Software research and development resource dynamic allocation optimization method and visual analysis tool

Abstract

The invention provides a software research and development resource dynamic allocation optimization method and a visual analysis tool, and belongs to the technical field of data processing, and the method specifically comprises the steps: determining a monitoring target in research and development personnel according to the distribution data of the research and development personnel with different analysis demand types in different associated research and development projects, according to distribution data of monitoring targets in different research and development projects and composition data of research and development personnel, monitoring processing strategies of the different monitoring targets in the research and development projects are determined, and whether software research and development resource dynamic allocation optimization processing needs to be carried out or not is determined based on monitoring data of the monitoring targets in the different research and development projects. And the reliability of monitoring and processing the defect discovery rate in the development process of the software project is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and particularly relates to a method for dynamically allocating and optimizing software R & D resources and a visualization analysis tool. Background Art

[0002] During the software development and processing, it often involves subtasks in multiple dimensions. Since the development progress between different subtasks is not interconnected, managers often cannot accurately control the software development progress, and thus cannot carry out targeted optimization scheduling of software R & D resources to improve the efficiency of software development and processing.

[0003] Therefore, to solve the above technical problems, in the patent application for invention CN202411505922.8, "A Software R & D Digital Supervision System and Method Based on a Cloud Platform", through the construction of a cloud platform, quantitative supervision of the development process is realized, the R & D efficiency is improved, the R & D cost is reduced at the same time, the input-output ratio is maximized, the return rate of software R & D is increased, and the efficiency and quality of software development are improved. However, there are the following technical problems: In the process of visual analysis and processing of data, the defect discovery rate needs to be verified by manual review for different R & D personnel. Since the number of different R & D personnel is large and there are deviations in the associated R & D projects of different R & D personnel, the impact degree of differences in data dimensions such as the defect discovery rate on the entire R & D progress is different. Therefore, how to determine the monitoring target of the defect discovery rate of different R & D projects according to the association between different R & D personnel and R & D projects, while reducing the difficulty of monitoring and analysis processing, and also meeting the requirements of optimizing the scheduling of software R & D resources has become a technical problem to be solved urgently.

[0004] To solve the above technical problems, the present application provides a method for dynamically allocating and optimizing software R & D resources and a visualization analysis tool. Summary of the Invention

[0005] To achieve the object of the present invention, the present invention adopts the following technical solutions: Specifically, the present application provides a method for dynamically allocating and optimizing software R & D resources, which specifically includes: S1 Determine the associated R & D projects of R & D personnel, and determine the correlation coefficients of test cases between different associated R & D projects and other R & D projects according to the requirement data of test cases of different associated R & D projects and other R & D projects; S2 Use the correlation coefficients of test cases between different associated R & D projects and other R & D projects, and combine the software defect identification data in different R & D projects to determine the analysis requirement types of the R & D personnel in different associated R & D projects; S3 determines the monitoring targets among the R & D personnel according to the distribution data of R & D personnel with different types of analysis requirements in different associated R & D projects; S4 determines the monitoring processing strategies of different monitoring targets in R & D projects based on the distribution data of monitoring targets and the composition data of R & D personnel in different R & D projects, and determines whether software R & D resource dynamic allocation optimization processing is required based on the monitoring data of monitoring targets in different R & D projects.

[0006] The beneficial effects of the present invention are as follows: Based on the correlation coefficients of test cases between different associated R & D projects and other R & D projects, and the software defect identification data in different R & D projects, the analysis requirement types of R & D personnel in different associated R & D projects are determined. Considering the differences in the feasibility of optimizing the scheduling of R & D personnel to other R & D projects due to the differences in the similarity degree of test cases between associated R & D projects and other R & D projects, the evaluation processing of the generality of test experience in different associated R & D projects is realized, and thus it also lays a foundation for determining monitoring targets according to the feasibility of scheduling R & D personnel to other R & D projects.

[0007] Based on the distribution data of monitoring targets and the composition data of R & D personnel in different R & D projects, the monitoring processing strategies of different monitoring targets in R & D projects are determined, which not only avoids the technical problem of excessive difficulty in monitoring the defect discovery rate caused by monitoring monitoring targets in all R & D projects, but also further combines the distribution data of monitoring targets and the composition data of R & D personnel, and also ensures the reliability of the identification processing of the defect discovery rate of R & D projects with a small number of distributed monitoring targets.

