CN112966971B - Project workload assessment method and device - Google Patents

Project workload assessment method and device Download PDF

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CN112966971B
CN112966971B CN202110340079.2A CN202110340079A CN112966971B CN 112966971 B CN112966971 B CN 112966971B CN 202110340079 A CN202110340079 A CN 202110340079A CN 112966971 B CN112966971 B CN 112966971B
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马腾
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CCB Finetech Co Ltd
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Abstract

The invention discloses a project workload assessment method and device, and relates to the field of artificial intelligence. One embodiment of the method comprises: acquiring a project to be evaluated according to a request of the workload of the evaluation project, identifying a business requirement corresponding to the project to be evaluated, and acquiring one or more business functions contained in the project to be evaluated; classifying one or more service functions, and determining an existing service function and a newly added service function in the one or more service functions; based on a machine learning algorithm, calculating the workload of the existing service function, and evaluating the workload of the newly added service function according to the service function evaluation template; and determining the workload of the project to be evaluated according to the workload of the existing service function and the workload of the newly added service function. According to the embodiment, the workload evaluation efficiency can be improved, the workload evaluation period is shortened, the waste of human resources is reduced, and the accuracy of workload evaluation is improved due to the application of the machine learning algorithm.

Description

Project workload assessment method and device
Technical Field
The invention relates to the field of artificial intelligence, in particular to a project workload assessment method and device.
Background
The total number of the bank software projects is large, the project complexity is high, and difficulty is brought to project workload evaluation. Currently, project workload is mainly evaluated depending on personal experience and capability, a uniform workload evaluation method is lacked, and each project has differences, so that inaccurate evaluation is caused; in addition, for the workload evaluation of the project group, a large amount of human resources are consumed, and the evaluation period is long; moreover, the workload evaluation data lacks effective accumulation and precipitation, and lacks effective help for the workload evaluation of the iterative optimization type project; in addition, without quantitative basis for workload assessment, it is difficult to obtain approval from project management or financial settlement departments during project establishment and settlement stages.
Disclosure of Invention
In view of this, embodiments of the present invention provide a project workload assessment method and apparatus, which can improve workload assessment efficiency, shorten workload assessment period, reduce waste of human resources, and improve accuracy of workload assessment by applying a machine learning algorithm.
To achieve the above object, according to an aspect of an embodiment of the present invention, a project workload assessment method is provided.
The project workload evaluation method of the embodiment of the invention comprises the following steps: acquiring a project to be evaluated according to a request of evaluating project workload, identifying a business requirement corresponding to the project to be evaluated, and acquiring one or more business functions contained in the project to be evaluated; classifying the one or more service functions, and determining an existing service function and a newly added service function in the one or more service functions; based on a machine learning algorithm, calculating the workload of the existing service function, and evaluating the workload of the newly added service function according to a service function evaluation template; and determining the workload of the project to be evaluated according to the workload of the existing service function and the workload of the newly added service function.
Optionally, the calculating the workload of the existing business function based on the machine learning algorithm includes: determining the reference workload of the existing business function based on a machine learning algorithm; determining a modification coefficient corresponding to the existing service function according to the modification type corresponding to the existing service function; and substituting the reference workload of the existing service function and the modification coefficient corresponding to the existing service function into an existing service function workload evaluation formula, and calculating the workload of the existing service function.
Optionally, the determining the benchmark workload of the existing business function based on the machine learning algorithm includes: acquiring one or more target service functions contained in a history item, wherein the existing service function is a service function in the one or more target service functions; measuring the reference workload of the one or more target service functions by adopting a linear regression model according to the historical workload of the one or more target service functions, the modification frequency corresponding to the one or more target service functions and the proportion of the one or more target service functions in the historical items; and determining the reference workload of the existing service function according to the reference workload of the one or more target service functions.
Optionally, the method further comprises: updating the one or more target business functions; and updating the reference workload of the one or more target business functions by adopting a linear regression model according to the information corresponding to the one or more target business functions.
Optionally, the determining, according to the modification type corresponding to the existing service function, a modification coefficient corresponding to the existing service function includes: determining a modification type corresponding to the existing service function, wherein the modification type comprises: parameter type modification, service logic modification, data type modification and interface type modification; and inquiring the modification coefficient corresponding to the existing service function according to the modification type corresponding to the existing service function based on the corresponding relation between the pre-established modification type and the modification coefficient.
Optionally, the correspondence between the modification type and the modification coefficient is established according to the following procedure: acquiring different modification types, and setting modification coefficients corresponding to the different modification types; and establishing a corresponding relation between the modification types and the modification coefficients according to the different modification types and the modification coefficients corresponding to the different modification types.
Optionally, after determining an existing service function and a newly added service function in the one or more service functions, the method further includes: judging whether the existing service function is modified in the project to be evaluated; if so, determining that the workload of the existing service function needs to be evaluated; if not, determining that the workload of the existing service function does not need to be evaluated, and filtering the existing service function.
Optionally, the evaluating the workload of the newly added service function according to the service function evaluation template includes: after filling the related information of the newly added service function according to the service function evaluation template, acquiring the related information of the newly added service function; and evaluating the newly added service function by utilizing a function point analysis method according to the related information of the newly added service function to obtain the workload of the newly added service function.
