CN112068806B - Method and system for optimizing project management in software development - Google Patents
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Abstract
The invention provides a method and a system for optimizing project management in software development, which comprises the following steps: step M1: inputting function points related to project iteration and corresponding development requirements; step M2: detecting missing code change points in development requirements by using an Apriori algorithm and intelligently reminding; step M3: clustering the development requirements with the association degree reaching a preset state by using a K-means algorithm to output development plan arrangement; step M4: confirming and implementing development plan arrangement, and evaluating the development plan arrangement and the omitted code change points; step M5: the Apriori algorithm and the K-means algorithm parameters are adjusted based on evaluations of development planning and missed code transition points. According to the invention, the K-means algorithm is adopted for intelligent clustering of development requirements, and an objective development plan is given by combining the algorithm with historical data, so that the problem that project delivery timeliness is influenced because project managers and development responsible persons are difficult to agree on project schedule is solved.
Description
Technical Field
The invention relates to the technical field of software development, in particular to a method and a system for optimizing project management in software development, and more particularly to a method for optimizing project management in software development based on K-means and Apriori algorithms.
Background
A general software development team carries out requirement management and task scheduling by virtue of project management software such as Zen channel and JIRA. Usually, the project management software provides functions of demand record tracking, collaborative editing and the like, but the final management of demands is still a project manager, and although people can make reasonable arrangement thought by the people according to past experience, if the demands are staggered and complicated, the people can hardly make objective and reasonable arrangement by the people alone;
currently popular static code scanning tools such as Lint and Fortify are often used for solving whether the codes have syntax errors or security holes. However, in actual development, the absence of a bug does not mean that a program can be executed as expected, for example, a developer may omit development of a certain function point, and for this problem, the current development team often puts this check back to a test verification link, and if we can monitor code omission in the development stage, our project management capability can be improved certainly.
Patent document CN107291448A (application number: 201710358547.2) discloses a software development project management system, which belongs to the technical field of software development; the system comprises a plurality of clients, a plurality of project service terminals respectively connected with each client, and a plurality of database service terminals respectively connected with each client; each client comprises a code auxiliary management component, and the code auxiliary management component comprises a branch management unit, an item storage unit, a version number editing unit and a dependency check unit. The beneficial effects of the above technical scheme are: the method has the advantages that the repetitive operation in the project development process is reduced, the operation time of project members and the subsequent maintenance and management cost are saved, the work efficiency of project developers is improved, and the project development quality is improved to a certain extent.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method and a system for optimizing project management in software development.
The method for optimizing project management in software development provided by the invention comprises the following steps:
step M1: inputting function points related to project iteration and corresponding development requirements;
step M2: detecting missing code change points in the development requirements by using an Apriori algorithm and making corresponding prompts;
step M3: clustering the development requirements with the association degree reaching a preset state by using a K-means algorithm to output development plan arrangement;
step M4: confirming and implementing development plan arrangement, and evaluating the development plan arrangement and the omitted code change points;
step M5: the Apriori algorithm and the K-means algorithm parameters are adjusted based on evaluations of development planning and missed code transition points.
Specifically, the step M2 includes:
step M2.1: constructing a development requirement-change code module relation data set;
step M2.2: finding out a frequent item set of a change code module in a development demand-change code module relation data set through an Apriori algorithm;
step M2.3: and traversing the development requirement and changing the frequent item set of the code module in a two-layer cycle manner, and outputting the missing code change points.
In particular, said step M2.1 comprises: the development requirement is associated with the change of function level code modules in different logic levels in a software architecture, and a development requirement-change code module relation data set is constructed by adding a code static scanning step in the project development.
In particular, said step M2.2 comprises: setting initial values of a support degree parameter threshold value and a reliability degree parameter threshold value as preset values, and triggering an Apriori algorithm to find out a frequent item set of a change code module;
and the support degree parameter threshold value and the credibility parameter threshold value are dynamically adjusted according to the data volume in the current data set and the accuracy of prediction by an Apriori algorithm, and when the total data volume reaches a preset value or the predicted misinformation reaches a preset value, the support degree parameter threshold value is improved, the calculation timeliness is accelerated, and the misinformation is reduced.
In particular, said step M2.3 comprises: the method comprises the steps that the coverage rate of a change code module in the change code module frequent item set in the change code module coverage development requirements reaches above a preset value, at least one change code module which does not exist in the development requirements is included, the association rule of the change code module union set is found out through an Apriori algorithm, the change of the code modules which do not belong to the union set in the association rule is transmitted to developers to confirm information, and the omitted code change points are obtained.
