CN114238080A - Software project quality prediction method, prediction system and medium - Google Patents
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Abstract
The invention discloses a method, a system and a medium for predicting the quality of a software project, wherein the method for predicting the quality of the software project comprises the following steps: acquiring quality evaluation results of a plurality of software projects and first defect item lists corresponding to the plurality of software projects, establishing a software quality prediction model, training and verifying the software quality prediction model based on the first defect item lists and the quality evaluation results, acquiring a second defect item list corresponding to the current software project, and inputting the second defect item list into the software quality prediction model to obtain a quality prediction result of the current software project; according to the prediction method, the quality condition of the current software project is predicted through machine learning according to the quantitative data generated in the development process, so that manpower and material resources are saved, and the feedback efficiency is improved.
Description
Technical Field
The invention belongs to the technical field of software quality evaluation, and particularly relates to a software project quality prediction method, a prediction system and a medium.
Background
The software quality measurement is used for evaluating the quality of software from multiple dimensions and multiple periods, the software quality measurement has the significance of management, and judgment, evaluation and decision are carried out through quantized indexes, so that the software quality measurement has very important significance in the whole software development process. In the current stage of software measurement, quality scores of projects are given by verifying the completion conditions of measurement indexes one by one, the quality scores are used for evaluating the quality conditions of the projects and identifying teams with excellent quality, so that the completion conditions of each quality measurement index need to be verified from complex documents and reports, the cost of manpower and material resources is increased, and the feedback efficiency is reduced.
Therefore, it is desirable to provide a method for predicting the quality of a software project, which can effectively solve the problem of the increase of the cost of manpower and material resources caused by the prior art and improve the feedback efficiency.
Disclosure of Invention
The invention aims to provide a method for predicting the quality of a software project, which can effectively solve the problem of improvement of labor and material cost caused by the prior art and improve the feedback efficiency.
In order to achieve the above object, the present invention provides a method for predicting quality of a software project, comprising:
obtaining quality evaluation results of a plurality of software projects and a first defect item list corresponding to the plurality of software projects;
establishing a software quality prediction model;
training and verifying the software quality prediction model based on the first defect item list and the quality evaluation result;
acquiring a second defect item list corresponding to the current software item;
and inputting the second defect item list into the software quality prediction model to obtain a quality prediction result of the current software item.
Optionally, the first defect entry list includes at least one of a defect density, a defect removal rate, a defect average repair time, a defect proportion modified after delay, a defect proportion repaired multiple times, a defect proportion of new defects introduced, defect proportions of various levels, a unit test coverage rate, and a code static inspection defect rate corresponding to each of the software items.
Optionally, before the obtaining the quality evaluation results of the plurality of software items and the first defect item list corresponding to the plurality of software items, the method further includes:
acquiring an original defect data list of a plurality of software projects and a code amount corresponding to each software project, wherein the original defect data list comprises the total number of defects corresponding to each software project, the number of repaired defects, the total repair time of the defects, the number of defects modified after delay, the number of defects repaired for multiple times, the number of defects introduced with new defects after repair, the number of defects in various grades and the number of defects detected by code static state;
and calculating to obtain the first defect item list based on the original defect data list and the code amount.
Alternatively,
defect density ═ total number of defects ÷ amount of code;
the defect removal rate is the number of repaired defects divided by the total number of defects;
the average defect repairing time is the sum of the repairing time of the defects and the total number of the defects;
the defect proportion of delayed modification is the defect quantity of delayed modification divided by the total number of defects;
the defect proportion of the multiple repairing is equal to the defect quantity repaired for multiple times divided by the total number of the defects;
the defect proportion of new defects is divided into the number of the new defects introduced after the repair and the total number of the defects;
the number of various grades of defects is the number of various grades of defects and the total number of the defects;
the code static inspection defect rate is the number of defects detected by the code static inspection divided by the code amount.
Optionally, the grade of the defect includes a fatal defect, a heavier defect, a general defect, and a light defect, wherein:
the ratio of fatal defect is the number of fatal defect and the total number of defect;
the ratio of serious defects is the quantity of serious defects divided by the total number of defects;
the ratio of the heavier defects is the number of the heavier defects divided by the total number of the defects;
general defect ratio is general defect number divided by defect total number;
light defect ratio is light defect number divided by defect total number.
Optionally, the training and verifying the software quality prediction model based on the first defect item list and the quality evaluation result includes:
extracting data with a preset proportion from the first defect item list;
training a software quality prediction model based on the data of the preset proportion and the quality evaluation result;
extracting data of a residual proportion from the first defect item list;
and verifying the software quality prediction model based on the data of the residual proportion and the quality evaluation result.