Optionally, the method further comprises: during the process of filling the related information of the newly added service function according to the service function evaluation template or after filling the related information of the newly added service function according to the service function evaluation template, checking the filled information by using a preset information checking rule; and acquiring a verification result, and modifying the filled information according to the verification result to acquire the related information of the newly added service function.
Optionally, the service function assessment template includes: the service function upgrading method comprises the following steps that a service requirement number, a service requirement name, a service function number, a service function name, a logic subsystem related to a service function, a transaction function contained in the service function, a data function contained in the service function, a function type corresponding to the service function and an upgrading type corresponding to the service function are included; and the preset information checking rule comprises the following steps: and splitting the service function into a limited transaction function and a data function, and limiting the difficulty coefficient corresponding to the function type.
Optionally, after determining the workload of the project to be evaluated according to the workload of the existing service function and the workload of the newly added service function, the method further includes: if the workload of the project to be evaluated is greater than the mean workload of the reference project, monitoring the project to be evaluated; and in the implementation process of the project to be evaluated, displaying the implementation progress of the project to be evaluated by using a preset display form according to the workload of the project to be evaluated, and monitoring and early warning the implementation progress of the project to be evaluated, wherein the preset display form comprises the following steps: report forms and visual compasses.
Optionally, after determining the workload of the project to be evaluated according to the workload of the existing service function and the workload of the newly added service function, the method further includes: and storing the workload of the project to be evaluated, and performing model optimization by using the workload of the project to be evaluated.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided a project workload estimation apparatus.
The project workload evaluation device of the embodiment of the invention comprises: the acquisition module is used for acquiring a project to be evaluated according to a request of evaluating the workload of the project, identifying a service requirement corresponding to the project to be evaluated and acquiring one or more service functions contained in the project to be evaluated; the classification module is used for classifying the one or more service functions and determining the existing service function and the newly added service function in the one or more service functions; the evaluation module is used for calculating the workload of the existing service function based on a machine learning algorithm and evaluating the workload of the newly added service function according to a service function evaluation template; and the determining module is used for determining the workload of the project to be evaluated according to the workload of the existing service function and the workload of the newly added service function.
Optionally, the evaluation module is further configured to: determining the reference workload of the existing business function based on a machine learning algorithm; determining a modification coefficient corresponding to the existing service function according to the modification type corresponding to the existing service function; and substituting the reference workload of the existing service function and the modification coefficient corresponding to the existing service function into an existing service function workload evaluation formula, and calculating the workload of the existing service function.
Optionally, the evaluation module is further configured to: acquiring one or more target service functions contained in a history item, wherein the existing service function is a service function in the one or more target service functions; measuring the reference workload of the one or more target business functions by adopting a linear regression model according to the historical workload of the one or more target business functions, the modification frequency corresponding to the one or more target business functions and the proportion of the one or more target business functions in the historical items; and determining the reference workload of the existing service function according to the reference workload of the one or more target service functions.
Optionally, the evaluation module is further configured to: updating the one or more target business functions; and updating the reference workload of the one or more target business functions by adopting a linear regression model according to the information corresponding to the one or more target business functions.
Optionally, the evaluation module is further configured to: determining a modification type corresponding to the existing service function, wherein the modification type comprises: parameter type modification, service logic modification, data type modification and interface type modification; and inquiring the modification coefficient corresponding to the existing service function according to the modification type corresponding to the existing service function based on the corresponding relation between the pre-established modification type and the modification coefficient.
Optionally, the classification module is further configured to: judging whether the existing service function is modified in the project to be evaluated; if so, determining that the workload of the existing service function needs to be evaluated; if not, determining that the workload of the existing service function does not need to be evaluated, and filtering the existing service function.
Optionally, the evaluation module is further configured to: after filling the related information of the newly added service function according to the service function evaluation template, acquiring the related information of the newly added service function; and evaluating the newly added service function by utilizing a function point analysis method according to the related information of the newly added service function to obtain the workload of the newly added service function.
Optionally, the evaluation module is further configured to: during the process of filling the related information of the newly added service function according to the service function evaluation template or after filling the related information of the newly added service function according to the service function evaluation template, checking the filled information by using a preset information checking rule; and acquiring a verification result, and modifying the filled information according to the verification result to acquire the related information of the newly added service function.
Optionally, the apparatus further comprises a monitoring module configured to: if the workload of the project to be evaluated is greater than the average workload of the reference project, monitoring the project to be evaluated; and in the implementation process of the project to be evaluated, displaying the implementation progress of the project to be evaluated by using a preset display form according to the workload of the project to be evaluated, and monitoring and early warning the implementation progress of the project to be evaluated, wherein the preset display form comprises the following steps: report forms and visual compasses.
Optionally, the apparatus further comprises an optimization module configured to: and storing the workload of the project to be evaluated, and performing model optimization by using the workload of the project to be evaluated.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided an electronic apparatus.
An electronic device of an embodiment of the present invention includes: one or more processors; the storage device is used for storing one or more programs, and when the one or more programs are executed by one or more processors, the one or more processors are enabled to realize the project workload evaluation method of the embodiment of the invention.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided a computer-readable medium.
A computer-readable medium of an embodiment of the present invention has a computer program stored thereon, and when the program is executed by a processor, the program implements a project workload assessment method of an embodiment of the present invention.