[0008] A further technical solution is that the associated R & D project is an R & D project participated by the R & D personnel.

[0009] A further technical solution is that the requirement data of the test cases includes requirement items in three dimensions: the number of function points to be tested, the types of input and output data, the number of performance test requirements, and the requirement values.

[0010] A further technical solution is that the method for determining the correlation coefficient of test cases between the associated R & D project and other R & D projects is as follows: Based on the requirement data of the test cases between the associated R & D project and other R & D projects, the deviation data in requirement items in different dimensions is determined; Based on the deviation data, the deviation quantity of the test cases in different requirement items is determined, and based on the deviation quantity, the similar test cases in the test cases are determined; Determine the correlation coefficient of test cases between the associated R & D project and other R & D projects based on the proportion of the similar test cases in the test cases of other R & D projects.

[0011] A further technical solution is that the similar test cases are test cases in which the deviation quantities in different requirement items are all within a preset deviation quantity range.

[0012] A further technical solution is that the method for determining the monitoring and processing strategy of the monitoring target in the R & D project is as follows: Take the R & D project with the monitoring target as the analysis target project, and determine the proportion of the monitoring target in different analysis target projects based on the proportion of the number of the monitoring target in the R & D personnel in the analysis target project; Take the analysis target project with the proportion of the monitoring target less than the preset proportion threshold or the proportion of the monitoring target of a certain type of requirement type less than the preset target proportion threshold as the monitoring deviation project; According to the number of monitoring deviation projects and the proportion of the monitoring target in different analysis target projects, determine the monitoring and processing strategy of the monitoring target in different analysis target projects.

[0013] A further technical solution is that according to the number of monitoring deviation projects and the proportion of the monitoring target in different analysis target projects, determine the monitoring and processing strategy of the monitoring target in different analysis target projects, which specifically includes: When the number of monitoring deviation projects is greater than the preset monitoring project quantity threshold, only the defect identification rate needs to be identified and processed in the monitoring deviation projects; When the number of the monitoring deviation projects is not greater than the preset monitoring project quantity threshold, determine the analysis target projects that need to be identified and processed for the defect identification rate based on the preset quantity and the proportion of the monitoring target in different analysis target projects.

[0014] A further technical solution is that determine whether software R & D resource dynamic allocation optimization processing is required, which specifically includes: Based on the monitoring data of the monitoring target in different R & D projects, determine the defect discovery rate of different monitoring targets in different R & D projects, and take the minimum value of the defect discovery rate in different R & D projects as the monitoring discovery rate of different monitoring targets; Based on the monitoring discovery rate of the monitoring target in different R & D projects, determine whether software R & D resource dynamic allocation optimization processing is required.

[0015] A further technical solution is that based on the monitoring discovery rate of the monitoring target in different R & D projects, determine whether software R & D resource dynamic allocation optimization processing is required, which specifically includes: Take the monitoring target with a monitoring discovery rate less than the preset discovery rate threshold as an abnormal monitoring target; When there is a research and development project in which the proportion of the number of abnormal monitoring targets among R & D personnel is greater than the preset monitoring target proportion threshold, it is determined that dynamic allocation optimization processing of software R & D resources needs to be carried out.

[0016] In a second aspect, the present invention provides a visualization analysis tool, which adopts the above-mentioned software R & D resource dynamic allocation optimization method, specifically including: A research and development project division module, a monitoring target determination module, and a monitoring processing module; Among them, the research and development project division module is responsible for dividing the R & D personnel in different research and development projects; The monitoring target determination module is responsible for determining the monitoring targets in different research and development projects; The monitoring processing module is responsible for determining the monitoring processing of the defect discovery rates of different monitoring targets.

[0017] Other features and advantages will be described in the subsequent specification. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification and the drawings.

[0018] To make the above-mentioned objectives, features, and advantages of the present invention more obvious and understandable, the following specifically gives preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] By referring to the accompanying drawings and describing its exemplary embodiments in detail, the above and other features and advantages of the present invention will become more obvious.