Specifically, the step M3 includes:
step M3.1: generating initial three-dimensional data for each development requirement;
step M3.2: carrying out the most value normalization on the three-dimensional data;
step M3.3: and outputting the clustering by using a K-means algorithm according to the value after the most value normalization.
Specifically, the three-dimensional data in step M3.1 includes: development time, dependences and support function points;
the development time is in hours, and corresponding scores are obtained according to a preset rule;
ranking the depended-on according to the times of the development requirements being depended on by other development requirements, and obtaining corresponding scores according to a preset rule by ranking;
the support function points correspond to different discrete value scores for different function points, and the score corresponding to the current function point is given to the function point service according to development requirements.
In particular, said step M3.2 comprises:
wherein x isscaleRepresenting the value after the most value normalization; x represents the original value; x is the number ofminRepresents the minimum value in any one-dimensional data set; x is the number ofmaxRepresenting the maximum value in any one-dimensional data.
Specifically, the step M5 includes adjusting the support degree parameter threshold and the reliability degree parameter threshold in Apriori algorithm and the distance calculation function parameter and the clustering method in K-means algorithm according to the evaluation of the development planning and the missing code change point.
The invention provides a system for optimizing project management in software development, which comprises:
module M1: inputting function points related to project iteration and corresponding development requirements;
module M2: detecting missing code change points in development requirements by using an Apriori algorithm and intelligently reminding;
module M3: clustering the development requirements with the association degree reaching a preset state by using a K-means algorithm to output development plan arrangement;
module M4: confirming and implementing development plan arrangement, and evaluating the development plan arrangement and the omitted code change points;
module M5: the Apriori algorithm and the K-means algorithm parameters are adjusted based on evaluations of development planning and missed code transition points.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, the K-means algorithm is adopted for intelligent clustering of development requirements, and an objective development plan is given by combining the algorithm with historical data, so that the problem that project delivery timeliness is influenced because project managers and development responsible persons are difficult to agree on project schedule is solved;
2. the method and the device have the advantages that the Apriori algorithm is adopted to intelligently detect the code change which is possibly lost in the development requirement, the algorithm is combined with historical data to monitor the development problem which is possibly existed in the development stage, and the problem that the function loss is found in the on-line stage of the project so that the delivery timeliness of the project is influenced is favorably reduced.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic diagram of the system operation for optimizing project management in software development;
FIG. 2 is a flow chart of development planning using the K-means algorithm;
FIG. 3 is a flow chart of a method for optimizing project management in software development.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Example 1
The method for optimizing project management in software development provided by the invention comprises the following steps:
step M1: inputting function points related to project iteration and corresponding development requirements;
step M2: detecting missing code change points in the development requirements by using an Apriori algorithm and making corresponding prompts;
step M3: clustering the development requirements with the association degree reaching a preset state by using a K-means algorithm to output development plan arrangement;
step M4: confirming and implementing development plan arrangement, and evaluating the development plan arrangement and the omitted code change points;
step M5: the Apriori algorithm and the K-means algorithm parameters are adjusted based on evaluations of development planning and missed code transition points.
Specifically, the step M2 includes:
step M2.1: constructing a development requirement-change code module relation data set;
step M2.2: finding out a frequent item set of a change code module in a development demand-change code module relation data set through an Apriori algorithm;
step M2.3: and traversing the development requirement and changing the frequent item set of the code module in a two-layer cycle manner, and outputting the missing code change points.
In particular, said step M2.1 comprises: the development requirement is associated with the change of function level code modules in different logic levels in a software architecture, and a development requirement-change code module relation data set is constructed by adding a code static scanning step in the project development.
In particular, said step M2.2 comprises: setting initial values of a support degree parameter threshold value and a reliability degree parameter threshold value as preset values, and triggering an Apriori algorithm to find out a frequent item set of a change code module;
and the support degree parameter threshold value and the credibility parameter threshold value are dynamically adjusted according to the data volume in the current data set and the accuracy of prediction by an Apriori algorithm, and when the total data volume reaches a preset value or the predicted misinformation reaches a preset value, the support degree parameter threshold value is improved, the calculation timeliness is accelerated, and the misinformation is reduced.
In particular, said step M2.3 comprises: the method comprises the steps that the coverage rate of a change code module in the change code module frequent item set in the change code module coverage development requirements reaches above a preset value, at least one change code module which does not exist in the development requirements is included, the association rule of the change code module union set is found out through an Apriori algorithm, the change of the code modules which do not belong to the union set in the association rule is transmitted to developers to confirm information, and the omitted code change points are obtained.