One embodiment of the above invention has the following advantages or benefits: the method comprises the steps of identifying a project to be evaluated into one or more service functions, classifying the one or more service functions to obtain the existing service functions and the newly added service functions, calculating the workload of the existing service functions based on a machine learning algorithm, evaluating the workload of the newly added service functions through a service function evaluation template, and further obtaining the workload of the project to be evaluated. In addition, the unified workload evaluation method provided by the embodiment of the invention can provide quantitative data for evaluating the workload, avoids the problem that the approval of a project management department or a financial settlement department is difficult to obtain in project establishment and settlement stages, and has practicability.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of the main steps of a project workload assessment method according to an embodiment of the invention;
FIG. 2 is a schematic diagram of the main process of computing the workload of an existing business function based on a machine learning algorithm according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a main process of evaluating the workload of the newly added service function according to the service function evaluation template according to the embodiment of the present invention;
FIG. 4 is a schematic diagram of the main process of calculating the workload of an existing business function according to an embodiment of the invention;
FIG. 5 is a schematic diagram of the main blocks of a project workload assessment apparatus according to an embodiment of the present invention;
FIG. 6 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 7 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
FIG. 1 is a schematic diagram of the main steps of a project workload assessment method according to an embodiment of the present invention. As shown in FIG. 1, the main steps of the project workload assessment method may include:
step S101, acquiring a project to be evaluated according to a request of evaluating the workload of the project, identifying a service requirement corresponding to the project to be evaluated, and acquiring one or more service functions contained in the project to be evaluated;
step S102, classifying one or more service functions, and determining the existing service function and the newly added service function in the one or more service functions;
step S103, based on a machine learning algorithm, calculating the workload of the existing service function, and evaluating the workload of the newly added service function according to a service function evaluation template;
step S104: and determining the workload of the project to be evaluated according to the workload of the existing service function and the workload of the newly added service function.
In step S101, when the workload of the project needs to be evaluated, a request for evaluating the workload of the project is received, and then the project to be evaluated, that is, the project that needs to be evaluated is obtained according to the received request. The number of the items to be evaluated is one or more, that is, the items to be evaluated can be a set of items requiring workload evaluation; also, the item to be evaluated may involve multiple systems. After the project to be evaluated is obtained, the business requirement corresponding to the project to be evaluated is analyzed, and the business function contained in the project to be evaluated is identified. Wherein, the number of the service functions is one or more.
In step S102, one or more business functions included in the item to be evaluated are classified. Specifically, for each service function, whether the service function is an existing service function or a newly added service function is determined, so that one or more service functions can be divided into the existing service function and the newly added service function, so as to analyze the existing service function and the newly added service function respectively in the follow-up process.
It should be noted that, if the existing service function is not modified in the project to be evaluated, the workload corresponding to the existing service function may be considered to be 0. That is, no workload estimation is required for existing service functions that are not modified. Therefore, as an embodiment of the present invention, after determining an existing service function and a newly added service function in one or more service functions, the method for evaluating project workload may further include: judging whether the existing service function is modified in the project to be evaluated; if yes, determining that the workload of the existing service function needs to be evaluated; if not, determining that the workload of the existing service function does not need to be evaluated, and filtering the existing service function.
Specifically, after the existing service functions corresponding to the project to be evaluated are obtained, each existing service function is analyzed, and whether the existing service functions are modified in the project to be evaluated is judged. If the modification is carried out, the workload of the existing service function needs to be evaluated; if not, the workload of the existing service function does not need to be evaluated, and the existing service function is filtered.
In step S103, different methods are used for the existing service function and the newly added service function to perform workload estimation.
And (I) for the existing business functions, workload evaluation can be carried out based on a machine learning algorithm. The machine learning algorithm is an algorithm for automatically analyzing and obtaining rules from data and predicting unknown data by using the rules. Considering that the existing service function is an existing service function, data related to the existing service function, such as historical workload, modification times and the like of the existing service function, can be acquired. Therefore, the data related to the existing service function can be analyzed, and the workload corresponding to the existing service function can be further obtained. In summary, the workload of the existing business function can be evaluated based on a machine learning algorithm.
Fig. 2 is a schematic diagram of a main process of calculating the workload of an existing business function based on a machine learning algorithm according to an embodiment of the present invention. In fig. 2, the main process of calculating the workload of the existing business function based on the machine learning algorithm may include:
step S201, based on a machine learning algorithm, determining the reference workload of the existing service function;
step S202, determining a modification coefficient corresponding to the existing service function according to the modification type corresponding to the existing service function;
step S203, substituting the reference workload of the existing service function and the modification coefficient corresponding to the existing service function into the workload evaluation formula of the existing service function, and calculating the workload of the existing service function.
The reference workload of the existing service function is the workload reference data required for modifying the existing service function. In step S202, a modification coefficient corresponding to an existing service function is determined according to a modification type corresponding to the existing service function. Then, in step S203, the reference workload of the existing service function and the modification coefficient corresponding to the existing service function are substituted into the existing service function workload evaluation formula, and the workload of the existing service function is calculated. The existing service function workload evaluation formula is as follows: the workload of the existing service function is (the reference workload of the existing service function) × (the modification coefficient corresponding to the existing service function).
It should be noted that the number of the existing service functions is one or more, and therefore, the workload of each existing service function is calculated according to the existing service function workload estimation formula. And finally, accumulating the workload of all the existing service functions to obtain the workload of the existing service functions in the project to be evaluated.
