimum service factor combination which is classified and managed according to attributes such as service entities, service actions and the like is utilized to form new scenes and flows, the service factor multiplexing degree is high, and a factor library, a scene library and a flow library become resource containers with repeated production capacity, so that scene analysis for service test can be more comprehensive, stable and reliable, and the problem of insufficient flow coverage caused by the limitation of personnel on service understanding is solved. Because the system automatically generates scenes and flows in batches, the analysis and preparation time before the test can be shortened, the test analysis efficiency can be improved, and the test analysis period can be shortened.
Claims (22)
1. A scenario generation system for business testing, comprising:
a factor library for storing factors as minimum service elements for forming one service,
A factor extracting unit for extracting a factor matching the keyword extracted from the description document of the service from the factor library, and
A scene generation unit for combining the factors extracted by the factor extraction unit into a scene according to a predetermined factor relation table for specifying whether the factors have combinable relations;
a factor maintenance unit, when there is no factor matched with the keyword extracted from the service description document in the factor library, generating a new factor corresponding to the keyword, storing the new factor in the factor library, and updating the factor relation table for combinable relation between each factor and the new factor according to the description document of the service;
the scene generating unit combines the factors extracted by the factor extracting unit and the new factors generated by the factor maintaining unit into a scene according to the updated factor relation table;
the scene generation unit combines the extracted factors according to the sequence of role types, time types, channel types, event types, condition types and product types, and refers to the factor relation table, and generates a scene according to the condition type factors used in the combination and the corresponding result type factors;
The factor relation table is used for specifying whether each factor has a combinable relation or not, and factors which are not indicated in the factor relation table to be combinable cannot be combined.
2. The scene generation system of claim 1, wherein,
The factor carries identification information and type information indicating the type of the factor;
the types at least comprise seven types of products, roles, channels, time, events, conditions and results;
The type information of the factor is used to indicate that the factor is at least one of the seven types.
3. The scene generation system of claim 2, wherein,
In the actions of the factor extraction unit and the scene generation unit, factors of the product, role, channel, time, event and condition types in the factors are divided into a service core layer, a service execution layer, a service generation layer and a service role layer for processing, wherein the service core layer comprises the product type factors, the service execution layer comprises the event and condition type factors, the service generation layer comprises the time and channel type factors, and the service role layer comprises the role type factors.
4. The scene generation system of claim 3, wherein,
The factor extraction unit extracts the factors layer by layer according to the sequence of the service core layer, the service execution layer, the service generation layer and the service role layer.
5. The scene generation system of claim 4, wherein,
The factor extracting unit firstly extracts factors which are matched with keywords extracted from the description document of the service and belong to the type of the service core layer from a factor library, then extracts factors which have combinable relations with the extracted factors belonging to the type of the previous layer by referring to the factor relation table, and extracts factors of a result type which has combinable relations with the extracted event type factors by referring to the factor relation table.
6. The scene generation system of claim 5, wherein,
And when the factor extraction unit extracts the factors belonging to the service execution layer, the service generation layer and the service role layer by layer, referring to the factor relation table respectively, and removing the factors which do not have combinable relation with the factors belonging to the types of the layer and the previous layers as redundant items.
7. The scene generation system of any of claims 3 to 6, wherein,
The scene generation unit refers to the factor relation table, combines factors of each layer according to the sequence of the service role layer, the service generation layer, the service execution layer and the service role layer, and matches corresponding result type factors according to the condition type factors used in the combination to generate a scene.
8. The scene generation system of claim 2, wherein,
The factor extraction unit includes:
A module for extracting product type factors matching the keywords extracted from the business description document from the factor library,
A module for extracting event type factors having combinable relation with the extracted product type factors with reference to the factor relation table,
A module for extracting factors of a condition type, a time type, a channel type, and a result type having combinable relations with the extracted event type factors, respectively, with reference to the factor relation table,
A module for eliminating the factors which are different from the three types of factors and have combinable relations at the same time from the factors of the condition type, the time type and the channel type which are extracted by referring to the factor relation table, and
And referring to the factor relation table, extracting a role type factor module which has combinable relation with the factors of the condition type, the time type and the channel type after the elimination.
9. The scene generation system of claim 1, further comprising:
and the scene library is used for storing the scenes generated by the scene generating unit.