Specifically, the step M3 includes, as shown in fig. 2:
step M3.1: generating initial three-dimensional data for each development requirement;
step M3.2: carrying out the most value normalization on the three-dimensional data;
step M3.3: and outputting the clustering by using a K-means algorithm according to the value after the most value normalization.
Specifically, the three-dimensional data in step M3.1 includes: development time, dependences and support function points;
the development time is in hours, and corresponding scores are obtained according to a preset rule;
ranking the depended-on according to the times of the development requirements being depended on by other development requirements, and obtaining corresponding scores according to a preset rule by ranking;
the support function points correspond to different discrete value scores for different function points, and the score corresponding to the current function point is given to the function point service according to development requirements.
In particular, said step M3.2 comprises:
wherein x isscaleRepresenting the value after the most value normalization; x represents the original value; x is the number ofminRepresents the minimum value in any one-dimensional data set; x is the number ofmaxRepresenting the maximum value in any one-dimensional data.
Specifically, the step M5 includes adjusting the support degree parameter threshold and the reliability degree parameter threshold in Apriori algorithm and the distance calculation function parameter and the clustering method in K-means algorithm according to the evaluation of the development planning and the missing code change point.
The system for optimizing project management in software development provided by the invention, as shown in fig. 1, comprises:
module M1: inputting function points related to project iteration and corresponding development requirements;
module M2: detecting missing code change points in the development requirements by using an Apriori algorithm and making corresponding prompts;
module M3: clustering the development requirements with the association degree reaching a preset state by using a K-means algorithm to output development plan arrangement;
module M4: confirming and implementing development plan arrangement, and evaluating the development plan arrangement and the omitted code change points;
module M5: the Apriori algorithm and the K-means algorithm parameters are adjusted based on evaluations of development planning and missed code transition points.
In particular, said module M2 comprises:
module M2.1: constructing a development requirement-change code module relation data set;
module M2.2: finding out a frequent item set of a change code module in a development demand-change code module relation data set through an Apriori algorithm;
module M2.3: and traversing the development requirement and changing the frequent item set of the code module in a two-layer cycle manner, and outputting the missing code change points.
In particular, said module M2.1 comprises: the development requirement is associated with the change of function level code modules in different logic levels in a software architecture, and a development requirement-change code module relation data set is constructed by adding a code static scanning module in the project development.
In particular, said module M2.2 comprises: setting initial values of a support degree parameter threshold value and a reliability degree parameter threshold value as preset values, and triggering an Apriori algorithm to find out a frequent item set of a change code module;
and the support degree parameter threshold value and the credibility parameter threshold value are dynamically adjusted according to the data volume in the current data set and the accuracy of prediction by an Apriori algorithm, and when the total data volume reaches a preset value or the predicted misinformation reaches a preset value, the support degree parameter threshold value is improved, the calculation timeliness is accelerated, and the misinformation is reduced.
In particular, said module M2.3 comprises: the method comprises the steps that the coverage rate of a change code module in the change code module frequent item set in the change code module coverage development requirements reaches above a preset value, at least one change code module which does not exist in the development requirements is included, the association rule of the change code module union set is found out through an Apriori algorithm, the change of the code modules which do not belong to the union set in the association rule is transmitted to developers to confirm information, and the omitted code change points are obtained.
In particular, said module M3 comprises:
module M3.1: generating initial three-dimensional data for each development requirement;
module M3.2: carrying out the most value normalization on the three-dimensional data;
module M3.3: and outputting the clustering by using a K-means algorithm according to the value after the most value normalization.
Specifically, the three-dimensional data in the module M3.1 includes: development time, dependences and support function points;
the development time is in hours, and corresponding scores are obtained according to a preset rule;
ranking the depended-on according to the times of the development requirements being depended on by other development requirements, and obtaining corresponding scores according to a preset rule by ranking;
the support function points correspond to different discrete value scores for different function points, and the score corresponding to the current function point is given to the function point service according to development requirements.
In particular, said module M3.2 comprises:
wherein x isscaleRepresenting the value after the most value normalization; x represents the original value; x is the number ofminRepresents the minimum value in any one-dimensional data set; x is the number ofmaxRepresenting the maximum value in any one-dimensional data.
Specifically, the module M5 includes adjusting the support degree parameter threshold and the confidence degree parameter threshold in Apriori algorithm and the distance calculation function parameter and the clustering method in K-means algorithm according to the evaluation of the development planning and the missing code change point.