And (II) for the newly added service function, the workload evaluation can be carried out through the service function evaluation template. The service function evaluation template may be used to obtain information related to the service function. Specifically, the following information is filled in the service function evaluation template: the service function upgrading method comprises the steps of service requirement number, service requirement name, service function number, service function name, logic subsystem related to service function, transaction function contained in service function, data function contained in service function, function type corresponding to service function and upgrading type corresponding to service function. For the newly added service function, since there is no historical reference data, the attribute related to the newly added service function can be filled in by means of the service function evaluation template, and then the workload evaluation is performed.
Fig. 3 is a schematic diagram of a main process of evaluating the workload of the added service function according to the service function evaluation template according to the embodiment of the present invention. As shown in figure 3 of the drawings,
step S301, after filling the relevant information of the newly added service function according to the service function evaluation template, acquiring the relevant information of the newly added service function;
step S302, according to the related information of the newly added service function, the newly added service function is evaluated by using a function point analysis method, and the workload of the newly added service function is obtained.
Among them, a Function Point Analysis method, abbreviated as FPA, is a method for measuring a software scale from a user perspective. From the perspective of a user, the FPA divides the system into two categories, namely a data function and an object function, calculates function points according to specific rules, and finally adjusts the number of the function points by combining with characteristic factors of the system, thereby obtaining the final system scale.
In the embodiment of the invention, the related information of the newly added service function can be obtained through the service function evaluation template, and then the newly added service function is evaluated by means of the FPA method to obtain the workload of the newly added service function.
In addition, the number of the newly added service functions is one or more, so the workload of each newly added service function is obtained according to the method. And finally, accumulating the workload of all the newly added service functions to obtain the workload of the newly added service functions in the project to be evaluated.
In step S104, the workload of the existing service function in the project to be evaluated and the workload of the newly added service function in the project to be evaluated are summed to obtain the workload of the project to be evaluated.
As an embodiment of the present invention, after determining the workload of the project to be evaluated according to the workload of the existing service function and the workload of the newly added service function, the project workload evaluation method may further include:
(1) and if the workload of the project to be evaluated is greater than the mean value of the workload of the reference project, monitoring the project to be evaluated.
The reference item is an item related to the item to be evaluated, and the workload of the reference item is known. For example, the service requirement corresponding to the reference item is the same as the service requirement corresponding to the item to be evaluated, and for example, the item to be evaluated is an upgraded version of the reference item. If the workload of the project to be evaluated is greater than the average workload of the reference project, the project to be evaluated can be monitored.
(2) In the implementation process of the project to be evaluated, the implementation progress of the project to be evaluated is displayed in a preset display form according to the workload of the project to be evaluated, and the implementation progress of the project to be evaluated is monitored and early warned, wherein the preset display form comprises the following steps: report forms and visual compasses.
In addition, the workload use condition of the project to be evaluated can be monitored through a report form, a visual compass and other display forms.
The embodiment of the invention provides a unified workload assessment method, which can identify a project to be assessed as one or more service functions, classify the one or more service functions to obtain the existing service functions and the newly added service functions, calculate the workload of the existing service functions based on a machine learning algorithm, and assess the workload of the newly added service functions through a service function assessment template to further obtain the workload of the project to be assessed. In addition, the unified workload evaluation method provided by the embodiment of the invention can provide quantitative data for evaluating the workload, avoids the problem that the approval of a project management department or a financial settlement department is difficult to obtain in project establishment and settlement stages, and has practicability.
The application of the machine learning algorithm can improve the efficiency of workload evaluation and shorten the evaluation period of workload. In the embodiment of the invention, the reference workload of the existing business function is determined mainly by using a machine learning algorithm. The specific implementation can be as follows:
(1) and acquiring one or more target business functions contained in the history item.
The historical item is an item of already evaluated workload, that is, the historical workload of the target service function can be acquired. And the existing business function in the project to be evaluated is the business function in one or more target business functions.
It should be noted that the historical workload of the target business function refers to the workload of the target business function in the historical item. For example, there are history entries 1, 2, and 3, and all of the 3 history entries have the target business function a. For the historical item 1, the target service function A is a newly added service function, and the historical workload corresponding to A is M1; for the historical item 2, A is an existing service function, and the A is modified in the historical item 2 to obtain a historical workload M2 corresponding to the A; for the history item 3, a is an existing service function, and the history item 3 is not modified to obtain that the history workload corresponding to a is 0.
(2) And measuring the reference workload of the one or more target service functions by adopting a linear regression model according to the historical workload of the one or more target service functions, the modification frequency corresponding to the one or more target service functions and the proportion of the one or more target service functions in historical items. The linear regression model is one of machine learning models, assumes that features and classification results have a linear relationship, and has the characteristic of accurate prediction precision.
The historical workload of the target business function has been explained above and will not be described here. The modification frequency corresponding to the target business function refers to the number of modifications of the target business function in the history item, for example, in the above example, the number of modifications of the target business function a in the history items 1 to 3 is 1. The proportion of the target business function in the historical items refers to the proportion value of the target business function in each historical item, and can be set by a project developer or in combination with the modification frequency of the historical items. Generally, the higher the proportion of the service function in a project, the more important the service function is, or the incomplete service function is, the more workload needs to be put into it. For example, history item 1 has 3 business functions of the target business function A, B, C, and a may be set to have a proportion of 0.2 in history item 1, B to have a proportion of 0.5 in history item 1, and C to have a proportion of 0.3 in history item 1.