Optionally, the preset ratio + the remaining ratio is 100%.
Optionally, an SVM algorithm is used to train the software quality prediction model.
A system for predicting quality of a software project, comprising:
the first data acquisition module is used for acquiring quality evaluation results of a plurality of software items and a first defect item list corresponding to the plurality of software items;
the software quality prediction model establishing module is used for establishing a software quality prediction model;
the machine learning module is used for training and verifying a software quality prediction model based on the first defect item list and the quality evaluation result;
the second data acquisition module is used for acquiring a second defect item list corresponding to the current software item;
and the software quality prediction module is used for obtaining a quality prediction result of the current software project based on the second defect item list and the software prediction model.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method of predicting the quality of an item of software.
The invention has the beneficial effects that:
the method for predicting the quality of the software project comprises the following steps: acquiring quality evaluation results of a plurality of software projects and first defect item lists corresponding to the plurality of software projects, establishing a software quality prediction model, training and verifying the software quality prediction model based on the first defect item lists and the quality evaluation results, acquiring a second defect item list corresponding to the current software project, and inputting the second defect item list into the software quality prediction model to obtain a quality prediction result of the current software project; according to the prediction method, the quality condition of the current software project is predicted through machine learning according to the quantitative data generated in the development process, so that manpower and material resources are saved, and the feedback efficiency is improved.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
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The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts throughout.
FIG. 1 shows a flow diagram of a method of predicting quality of an item of software, according to one embodiment of the invention.
FIG. 2 illustrates a block diagram of a prediction system for quality of an item of software, according to one embodiment of the invention.
Detailed Description
Preferred embodiments of the present invention will be described in more detail below. While the following describes preferred embodiments of the present invention, it should be understood that the present invention may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The invention relates to a method for predicting the quality of a software project, which comprises the following steps:
obtaining quality evaluation results of a plurality of software projects and a first defect item list corresponding to the plurality of software projects;
establishing a software quality prediction model;
training and verifying the software quality prediction model based on the first defect item list and the quality evaluation result;
acquiring a second defect item list corresponding to the current software item;
and inputting the second defect item list into the software quality prediction model to obtain a quality prediction result of the current software item.
Specifically, the method for predicting the quality of the software project comprises the following steps: acquiring quality evaluation results of a plurality of software projects and first defect item lists corresponding to the plurality of software projects, establishing a software quality prediction model, training and verifying the software quality prediction model based on the first defect item lists and the quality evaluation results, acquiring a second defect item list corresponding to the current software project, and inputting the second defect item list into the software quality prediction model to obtain a quality prediction result of the current software project; according to the prediction method, the quality condition of the current software project is predicted through machine learning according to the quantitative data generated in the development process, so that manpower and material resources are saved, and the feedback efficiency is improved.
Furthermore, the manager can check the quality condition of the current software project at any time, provide necessary basis for subsequent decision making, and promote the quality improvement of the current software project.
In one example, the first defect entry list includes at least one of a defect density, a defect removal rate, a defect mean repair time, a defect proportion modified after delay, a defect proportion repaired multiple times, a defect proportion of new defects introduced, defect proportions of various levels, a unit test coverage rate, and a code static inspection defect rate corresponding to each software entry.
Specifically, the defect density is indirectly calculated based on the total number of defects and the amount of added and modified code. Based on the clustering of software defects, the more defects a feature has discovered, the greater the likelihood of finding more undiscovered defects, and thus, the greater the defect density, the better we have reason to believe the software quality. The defect removal rate represents the proportion of the proposed defects that are repaired, and a higher defect removal rate indicates a lower number of unrepaired defects and a higher software quality. The average defect repair time reflects the importance of developers to defects and the difficulty of repairing defects, and generally, the longer the defect repair time is, the harder it is to repair the defects, so that if the average defect repair time is longer, the worse the software quality is considered. The average defect repair time reflects the importance of developers to defects and the difficulty of repairing defects, and generally, the longer the defect repair time is, the harder it is to repair the defects, so that if the average defect repair time is longer, the worse the software quality is considered. There are some defects that, for various reasons, after being evaluated by a developer, the current version is considered to be unable to be solved, and the current version needs to be solved in a subsequent version, and this problem usually involves the change of a certain module or function implementation scheme, even involves the common modification of several modules, and the influence is often large, so if the defects of delayed modification are more, the software quality is considered to be worse. If the defect is not repaired well at one time, the defect is not verified to pass in the regression test, and the defect needs to be repaired for multiple times. The higher the defect rate of the multiple repairs, the worse the software quality is considered. When some defects are submitted to regression testing after being repaired, new defects are introduced, and the higher the defect proportion of the introduced new defects is, the worse the software quality is. The unit test coverage rate is given by a unit test tool, the unit test can find defects in the encoding stage, and can find some problems which are hidden deeply and are not easy to test, therefore, the higher the coverage rate of the unit test is, the better the software quality is. The code static inspection can not only reflect the problems of the programming specification, but also reflect the structural quality of the code and find out potential defects, so that the higher the defect rate of the code static inspection is, the worse the software quality is. The quantitative process data generated in the software development process can directly influence the quality of the software, and if the process data can be analyzed as early as possible in the process to adopt strategies in time, the improvement of the software quality can be effectively promoted.