Example 2
Example 2 is a modification of example 1
In software project development iterations, one function point often relates to development requirements across multiple applications. The development requirements are intelligently clustered by adopting a K-means algorithm from three dimensional abstract characteristics of development time consumption, dependence degree and support function, and omitted detection is carried out on the development requirements by adopting an Apriori algorithm from a code atomic layer abstract association relation in combination with code scanning, so that the problem that the project progress is difficult to control in the software project iterative development process can be effectively solved.
Step 1: a project manager and a development responsible person respectively input function points related to the project iteration and corresponding development requirements;
step 2: detecting code change points which are possibly omitted in the development requirements by using an Apriori algorithm and intelligently reminding;
and step 3: clustering the development requirements with high relevance by using a K-means algorithm to output development plan arrangement;
and 4, step 4: the project manager and the development responsible person confirm the arrangement and implementation of the development plan, evaluate the intelligent suggestions given by the system afterwards and adjust the algorithm parameters of the system;
the existing code scanning technology lacks detection on the dependency relationship among codes, and scanning can be performed online without syntax errors and bugs. Whether a developer omits certain development work or not can only be exposed until the test and verification stage, the system can monitor possible omission in the development process, obviously can improve the project delivery speed, is the significance of the system, and optimizes the software project management system.
The step 2 comprises the following steps:
step 2.1: and constructing a development requirement-change code module relation data set. Software architecture design typically splits a code module into view, controller, model layers. Through a code static scanning technology, development requirements can be associated with code module changes of function levels in different logic levels, and a code static scanning step is added in project development to continuously strengthen a local development requirement-code module change relation data set
The data in the data set adopts the following structure: { development requirement: code change 1, code change 2. },
for example: { bulk empty shopping cart: ListAllProductInCart, DeleteCartProduct, CartManager, CartDao, ProductDao }.
Initially using this system, the user can put historical data into the format { demand: code changes are recorded into a data set, and if history is not recorded, the current data is recorded into the data set every time the system is used from now on, so that the data set is continuously enlarged.
Step 2.2: and finding out a variable code module frequent item set in a development demand-variable code module relation data set through an Apriori algorithm. The initial value of the support degree parameter threshold is set to be 0.5, the support degree parameter threshold can be dynamically adjusted by a user according to the data quantity in the current data set and the accuracy of algorithm prediction, and when the total data quantity is increased or the number of predicted false alarms is large, the support degree threshold can be properly improved to accelerate the calculation timeliness and reduce the false alarms.
Step 2.3: as shown in FIG. 3, two levels of loops traverse the development requirements and the frequent item sets of the change code modules, outputting code changes that may be missed. If the changed code modules in the changed code module frequent item set cover more than 80% of the changed code modules in the development requirements and at least comprise the changed code modules which do not exist in the development requirements, finding out the association rules of the changed code module union set through an Apriori algorithm, changing the code modules which do not belong to the union set in the association rules and inputting the changed code modules to the development responsible person for confirmation, setting the initial value of the reliability parameter threshold value to be 0.8, and if the prediction error reports are more, reducing the false reports by improving the reliability threshold value.
Two-layer loops are the loop structures in software development.
The step 3 comprises the following steps:
step 3.1: generating initial three-dimensional data for each development requirement: the development time, the dependence and the support function point are used as data in the K-means algorithm data set. Wherein development time is in hours; the dependences are ranked according to the times that the development demand is depended on by other development demands, and the top 20%, 30%, 40%, 50%, 60%, 70%, 80% and 100% of the rankings are respectively given by 9,7,6,5,4,3,2 and 1; and corresponding different discrete value scores are corresponding to different function points, and the corresponding score is given to which function point is served by the development requirement. For example: if a certain development requirement takes 5 hours, is ranked by the dependency 20%, and the support function point correspondence score is 1, then its initial three-dimensional data is (5,9, 1).
Step 3.2: and carrying out the most value normalization on the three-dimensional data. Since development is time consuming, depended and the importance of support function points in clustering for development requirements is the same, to improve clustering accuracy, initial data needs to be normalized. The formula is as follows:
wherein x isscaleThe representation represents a normalized value, x represents the original value, xminRepresenting the minimum, x, in a certain one-dimensional data setmaxRepresenting the maximum value in some one-dimensional data.