In the embodiment of the present invention, for each target service function, a linear regression model may be used to measure the workload reference data required for modifying the target service function, that is, the reference workload of the target service function, according to the historical workload of the target service function, the modification frequency corresponding to the target service function, and the proportion of the target service function in the historical item.
(3) And determining the reference workload of the existing business function according to the reference workloads of one or more target business functions.
Because the existing service function in the project to be evaluated is the service function in one or more target service functions, the reference workload of the existing service function can be obtained.
In addition, before the project workload evaluation method provided by the embodiment of the invention is utilized, project evaluation can be carried out in a manual filling and evaluation mode, and then the obtained evaluation data can be stored as historical data. Therefore, in the initial stage of workload estimation by using the project workload estimation method provided by the embodiment of the invention, the workload estimation can be carried out on the existing service function based on the machine learning algorithm by means of the stored historical data.
As an embodiment of the present invention, the project workload evaluation method may further include: updating one or more target business functions; and updating the reference workload of the one or more target business functions by adopting a linear regression model according to the information corresponding to the one or more target business functions.
In the embodiment of the present invention, history items are continuously increased, that is, items of which workloads have been evaluated are more and more, and in order to ensure accuracy of workload evaluation, it is necessary to update the target service function, and update the history workload of the target service function, the modification frequency corresponding to the target service function, and the proportion of the target service function in the history items. And then, measuring the reference workload of the target business function again by adopting a linear regression model according to the updated data so as to complete the reference workload update of the target business function.
Furthermore, as an embodiment of the present invention, after determining the workload of the project to be evaluated according to the workload of the existing service function and the workload of the newly added service function, the project workload evaluation method may further include: and storing the workload of the project to be evaluated, and performing model optimization by using the workload of the project to be evaluated. That is, when the reference workload of the target business function is updated, the workload of the project to be evaluated can be combined, so that the accuracy of workload evaluation is improved.
The amount of work required for different types of modifications to existing service functions is different. Therefore, in the method for calculating the workload of the existing service function, the modification type corresponding to the existing service function also needs to be considered. Therefore, as an embodiment of the present invention, determining a modification coefficient corresponding to an existing service function according to a modification type corresponding to the existing service function may include: determining a modification type corresponding to the existing service function; and inquiring the modification coefficient corresponding to the existing service function according to the modification type corresponding to the existing service function based on the pre-established corresponding relation between the modification type and the modification coefficient.
Wherein the modification type may include: parameter type modification, service logic modification, data type modification and interface type modification. If the modification type corresponding to the existing service function is modification other than parameter type modification, service logic modification, data type modification and interface type modification, the modification type corresponding to the existing service function may be considered as other modification types. Of course, other modification types may be named according to the actual situation.
In the embodiment of the invention, the corresponding relation between the modification type and the modification coefficient is established according to the following processes: acquiring different modification types, and setting modification coefficients corresponding to the different modification types; and establishing a corresponding relation between the modification type and the modification coefficient according to the different modification types and the modification coefficients corresponding to the different modification types. For example, the modification coefficient corresponding to parameter type modification is set to 0.4, the modification coefficient corresponding to service logic modification is set to 0.7, the modification coefficient corresponding to data type modification is set to 0.4, and the modification coefficient corresponding to interface type modification is set to 0.5. For the modification coefficients corresponding to other modification types, the modification coefficient can default to 0.6.
Fig. 4 is a schematic diagram of a main process of calculating the workload of an existing business function according to an embodiment of the present invention. As shown in fig. 4, the main process of calculating the workload of the existing business function may include:
step S401, selecting an existing service function from existing service functions contained in the item to be evaluated;
step S402, judging whether the existing service function is modified in the project to be evaluated, if so, executing step S403, and if not, executing step S407;
step S403, determining the reference workload of the existing business function based on a machine learning algorithm;
step S404, determining the modification type corresponding to the existing service function;
step S405, based on the pre-established corresponding relationship between the modification type and the modification coefficient, querying the modification coefficient corresponding to the existing service function according to the modification type corresponding to the existing service function;
step S406, substituting the reference workload of the existing service function and the modification coefficient corresponding to the existing service function into the workload evaluation formula of the existing service function, and calculating the workload of the existing service function;
step S407, determining that the workload of the existing service function does not need to be evaluated, and considering that the workload of the existing service function is 0;
step S408, judging whether all the existing service functions contained in the item to be evaluated are analyzed, if so, executing step S409;
and step S409, accumulating the workload of all the existing service functions to obtain the workload of the existing service functions in the project to be evaluated.
It should be noted that the existing service function selected in step S401 is a service function that has not been analyzed according to steps S402 to S407. In addition, the main process of determining the benchmark workload of the existing business function based on the machine learning algorithm has been described above, and is not described in detail herein.
For the newly added service function, the workload evaluation can be performed through the service function evaluation template. Table 1 is a service function evaluation template. As seen from table 1, the information filled in the service function evaluation template includes: the service function upgrading method comprises the steps of service requirement number, service requirement name, service function number, service function name, logic subsystem related to service function, transaction function contained in service function, data function contained in service function, function type corresponding to service function and upgrading type corresponding to service function. It should be noted that the number of function points and the workload do not need to be filled, and are calculated by using the FPA method after information is filled.