[0020] Figure 1 is a flowchart of a software R & D resource dynamic allocation optimization method; Figure 2 is a flowchart of a method for determining the correlation coefficient of test cases between an associated R & D project and other R & D projects; Figure 3 is a flowchart of a method for determining the analysis requirement type of R & D personnel in the associated R & D project; Figure 4 is a flowchart of a method for determining the monitoring targets among R & D personnel; Figure 5 is a framework diagram of a visualization analysis tool. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] To enable those skilled in the art to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this specification.

[0022] In this application, according to the similarity of test cases in related R & D projects of R & D personnel with other R & D projects, and the number of R & D personnel in different related R & D projects, the related R & D projects for defect discovery rate monitoring are determined, thereby improving the efficiency of software defect rate monitoring and processing.

[0023] The defect discovery rate is the ratio of the number of software defects discovered by R & D personnel during testing to the number of software defects discovered during review processing.

[0024] The correlation coefficient of test cases between a related R & D project and other R & D projects: It is determined according to the ratio of the number of test cases in the related R & D project where the requirements items are the same as those in other R & D projects to the number of test cases in other R & D projects.

[0025] The analysis requirement types include a first type of requirement type and a second type of requirement type. Among them, the first type of requirement type is other R & D projects with a correlation coefficient greater than 0.5 or more than 5, or the average value of the number of software defect identifications in other R & D projects with a correlation coefficient greater than 0.5 is less than the preset number of R & D personnel.

[0026] Take R & D personnel with more than 3 related R & D projects of the first type of requirement type as the monitoring target.

[0027] In R & D projects where the ratio of the monitoring target to the R & D personnel is less than 0.1, monitor the defect discovery rate of the monitoring target.

[0028] Embodiment 1 As Figure 1 shown, this application provides a method for optimizing the dynamic allocation of software R & D resources, which specifically includes: S1 Determine the related R & D projects of R & D personnel, and determine the correlation coefficient of test cases between different related R & D projects and other R & D projects according to the requirement data of test cases of different related R & D projects and other R & D projects; Further, the related R & D project is the R & D project participated by the R & D personnel.

[0029] Specifically, the requirement data of the test cases includes requirement items in three dimensions: the number of function points to be tested, the types of input and output data, the number of performance test requirements, and the requirement values.

[0030] It should be noted that, as Figure 2 shown, the method for determining the correlation coefficient of test cases between the associated R & D project and other R & D projects is as follows: Based on the requirement data of the test cases between the associated R & D project and other R & D projects, determine the deviation data of requirement items in different dimensions; Based on the deviation data, determine the number of deviations of the test cases in different requirement items, and based on the number of deviations, determine the similar test cases in the test cases; Based on the proportion of the similar test cases in the test cases of other R & D projects, determine the correlation coefficient of the test cases between the associated R & D project and other R & D projects.

[0031] Further, the similar test cases are test cases where the number of deviations in different requirement items is within a preset deviation number range.

[0032] In another possible embodiment, the method for determining the correlation coefficient of test cases between the associated R & D project and other R & D projects is as follows: Based on the requirement data of the test cases between the associated R & D project and other R & D projects, determine the test cases where the requirement items in different dimensions are all the same; Take the test cases where the requirement items in different dimensions are all the same as the same test cases; Based on the proportion of the same test cases in the test cases of other R & D projects, determine the correlation coefficient of the test cases between the associated R & D project and other R & D projects.

[0033] S2 Determine the analysis requirement types of the R & D personnel in different associated R & D projects based on the correlation coefficients of test cases between different associated R & D projects and other R & D projects, and in combination with the software defect identification data in different R & D projects; Further, the software defect identification data in the R & D project includes the number of identified software defects in the R & D project and the code volume of the software code.

[0034] Specifically, as Figure 3 shown, the method for determining the analysis requirement types of the R & D personnel in the associated R & D project is as follows: Based on the correlation coefficient of the test cases between the associated R & D project and other R & D projects, determine other R & D projects with a correlation coefficient greater than the preset correlation coefficient threshold, and take them as similar R & D projects; Determine the number of identified software defects and the amount of code in the software code in different similar R & D projects based on the software defect identification data in the similar R & D projects; Based on the number of identified software defects and the amount of code in the software code in different similar R & D projects, determine the type of analysis requirements of the R & D personnel in the associated R & D project.