In one example, before obtaining the quality evaluation results of the plurality of software items and the first defect item list corresponding to the plurality of software items, the method further comprises:
acquiring an original defect data list of a plurality of software projects and a code amount corresponding to each software project, wherein the original defect data list comprises the total number of defects, the number of repaired defects, the total repair time of the defects, the number of defects which are modified after delay, the number of defects which are repaired for multiple times, the number of defects which introduce new defects after repair, the number of defects of various grades and the number of defects which are statically checked by codes;
and calculating to obtain a first defect item list based on the original defect data list and the code amount.
In one example of the use of a magnetic resonance imaging system,
defect density ═ total number of defects ÷ amount of code;
the defect removal rate is the number of repaired defects divided by the total number of defects;
the average defect repairing time is the sum of the repairing time of the defects and the total number of the defects;
the defect proportion of delayed modification is the defect quantity of delayed modification divided by the total number of defects;
the defect proportion of the multiple repairing is equal to the defect quantity repaired for multiple times divided by the total number of the defects;
the defect proportion of new defects is divided into the number of the new defects introduced after the repair and the total number of the defects;
the number of various grades of defects is the number of various grades of defects and the total number of the defects;
the code static inspection defect rate is the number of defects detected by the code static inspection divided by the code amount.
In one example, the level of defects includes fatal defects, heavier defects, general defects, and light defects, wherein:
the ratio of fatal defect is the number of fatal defect and the total number of defect;
the ratio of serious defects is the quantity of serious defects divided by the total number of defects;
the ratio of the heavier defects is the number of the heavier defects divided by the total number of the defects;
general defect ratio is general defect number divided by defect total number;
light defect ratio is light defect number divided by defect total number.
Specifically, all defects are ranked according to severity as they are proposed, with higher levels indicating poorer software quality and lower levels indicating better software quality.
In one example, training and verifying the software quality prediction model based on the first defect item list and the quality evaluation result comprises:
extracting data with a preset proportion from the first defect item list;
training a software quality prediction model based on the data of the preset proportion and a quality evaluation result;
extracting data of the residual proportion from the first defect item list;
and verifying the software quality prediction model based on the data of the residual proportion and the quality evaluation result.
Specifically, in practical applications, preferably, the preset ratio is 75%, and the rest ratio is 25%.
In one example, the preset ratio + remaining ratio is 100%.
In one example, an SVM algorithm is employed to train the software quality prediction model.
Specifically, SVM is an abbreviation of support vector machines, and is named as support vector machine in chinese, but those skilled in the art generally use english abbreviations, and SVM algorithms are conventional algorithms used by those skilled in the art, and are easily obtained by those skilled in the art, and are not described herein in detail.
A system for predicting quality of a software project, comprising:
the first data acquisition module is used for acquiring quality evaluation results of a plurality of software projects and a first defect item list corresponding to the plurality of software projects;
the software quality prediction model establishing module is used for establishing a software quality prediction model;
the machine learning module is used for training and verifying the software quality prediction model based on the first defect item list and the quality evaluation result;
the second data acquisition module is used for acquiring a second defect item list corresponding to the current software item;
and the software quality prediction module is used for obtaining a quality prediction result of the current software item based on the second defect item list and the software prediction model.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method of predicting the quality of an item of software.
Example one
FIG. 1 shows a flow diagram of a method of predicting quality of an item of software, according to one embodiment of the invention.
As shown in fig. 1, the method for predicting the quality of a software project includes:
step 1: obtaining quality evaluation results of a plurality of software projects and a first defect item list corresponding to the plurality of software projects;
the first defect item list comprises at least one of defect density, defect removal rate, defect average repair time, delayed modified defect proportion, defect proportion of multiple repairs, defect proportion of introduced new defects, defect proportion of various grades, unit test coverage rate and code static inspection defect rate corresponding to each software item.