Table 1 service function evaluation template
Figure GDA0003752219560000151
Figure GDA0003752219560000161
As an embodiment of the present invention, in the process of filling in the related information of the newly added service function according to the service function evaluation template, or after filling in the related information of the newly added service function according to the service function evaluation template, the filled information is verified by using a preset information verification rule; and acquiring a verification result, and modifying the filled information according to the verification result to acquire the related information of the newly added service function. The preset information verification rule may include: and splitting the service function into a limited transaction function and a data function, and limiting the difficulty coefficient corresponding to the function type.
Specifically, the information filled in the service function evaluation template may be checked by clicking an information check button. Of course, the inspection can be carried out after all the filling is finished, and the inspection can also be carried out in the filling process. The content which does not conform to the information checking rule can be displayed in red, and is displayed on the content part which needs to be modified or the content part which is recommended to be modified of the template. It should be noted that the content in the final template that must be modified must be empty.
According to the technical scheme of the workload assessment method, the project to be assessed is identified as one or more business functions, then the one or more business functions are classified to obtain the existing business functions and the newly added business functions, then the workload of the existing business functions is calculated based on the machine learning algorithm, the workload of the newly added business functions is assessed through the business function assessment template, and further the workload of the project to be assessed is obtained. In addition, the unified workload evaluation method provided by the embodiment of the invention can provide quantitative data for evaluating the workload, avoids the problem that the approval of a project management department or a financial settlement department is difficult to obtain in project establishment and settlement stages, and has practicability.
Fig. 5 is a schematic diagram of main blocks of a project workload evaluation apparatus according to an embodiment of the present invention. As shown in fig. 5, the main modules of the project workload evaluation apparatus 500 may include: an acquisition module 501, a classification module 502, an evaluation module 503, and a determination module 504.
The obtaining module 501 may be configured to: acquiring a project to be evaluated according to a request of the workload of the evaluation project, identifying a business requirement corresponding to the project to be evaluated, and acquiring one or more business functions contained in the project to be evaluated; the classification module 502 may be used to: classifying one or more service functions, and determining an existing service function and a newly added service function in the one or more service functions; the evaluation module 503 may be configured to: based on a machine learning algorithm, calculating the workload of the existing service function, and evaluating the workload of the newly added service function according to a service function evaluation template; the determination module 504 may be operable to: and determining the workload of the project to be evaluated according to the workload of the existing service function and the workload of the newly added service function.
As an embodiment of the present invention, the evaluation module 503 may further be configured to: determining the reference workload of the existing business function based on a machine learning algorithm; determining a modification coefficient corresponding to the existing service function according to the modification type corresponding to the existing service function; and substituting the reference workload of the existing service function and the modification coefficient corresponding to the existing service function into the workload evaluation formula of the existing service function, and calculating the workload of the existing service function.
As an embodiment of the present invention, the evaluation module 503 may further be configured to: acquiring one or more target service functions contained in a history item, wherein the existing service function is a service function in the one or more target service functions; measuring the reference workload of one or more target service functions by adopting a linear regression model according to the historical workload of one or more target service functions, the modification frequency corresponding to one or more target service functions and the proportion of one or more target service functions in historical items; and determining the reference workload of the existing business function according to the reference workloads of one or more target business functions.
As an embodiment of the present invention, the evaluation module 503 may further be configured to: updating one or more target business functions; and updating the reference workload of the one or more target business functions by adopting a linear regression model according to the information corresponding to the one or more target business functions.
As an embodiment of the present invention, the evaluation module 503 may further be configured to: determining a modification type corresponding to the existing service function; and inquiring the modification coefficient corresponding to the existing service function according to the modification type corresponding to the existing service function based on the corresponding relation between the modification type and the modification coefficient established in advance. Wherein the modification type may include: parameter type modification, service logic modification, data type modification and interface type modification.
As an embodiment of the present invention, the classification module 502 may further be configured to: judging whether the existing service function is modified in the project to be evaluated; if yes, determining that the workload of the existing service function needs to be evaluated; if not, determining that the workload of the existing service function does not need to be evaluated, and filtering the existing service function.
As an embodiment of the present invention, the evaluation module 503 may further be configured to: after filling in the related information of the newly added service function according to the service function evaluation template, acquiring the related information of the newly added service function; and evaluating the newly added service function by utilizing a function point analysis method according to the related information of the newly added service function to obtain the workload of the newly added service function.
As an embodiment of the present invention, the evaluation module 503 may further be configured to: during the process of filling the related information of the newly added service function according to the service function evaluation template or after the related information of the newly added service function is filled according to the service function evaluation template, checking the filled information by using a preset information checking rule; and acquiring a verification result, and modifying the filled information according to the verification result to acquire the related information of the newly added service function.
As an embodiment of the present invention, the project workload assessment apparatus 500 may further comprise a monitoring module (not shown in the figures). The monitoring module may be operable to: if the workload of the project to be evaluated is greater than the mean workload of the reference project, monitoring the project to be evaluated; and in the implementation process of the project to be evaluated, displaying the implementation progress of the project to be evaluated by using a preset display form according to the workload of the project to be evaluated, and monitoring and early warning the implementation progress of the project to be evaluated. Wherein, the preset display form can include: report forms and visual compasses.
As an embodiment of the present invention, the project workload assessment apparatus 500 may further comprise an optimization module (not shown in the figures). The optimization module is operable to: and storing the workload of the project to be evaluated, and performing model optimization by using the workload of the project to be evaluated.