[0035] Further, based on the number of identified software defects and the amount of code in the software code in different similar R & D projects, determining the type of analysis requirements of the R & D personnel in the associated R & D project specifically includes: Use the ratio of the number of identified software defects to the amount of code in the software code in different similar R & D projects to determine the defect identification coefficient of different similar R & D projects; Take the sum of the defect identification coefficients of different similar R & D projects as the identification requirement value. When the identification requirement value is less than the preset identification requirement threshold, it is determined that the type of analysis requirements of the R & D personnel in the associated R & D project is a type I requirement type. When the identification requirement value is not less than the preset identification requirement threshold, it is determined that the type of analysis requirements of the R & D personnel in the associated R & D project is a type II requirement type.

[0036] It should be noted that the type I requirement type is greater than the type II requirement type.

[0037] Table 1. Classification table of the requirement types of a certain R & D personnel in different R & D projects

[0038] In another possible embodiment, the method for determining the type of analysis requirements of the R & D personnel in the associated R & D project is: S21 Use the correlation coefficient of test cases between the associated R & D project and other R & D projects to determine other R & D projects with a correlation coefficient greater than the preset correlation coefficient threshold, and use them as similar R & D projects; It should be noted that before proceeding to step S22, it is also necessary to determine whether there are similar R & D projects for the associated R & D project, whether the number of similar R & D projects meets the requirements, and whether the sum of the correlation coefficients between different similar R & D projects and the associated R & D project meets the requirements.

[0039] It is understandable that when there is no similar R & D project, the type of analysis requirement in the associated R & D project is directly determined as the type II requirement. When there is a similar R & D project, if both the number of similar R & D projects and the sum of the correlation coefficients between different similar R & D projects and the associated R & D project do not meet the requirements, that is, both are less than the corresponding thresholds, then the type of analysis requirement in the associated R & D project is directly determined as the type II requirement.

[0040] Specifically, when either the number of similar R & D projects or the sum of the correlation coefficients between different similar R & D projects and the associated R & D project meets the requirements, it is also necessary to determine that when the number of similar R & D projects is greater than the threshold of the number of similar R & D projects or the sum of the correlation coefficients between different similar R & D projects and the associated R & D project is greater than the preset coefficient threshold, then the type of analysis requirement in the associated R & D project is directly determined as the type I requirement. Otherwise, it proceeds to step S22.

[0041] S22 determines the number of software defect identifications and the code volume of software code in different similar R & D projects based on the software defect identification data in the similar R & D projects, and determines the defect identification coefficients of different similar R & D projects using the number of software defect identifications and the code volume of software code in different similar R & D projects; It should be further noted that the above step S22 also includes the following content: When there are multiple similar R & D projects with the number of software defect identifications less than the preset identification number threshold, then the type of analysis requirement in the associated R & D project is directly determined as the type I requirement. Otherwise, the defect identification coefficients of different similar R & D projects are further determined using the number of software defect identifications and the code volume of software code in different similar R & D projects; When there are multiple similar R & D projects with defect identification coefficients less than the preset defect identification coefficient threshold, then the type of analysis requirement in the associated R & D project is directly determined as the type I requirement. Otherwise, it is also necessary to determine whether the sum of the defect identification coefficients of different similar R & D projects meets the requirements; When the sum of the defect identification coefficients of different similar R & D projects is less than the preset identification requirement threshold, then the type of analysis requirement in the associated R & D project is directly determined as the type I requirement. When and only when the sum of the defect identification coefficients of different similar R & D projects is not less than the preset identification requirement threshold, it proceeds to the next step.

[0042] S23 determines the scheduling requirement value of the R & D personnel based on the defect identification coefficients in different similar R & D projects and in combination with the correlation coefficients of test cases between different similar R & D projects, and determines the type of analysis requirement of the R & D personnel in the associated R & D project based on the scheduling requirement value.

[0043] It is understandable that the scheduling requirement value of R & D personnel can be determined according to the sum of the products of the defect identification coefficient and the correlation coefficient in different similar R & D projects. When the scheduling requirement value is less than the preset scheduling requirement threshold, it is determined that the analysis requirement type in the associated R & D project is a type I requirement type; otherwise, it is a type II requirement type.