For example: the maximum value in the development requirement sub-data set is 20, the minimum value is 1, the maximum value in the dependent sub-data set is 9, the minimum value is 1, the maximum value in the support function point sub-data set is 5, the minimum value is 1, and the normalized value of the development requirement A is (0.21,1,0)
Step 3.3: clustering is output using the K-means algorithm. The clustering number is set by a project manager, data to be clustered are generated in the steps 3.1 and 3.2, a distance calculation function adopts three-dimensional vector Euler distance, an initial centroid is randomly selected, and a binary K clustering mode is adopted to dynamically reduce the error square sum of clustering. And finally, outputting the clustering result to a project manager and a development responsible person as a proposal of the iterative project development plan.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (8)
1. A method for optimizing project management in software development, comprising:
step M1: inputting function points related to project iteration and corresponding development requirements;
step M2: detecting missing code change points in the development requirements by using an Apriori algorithm and making corresponding prompts;
step M3: clustering the development requirements with the association degree reaching a preset state by using a K-means algorithm to output development plan arrangement;
step M4: confirming and implementing development plan arrangement, and evaluating the development plan arrangement and the omitted code change points;
step M5: adjusting Apriori algorithm and K-means algorithm parameters according to the evaluation made on development planning and missing code change points;
said step M2 comprises:
step M2.1: constructing a development requirement-change code module relation data set;
step M2.2: finding out a frequent item set of a change code module in a development demand-change code module relation data set through an Apriori algorithm;
step M2.3: two layers of circulation traverse development requirements and change the frequent item sets of the code modules, and output the missing code change points;
said step M3 comprises:
step M3.1: generating initial three-dimensional data for each development requirement;
step M3.2: carrying out the most value normalization on the three-dimensional data;
step M3.3: and outputting the clustering by using a K-means algorithm according to the value after the most value normalization.
2. A method for optimizing project management in software development according to claim 1, characterized in that said step M2.1 comprises: the development requirement is associated with the change of function level code modules in different logic levels in a software architecture, and a development requirement-change code module relation data set is constructed by adding a code static scanning step in the project development.
3. A method for optimizing project management in software development according to claim 1, characterized in that said step M2.2 comprises: setting initial values of a support degree parameter threshold value and a reliability degree parameter threshold value as preset values, and triggering an Apriori algorithm to find out a frequent item set of a change code module;
and the support degree parameter threshold value and the credibility parameter threshold value are dynamically adjusted according to the data volume in the current data set and the accuracy of prediction by an Apriori algorithm, and when the total data volume reaches a preset value or the predicted misinformation reaches a preset value, the support degree parameter threshold value is improved, the calculation timeliness is accelerated, and the misinformation is reduced.
4. A method for optimizing project management in software development according to claim 1, characterized in that said step M2.3 comprises: the method comprises the steps that the coverage rate of a change code module in the change code module frequent item set in the change code module coverage development requirements reaches above a preset value, at least one change code module which does not exist in the development requirements is included, the association rule of the change code module union set is found out through an Apriori algorithm, the change of the code modules which do not belong to the union set in the association rule is transmitted to developers to confirm information, and the omitted code change points are obtained.
5. The method of optimizing project management in software development according to claim 1, wherein said step M3.1 of three-dimensional data comprises: development time, dependences and support function points;
the development time is in hours, and corresponding scores are obtained according to a preset rule;
ranking the depended-on according to the times of the development requirements being depended on by other development requirements, and obtaining corresponding scores according to a preset rule by ranking;
the support function points correspond to different discrete value scores for different function points, and the score corresponding to the current function point is given to the function point service according to development requirements.
6. A method for optimizing project management in software development according to claim 1, characterized in that said step M3.2 comprises:
wherein x isscaleRepresenting the value after the most value normalization; x represents the original value; x is the number ofminRepresents the minimum value in any one-dimensional data set; x is the number ofmaxRepresenting the maximum value in any one-dimensional data.
7. The method of claim 1, wherein step M5 comprises adjusting support and confidence parameter thresholds in Apriori algorithm and distance computation function parameters and clustering methods in K-means algorithm based on evaluations of development planning and missing code transition points.
8. A system for optimizing project management in software development, based on the method for optimizing project management in software development of claim 1, comprising:
module M1: inputting function points related to project iteration and corresponding development requirements;
module M2: detecting missing code change points in development requirements by using an Apriori algorithm and intelligently reminding;
module M3: clustering the development requirements with the association degree reaching a preset state by using a K-means algorithm to output development plan arrangement;
module M4: confirming and implementing development plan arrangement, and evaluating the development plan arrangement and the omitted code change points;
module M5: the Apriori algorithm and the K-means algorithm parameters are adjusted based on evaluations of development planning and missed code transition points.
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