10. A flow generation system for business testing, comprising:
The scene generation system of any of claims 1 to 9, and
And the scene ordering unit is used for ordering the scenes generated by the scene generating unit according to a preset scene relation table for defining ordering relations among the scenes in the service to form an executable flow.
11. The process generation system of claim 10, further comprising:
And the flow library is used for storing the flow generated by the scene ordering unit.
12. A scenario generation method for service testing, comprising:
A factor extraction step of extracting, from a factor library, a factor matching a keyword extracted from a description document of the present service,
A scene generating step, namely combining the factors extracted in the factor extracting step into a scene according to a preset factor relation table;
a factor maintenance step, when no factor matched with the keyword extracted from the service description document exists in the factor library, generating a new factor corresponding to the keyword, storing the new factor in the factor library, and updating the factor relation table according to the description document of the service and the combinable relation of each factor and the new factor;
in the scene generating step, the factors extracted in the factor extracting step and the new factors generated in the factor maintaining step are combined into a scene according to an updated factor relation table;
In the scene generation step, the extracted factors are combined according to the sequence of role types, time types, channel types, event types, condition types and product types, and the factor relation table is referred, and corresponding result type factors are matched according to the condition type factors used in the combination, so that a scene is generated;
wherein the factor is a minimum service element for forming a service;
The factor relation table is used for specifying whether each factor has a combinable relation or not, and factors which are not indicated in the factor relation table to be combinable cannot be combined.
13. The scene generation method according to claim 12, wherein,
The factor carries identification information and type information indicating the type of the factor;
the types at least comprise seven types of products, roles, channels, time, events, conditions and results;
The type information of the factor is used to indicate that the factor is at least one of the seven types.
14. The scene generation method of claim 13, wherein,
In the factor extraction step and the scene generation step, the factors of the product, role, channel, time, event and condition types in the factors are divided into a service core layer, a service execution layer, a service generation layer and a service role layer for processing, wherein the service core layer comprises the product type factors, the service execution layer comprises the event and condition type factors, the service generation layer comprises the time and channel type factors, and the service role layer comprises the role type factors.
15. The scene generation method of claim 14, wherein,
In the factor extraction step, the factors are extracted layer by layer according to the sequence of the service core layer, the service execution layer, the service generation layer and the service role layer.
16. The scene generation method of claim 15, wherein,
In the factor extraction step, first, the factor belonging to the type of the service core layer, which is matched with the keyword extracted from the description document of the service, is extracted from the factor library, then, the factor having a combinable relation with the extracted factor belonging to the type of the previous layer is extracted layer by referring to the factor relation table, and the factor having a combinable relation with the extracted event type factor is extracted by referring to the factor relation table.
17. The scene generation method of claim 16, wherein,
In the factor extraction step, when the factors belonging to the service execution layer, the service generation layer and the service role layer are extracted layer by layer, the factor relation table is respectively referred to, and the factors which do not have combinable relation with the factors belonging to the types of the layers and the layers before are removed as redundant items.
18. The scene generation method according to any of the claims 14 to 17, characterized in that,
In the scene generation step, referring to the factor relation table, combining factors of each layer according to the sequence of the service role layer, the service generation layer, the service execution layer and the service role layer, and generating a scene by matching corresponding result type factors according to the condition type factors used in the combination.
19. The scene generation method of claim 13, wherein,
The factor extraction step includes:
a step of extracting a product type factor matching the keyword extracted from the business description document from the factor library,
A step of extracting event type factors having combinable relations with the extracted product type factors with reference to the factor relation table,
A step of extracting factors of a condition type, a time type, a channel type, and a result type having combinable relations with the extracted event type factors, respectively, with reference to the factor relation table,
A step of eliminating factors having combinable relationships at different times among the three types of factors from the extracted factors of the condition type, time type, channel type by referring to the factor relationship table, and
And extracting role type factors which have combinable relations with the factors of the condition type, the time type and the channel type at the same time by referring to the factor relation table.
20. The scene generation method of claim 12, further comprising:
And a scene saving step of saving the scene generated in the scene generating step to a scene library.
21. A flow generation method for business testing, comprising:
the scene generation method of any of claims 12 to 20, and
And a scene ordering step, wherein the scenes generated in the scene generating step are ordered according to a predetermined scene relation table for defining ordering relations among the scenes in the service to form an executable flow.
22. The flow generation method of claim 21, further comprising:
and a flow preservation step of preserving the flow formed in the scene ordering step into a flow library.
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