According to the project workload assessment device provided by the embodiment of the invention, a project to be assessed can be identified into one or more service functions, then the one or more service functions are classified to obtain the existing service functions and the newly added service functions, then the workload of the existing service functions is calculated based on a machine learning algorithm, and the workload of the newly added service functions is assessed through the service function assessment template, so that the workload of the project to be assessed is obtained, the problem of inaccurate assessment caused by the dependence on personal experience and capability assessment project workload in the prior art is solved, the workload assessment efficiency is improved, the workload assessment period is shortened, the waste of human resources is reduced, and the accuracy of workload assessment can be improved by applying the machine learning algorithm. In addition, the workload evaluation device provided by the embodiment of the invention can provide quantitative data for evaluating the workload, avoids the problem that the approval of a project management department or a financial settlement department is difficult to obtain in project establishment and settlement stages, and has practicability.
FIG. 6 illustrates an exemplary system architecture 600 of a project workload assessment method or project workload assessment apparatus to which embodiments of the invention may be applied.
As shown in fig. 6, the system architecture 600 may include terminal devices 601, 602, 603, a network 604, and a server 605. The network 604 serves as a medium for providing communication links between the terminal devices 601, 602, 603 and the server 605. Network 604 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use the terminal devices 601, 602, 603 to interact with the server 605 via the network 604 to receive or send messages or the like. The terminal devices 601, 602, 603 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 605 may be a server providing various services, such as a background management server (for example only) providing support during the process of evaluating the workload of a project by a user using the terminal devices 601, 602, 603; as another example, server 605 may perform project workload assessment in accordance with embodiments of the present invention.
It should be noted that the project workload assessment method provided by the embodiment of the present invention is generally executed by the server 605, and accordingly, the project workload assessment apparatus is generally disposed in the server 605.
It should be understood that the number of terminal devices, networks, and servers in fig. 6 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for an implementation.
Referring now to FIG. 7, shown is a block diagram of a computer system 700 suitable for use with a terminal device implementing embodiments of the present invention. The terminal device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU)701, which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for the operation of the system 700 are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 701.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present invention, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes an acquisition module, a classification module, an evaluation module, and a determination module. For example, the obtaining module may be further described as a module that obtains the item to be evaluated, identifies the business requirement corresponding to the item to be evaluated, and obtains one or more business functions included in the item to be evaluated according to a request of evaluating the workload of the item.
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: acquiring a project to be evaluated according to a request of the workload of the evaluation project, identifying a business requirement corresponding to the project to be evaluated, and acquiring one or more business functions contained in the project to be evaluated; classifying one or more service functions, and determining an existing service function and a newly added service function in the one or more service functions; based on a machine learning algorithm, calculating the workload of the existing service function, and evaluating the workload of the newly added service function according to a service function evaluation template; and determining the workload of the project to be evaluated according to the workload of the existing service function and the workload of the newly added service function.
According to the technical scheme of the embodiment of the invention, the project to be evaluated can be identified into one or more service functions, then the one or more service functions are classified to obtain the existing service functions and the newly added service functions, then the workload of the existing service functions is calculated based on the machine learning algorithm, and the workload of the newly added service functions is evaluated through the service function evaluation template, so that the workload of the project to be evaluated is obtained, the problem of inaccurate evaluation caused by the fact that the existing technology relies on personal experience and capability to evaluate the workload of the project is solved, the workload evaluation efficiency is improved, the workload evaluation period is shortened, the waste of human resources is reduced, and the accuracy of workload evaluation can be improved by applying the machine learning algorithm. In addition, the unified workload evaluation method provided by the embodiment of the invention can provide quantitative data for evaluating the workload, avoids the problem that the approval of a project management department or a financial settlement department is difficult to obtain in project establishment and settlement stages, and has practicability.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (16)

1. A method for project workload assessment, comprising:
acquiring a project to be evaluated according to a request of evaluating project workload, identifying a business requirement corresponding to the project to be evaluated, and acquiring one or more business functions contained in the project to be evaluated;
classifying the one or more service functions, and determining an existing service function and a newly added service function in the one or more service functions;
acquiring one or more target service functions contained in a history item, wherein the existing service function is a service function in the one or more target service functions;
measuring the reference workload of the one or more target service functions by adopting a linear regression model according to the historical workload of the one or more target service functions, the modification frequency corresponding to the one or more target service functions and the proportion of the one or more target service functions in the historical items;
determining the reference workload of the existing business function according to the reference workload of the one or more target business functions;
determining a modification coefficient corresponding to the existing service function according to the modification type corresponding to the existing service function,
substituting the reference workload of the existing service function and the modification coefficient corresponding to the existing service function into an existing service function workload evaluation formula, and calculating the workload of the existing service function;
after filling the relevant information of the newly added service function according to the service function evaluation template, acquiring the relevant information of the newly added service function;
evaluating the newly added service function by using a function point analysis method according to the related information of the newly added service function to obtain the workload of the newly added service function;
and determining the workload of the project to be evaluated according to the workload of the existing service function and the workload of the newly added service function.
2. The method of claim 1, further comprising: updating the one or more target business functions; and the number of the first and second groups,
and updating the reference workload of the one or more target business functions by adopting a linear regression model according to the information corresponding to the one or more target business functions.