[0044] S3 Determine the monitoring target among the R & D personnel according to the distribution data of R & D personnel with different analysis requirement types in different associated R & D projects; Specifically, as Figure 4 shown, the method for determining the monitoring target among the R & D personnel is as follows: Based on the distribution data of R & D personnel with different analysis requirement types in different associated R & D projects, determine the associated R & D projects in which the R & D personnel belong to the type I requirement type, and use them as type I R & D projects; According to the number of R & D personnel with the type I requirement type in different type I R & D projects, determine the matching coefficient in different type I R & D projects; Based on the matching coefficient in different type I R & D projects, determine whether the R & D personnel is the monitoring target.

[0045] Furthermore, the matching coefficient is determined according to the reciprocal of the number of R & D personnel with the type I requirement type in the type I R & D project.

[0046] It should be noted that when the sum of the matching coefficients in different type I R & D projects is greater than the preset matching coefficient threshold, it is determined that the R & D personnel is the monitoring target.

[0047] S4 Based on the distribution data of monitoring targets and the composition data of R & D personnel in different R & D projects, determine the monitoring processing strategy of different monitoring targets in the R & D project, and determine whether software R & D resource dynamic allocation optimization processing is required based on the monitoring data of the monitoring targets in different R & D projects.

[0048] Specifically, the method for determining the monitoring processing strategy of the monitoring target in the R & D project is as follows: Take the R & D project with the monitoring target as the analysis target project, and based on the proportion of the number of monitoring targets among the R & D personnel in the analysis target project, determine the proportion of monitoring targets in different analysis target projects; Take the analysis target project with the monitoring target proportion less than the preset proportion threshold or the proportion of monitoring targets of the type I requirement type less than the preset target proportion threshold as the monitoring deviation project; Determine the monitoring and processing strategies for the monitoring targets in different analysis target items according to the number of monitoring deviation items and the proportion of monitoring targets in different analysis target items.

[0049] Furthermore, determine the monitoring and processing strategies for the monitoring targets in different analysis target items according to the number of monitoring deviation items and the proportion of monitoring targets in different analysis target items. Specifically, it includes: When the number of monitoring deviation items is greater than the preset monitoring item quantity threshold, only the defect identification rate needs to be identified and processed in the monitoring deviation items; When the number of the monitoring deviation items is not greater than the preset monitoring item quantity threshold, determine the analysis target items that need to perform the identification and processing of the defect identification rate according to the preset quantity and the proportion of monitoring targets in different analysis target items.

[0050] It can be understood that determining the analysis target items that need to perform the identification and processing of the defect identification rate according to the preset quantity and the proportion of monitoring targets in different analysis target items specifically includes: Take the difference between the preset quantity and the number of monitoring deviation items as the target quantity, and take the analysis target item with the smallest proportion of monitoring targets and the target quantity as the analysis target item that needs to perform the identification and processing of the defect identification rate.

[0051] In another possible embodiment, the method for determining the monitoring and processing strategy of the monitoring target in the R & D project is: Take the R & D project with the monitoring target as the analysis target item; It can be understood that before proceeding to the next step, it is also necessary to further determine whether the number of analysis target items is too small. Specifically, when the number of analysis target items is too small, that is, less than a fixed threshold, in order to achieve reliable monitoring of the monitoring target, and at the same time when the number of analysis target items is too small, the defect discovery rate of the monitoring target needs to be monitored and processed in different analysis target items; In addition, even if the number of analysis target items is not too small, if the number of analysis target items is greater than the preset analysis target item quantity threshold, since the abnormal defect discovery rate will affect the development of the entire software system, it can be directly determined to screen the second preset quantity of analysis target items in ascending order according to the number of monitoring targets according to the second preset quantity, and take them as the analysis target items that need to monitor the defect discovery rate of the monitoring target. Only when the number of analysis target items is not greater than the preset analysis target item quantity threshold, proceed to the next step and continue to determine the monitoring deviation values of different analysis target items.

[0052] Determine the monitoring deviation value of different analysis target projects according to the proportion of the number of monitoring targets in the analysis target projects among the R & D personnel, and in combination with the number of monitoring targets in different analysis target projects, the number of analysis target projects of different monitoring targets, and the type of analysis requirements. It can be understood that in the above steps, if it is determined that the proportion of the number of monitoring targets in different analysis target projects among the R & D personnel is small, that is, less than a fixed threshold, then it is necessary to monitor the defect discovery rate in different analysis target projects. Among them, the monitoring deviation value of the analysis target project is determined by using a mathematical model based on the analytic hierarchy process with the proportion of the number of monitoring targets in the analysis target project among the R & D personnel, the number of monitoring targets in different analysis target projects, the number of analysis target projects of different monitoring targets, and the type of analysis requirements as input quantities.