3. The method according to claim 1, wherein said determining a modification coefficient corresponding to the existing service function according to the modification type corresponding to the existing service function comprises:
determining a modification type corresponding to the existing service function, wherein the modification type comprises: parameter type modification, service logic modification, data type modification and interface type modification;
and inquiring the modification coefficient corresponding to the existing service function according to the modification type corresponding to the existing service function based on the corresponding relation between the pre-established modification type and the modification coefficient.
4. The method according to claim 3, wherein the correspondence between the modification type and the modification coefficient is established according to the following process: acquiring different modification types, and setting modification coefficients corresponding to the different modification types; and establishing a corresponding relation between the modification types and the modification coefficients according to the different modification types and the modification coefficients corresponding to the different modification types.
5. The method according to any of claims 1 to 4, wherein after determining the existing service function and the added service function of the one or more service functions, the method further comprises:
judging whether the existing service function is modified in the project to be evaluated;
if so, determining that the workload of the existing service function needs to be evaluated;
if not, determining that the workload of the existing service function does not need to be evaluated, and filtering the existing service function.
6. The method of claim 1, further comprising:
during the process of filling the related information of the newly added service function according to the service function evaluation template or after filling the related information of the newly added service function according to the service function evaluation template, checking the filled information by using a preset information checking rule;
and acquiring a verification result, and modifying the filled information according to the verification result to acquire the related information of the newly added service function.
7. The method of claim 6, wherein the business function assessment template comprises: the service function upgrading method comprises the following steps that a service requirement number, a service requirement name, a service function number, a service function name, a logic subsystem related to a service function, a transaction function contained in the service function, a data function contained in the service function, a function type corresponding to the service function and an upgrading type corresponding to the service function are included; and (c) a second step of,
the preset information checking rule comprises the following steps: and dividing the service function into a limited number of transaction functions and data functions and limiting the difficulty coefficients corresponding to the function types.
8. The method according to claim 1, wherein after determining the workload of the item to be evaluated according to the workload of the existing service function and the workload of the newly added service function, the method further comprises:
if the workload of the project to be evaluated is greater than the mean workload of the reference project, monitoring the project to be evaluated; and the number of the first and second groups,
in the implementation process of the project to be evaluated, displaying the implementation progress of the project to be evaluated by using a preset display form according to the workload of the project to be evaluated, and monitoring and early warning the implementation progress of the project to be evaluated, wherein the preset display form comprises: report forms and visual compasses.
9. The method according to claim 1, wherein after determining the workload of the item to be evaluated according to the workload of the existing service function and the workload of the newly added service function, the method further comprises:
and storing the workload of the project to be evaluated, and performing model optimization by using the workload of the project to be evaluated.
10. A project workload assessment apparatus, comprising:
the acquisition module is used for acquiring a project to be evaluated according to a request of the workload of the evaluation project, identifying a business requirement corresponding to the project to be evaluated and acquiring one or more business functions contained in the project to be evaluated;
the classification module is used for classifying the one or more service functions and determining the existing service function and the newly added service function in the one or more service functions;
the evaluation module is used for acquiring one or more target business functions contained in the historical item, wherein the existing business function is a business function in the one or more target business functions;
measuring the reference workload of the one or more target service functions by adopting a linear regression model according to the historical workload of the one or more target service functions, the modification frequency corresponding to the one or more target service functions and the proportion of the one or more target service functions in the historical items;
determining the reference workload of the existing business function according to the reference workload of the one or more target business functions;
determining a modification coefficient corresponding to the existing service function according to the modification type corresponding to the existing service function,
substituting the reference workload of the existing service function and the modification coefficient corresponding to the existing service function into an existing service function workload evaluation formula, and calculating the workload of the existing service function;
after filling the relevant information of the newly added service function according to the service function evaluation template, acquiring the relevant information of the newly added service function;
evaluating the newly added service function by using a function point analysis method according to the related information of the newly added service function to obtain the workload of the newly added service function;
and the determining module is used for determining the workload of the project to be evaluated according to the workload of the existing service function and the workload of the newly added service function.
11. The apparatus of claim 10, wherein the evaluation module is further configured to:
determining a modification type corresponding to the existing service function, wherein the modification type comprises: parameter type modification, service logic modification, data type modification and interface type modification;
and inquiring the modification coefficient corresponding to the existing service function according to the modification type corresponding to the existing service function based on the corresponding relation between the pre-established modification type and the modification coefficient.
12. The apparatus of claim 10, wherein the evaluation module is further configured to:
during the process of filling the related information of the newly added service function according to the service function evaluation template or after filling the related information of the newly added service function according to the service function evaluation template, checking the filled information by using a preset information checking rule;
and acquiring a verification result, and modifying the filled information according to the verification result to acquire the related information of the newly added service function.
13. The apparatus of claim 10, further comprising a monitoring module to: if the workload of the project to be evaluated is greater than the mean workload of the reference project, monitoring the project to be evaluated; and (c) a second step of,
in the implementation process of the project to be evaluated, displaying the implementation progress of the project to be evaluated by using a preset display form according to the workload of the project to be evaluated, and monitoring and early warning the implementation progress of the project to be evaluated, wherein the preset display form comprises: report forms and visual compasses.
14. An electronic device, comprising:
one or more processors;
a storage device to store one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-9.
15. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-9.
16. A computer program product comprising a computer program, characterized in that the computer program realizes the method according to any of claims 1-9 when executed by a processor.
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