[0053] In addition, it should be further explained that even if the proportion of the number of R & D personnel is not uniformly small, it is necessary to further determine whether there are analysis target projects with monitoring deviation values meeting the requirements. When the monitoring deviation values of different analysis target projects do not meet the requirements, that is, on the basis that the monitoring deviation value is greater than the preset threshold, it is necessary to monitor the defect discovery rate of the monitoring targets in different analysis target projects. In addition, even if not all the monitoring deviation values of the analysis target projects do not meet the requirements, then first monitor the defect discovery rate of the monitoring targets in the analysis target projects where the monitoring deviation values do not meet the requirements, and continue to transfer to the next step to determine the monitoring processing strategy of the monitoring targets.

[0054] Based on the monitoring deviation values of different analysis target projects, determine the monitoring processing strategy of the monitoring targets in different analysis target projects.

[0055] It can be understood that when the number of analysis target projects with monitoring deviation values not meeting the requirements is less than the preset analysis project quantity threshold, then monitor the defect discovery rate in all analysis target projects where the monitoring deviation value is greater than the preset deviation threshold. When the number of analysis target projects with monitoring deviation values not meeting the requirements is not less than the preset analysis project quantity threshold, then only need to monitor the defect discovery rate in the analysis target projects where the monitoring deviation values do not meet the requirements.

[0056] Specifically, determine whether software R & D resource dynamic allocation optimization processing is required, which specifically includes: Based on the monitoring data of the monitoring targets in different R & D projects, determine the defect discovery rate of different monitoring targets in different R & D projects, and use the minimum value of the defect discovery rate in different R & D projects as the monitoring discovery rate of different monitoring targets. Determine whether software R & D resource dynamic allocation optimization processing is required according to the monitoring discovery rate of monitoring targets in different R & D projects.

[0057] Furthermore, determining whether software R & D resource dynamic allocation optimization processing is required according to the monitoring discovery rate of monitoring targets in different R & D projects specifically includes: Regarding the monitoring targets with a monitoring discovery rate less than the preset discovery rate threshold as abnormal monitoring targets; When there is a R & D project in which the proportion of the number of abnormal monitoring targets among R & D personnel is greater than the preset monitoring target proportion threshold, it is determined that software R & D resource dynamic allocation optimization processing is required.

[0058] The defect discovery rate is the ratio of the number of software defects discovered by R & D personnel during testing to the number of software defects discovered during review processing. The severe defect discovery rate is the ratio of the number of severe software defects discovered by R & D personnel during the testing process to the number of severe software defects discovered during review processing. Severe software defects are determined according to the definition within the enterprise.

[0059] Table 2. Data of the defect discovery rate of R & D personnel in a certain R & D project

[0060] Embodiment 2 In a second aspect, as Figure 5 shown, the present invention provides a visualization analysis tool, which adopts the above-mentioned software R & D resource dynamic allocation optimization method, specifically including: An R & D project division module, a monitoring target determination module, and a monitoring processing module; Among them, the R & D project division module is responsible for dividing R & D personnel in different R & D projects; The monitoring target determination module is responsible for determining the monitoring targets in different R & D projects; The monitoring processing module is responsible for determining the monitoring processing of the defect discovery rate of different monitoring targets.

[0061] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the embodiments of the device, equipment, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and for the relevant parts, reference can be made to the partial description of the method embodiments.

[0062] The specific embodiments of the present specification have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0063] The above description is only for one or more embodiments of the present specification and is not intended to limit the present specification. For those skilled in the art, various changes and modifications can be made to one or more embodiments of the present specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of one or more embodiments of the present specification shall be included within the scope of the claims of the present specification.

Claims

1. A method for optimizing the dynamic allocation of software R & D resources, characterized in that, Specifically, it includes: Determine the associated R & D projects of the R & D personnel, and determine the correlation coefficient of test cases between the associated R & D projects and other R & D projects according to the requirement data of test cases of different associated R & D projects and other R & D projects; Determine the analysis requirement types of the R & D personnel in different associated R & D projects with the correlation coefficient of test cases between different associated R & D projects and other R & D projects, and in combination with the software defect identification data in different R & D projects; Determine the monitoring targets among the R & D personnel according to the distribution data of R & D personnel with different analysis requirement types in different associated R & D projects; Determine the monitoring processing strategies of different monitoring targets in R & D projects with the distribution data of monitoring targets and the composition data of R & D personnel in different R & D projects, and determine whether software R & D resource dynamic allocation optimization processing is required based on the monitoring data of monitoring targets in different R & D projects.

2. The software R & D resource dynamic allocation optimization method according to claim 1, characterized in that The associated R & D project is the R & D project participated by the R & D personnel.

3. The software R & D resource dynamic allocation optimization method according to claim 1, characterized in that The requirement data of the test cases includes requirement items in three dimensions: the number of function points to be tested, the type of input and output data, the number of performance test requirements, and the requirement value.

4. The software R & D resource dynamic allocation optimization method according to claim 1, characterized in that The method for determining the correlation coefficient of test cases between the associated R & D project and other R & D projects is as follows: Determine the deviation data of requirement items in different dimensions with the requirement data of test cases between the associated R & D project and other R & D projects; Based on the deviation data, determine the deviation quantity of test cases in different requirement items, and determine the similar test cases in the test cases based on the deviation quantity; Determine the correlation coefficient of test cases between the associated R & D project and other R & D projects based on the proportion of the similar test cases in the test cases of other R & D projects.

5. The software R & D resource dynamic allocation optimization method according to claim 4, characterized in that The similar test cases are test cases whose deviation quantities in different requirement items are all within the preset deviation quantity range.

6. The software R & D resource dynamic allocation optimization method according to claim 1, characterized in that The software defect identification data in the R & D project includes the identification quantity of software defects and the code volume of software code in the R & D project.

7. The software R & D resource dynamic allocation optimization method according to claim 1, characterized in that, The method for determining the monitoring processing strategy of the monitoring target in the R & D project is as follows: Take the R & D project with the monitoring target as the analysis target project, and determine the proportion of the monitoring target in different analysis target projects based on the proportion of the number of the monitoring target in the R & D personnel in the analysis target project; Take the analysis target project with the proportion of the monitoring target less than the preset proportion threshold or the proportion of the monitoring target of a certain type of requirement type less than the preset target proportion threshold as the monitoring deviation project; Determine the monitoring processing strategy of the monitoring target in different analysis target projects according to the number of monitoring deviation projects and the proportion of the monitoring target in different analysis target projects.

8. The software R & D resource dynamic allocation optimization method according to claim 7, wherein Determine the monitoring processing strategy of the monitoring target in different analysis target projects according to the number of monitoring deviation projects and the proportion of the monitoring target in different analysis target projects, specifically including: When the number of monitoring deviation projects is greater than the preset monitoring project quantity threshold, only the defect identification rate identification processing needs to be carried out in the monitoring deviation projects; When the number of the monitored deviation items is not greater than the preset monitoring item quantity threshold, the analysis target items for which the defect identification rate needs to be identified are determined according to the preset quantity and the proportion of the monitoring targets in different analysis target items.

9. The software R & D resource dynamic allocation optimization method according to claim 8, characterized in that, Determining the analysis target items for which the defect identification rate needs to be identified according to the preset quantity and the proportion of the monitoring targets in different analysis target items specifically includes: Taking the difference between the preset quantity and the number of the monitored deviation items as the target quantity, and taking the analysis target item with the smallest proportion of the monitoring target among the target quantities as the analysis target item for which the defect identification rate needs to be identified.

10. A visualization analysis tool, characterized in that, Adopting a software R & D resource dynamic allocation optimization method according to any one of claims 1-9, specifically including: A R & D project division module, a monitoring target determination module, and a monitoring processing module; Wherein the R & D project division module is responsible for dividing the R & D personnel in different R & D projects; The monitoring target determination module is responsible for determining the monitoring targets in different R & D projects; The monitoring processing module is responsible for determining the monitoring processing of the defect discovery rate of different monitoring targets.

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