Before obtaining the quality evaluation results of the plurality of software items and the first defect item list corresponding to the plurality of software items, the method further comprises the following steps:
acquiring an original defect data list of a plurality of software projects and a code amount corresponding to each software project, wherein the original defect data list comprises the total number of defects, the number of repaired defects, the total repair time of the defects, the number of defects which are modified after delay, the number of defects which are repaired for multiple times, the number of defects which introduce new defects after repair, the number of defects of various grades and the number of defects which are statically checked by codes;
and calculating to obtain a first defect item list based on the original defect data list and the code amount.
Wherein,
defect density ═ total number of defects ÷ amount of code;
the defect removal rate is the number of repaired defects divided by the total number of defects;
the average defect repairing time is the sum of the repairing time of the defects and the total number of the defects;
the defect proportion of delayed modification is the defect quantity of delayed modification divided by the total number of defects;
the defect proportion of the multiple repairing is equal to the defect quantity repaired for multiple times divided by the total number of the defects;
the defect proportion of new defects is divided into the number of the new defects introduced after the repair and the total number of the defects;
the number of various grades of defects is the number of various grades of defects and the total number of the defects;
the code static inspection defect rate is the number of defects detected by the code static inspection divided by the code amount.
Wherein the grade of the defect includes a fatal defect, a heavier defect, a general defect, and a light defect, wherein:
the ratio of fatal defect is the number of fatal defect and the total number of defect;
the ratio of serious defects is the quantity of serious defects divided by the total number of defects;
the ratio of the heavier defects is the number of the heavier defects divided by the total number of the defects;
general defect ratio is general defect number divided by defect total number;
light defect ratio is light defect number divided by defect total number.
Step 2: establishing a software quality prediction model;
and step 3: training and verifying the software quality prediction model based on the first defect item list and the quality evaluation result;
the training and verifying of the software quality prediction model based on the first defect item list and the quality evaluation result comprises the following steps:
extracting data with a preset proportion from the first defect item list;
training a software quality prediction model based on the data of the preset proportion and a quality evaluation result;
extracting data of the residual proportion from the first defect item list;
and verifying the software quality prediction model based on the data of the residual proportion and the quality evaluation result.
And 4, step 4: acquiring a second defect item list corresponding to the current software item;
and 5: and inputting the second defect item list into the software quality prediction model to obtain a quality prediction result of the current software item.
Example two
FIG. 2 illustrates a block diagram of a prediction system for quality of an item of software, according to one embodiment of the invention.
As shown in fig. 2, the system for predicting the quality of a software project includes:
the first data acquisition module is used for acquiring quality evaluation results of a plurality of software projects and a first defect item list corresponding to the plurality of software projects;
the software quality prediction model establishing module is used for establishing a software quality prediction model;
the machine learning module is used for training and verifying the software quality prediction model based on the first defect item list and the quality evaluation result;
the second data acquisition module is used for acquiring a second defect item list corresponding to the current software item;
and the software quality prediction module is used for obtaining a quality prediction result of the current software item based on the second defect item list and the software prediction model.
EXAMPLE III
The present disclosure provides an electronic device including: a memory storing executable instructions; and the processor executes the executable instructions in the memory to realize the prediction method of the quality of the software project.
An electronic device according to an embodiment of the present disclosure includes a memory and a processor.
The memory is to store non-transitory computer readable instructions. In particular, the memory may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc.
The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the disclosure, the processor is configured to execute the computer readable instructions stored in the memory.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.
Claims (10)
1. A method for predicting quality of a software project, comprising:
obtaining quality evaluation results of a plurality of software projects and a first defect item list corresponding to the plurality of software projects;
establishing a software quality prediction model;
training and verifying the software quality prediction model based on the first defect item list and the quality evaluation result;
acquiring a second defect item list corresponding to the current software item;
and inputting the second defect item list into the software quality prediction model to obtain a quality prediction result of the current software item.
2. The method of predicting the quality of software projects according to claim 1, wherein the first defect entry list includes at least one of defect density, defect removal rate, defect mean repair time, defect proportion modified after delay, defect proportion repaired multiple times, defect proportion introduced with new defects, defect proportion of various levels, unit test coverage, and code static inspection defect rate corresponding to each of the software projects.
3. The method for predicting the quality of a software project according to claim 2, wherein before the obtaining the quality evaluation results of the plurality of software projects and the first defect item list corresponding to the plurality of software projects, the method further comprises:
acquiring an original defect data list of a plurality of software projects and a code amount corresponding to each software project, wherein the original defect data list comprises the total number of defects corresponding to each software project, the number of repaired defects, the total repair time of the defects, the number of defects modified after delay, the number of defects repaired for multiple times, the number of defects introduced with new defects after repair, the number of defects in various grades and the number of defects detected by code static state;
and calculating to obtain the first defect item list based on the original defect data list and the code amount.
4. The method of predicting the quality of an item of software according to claim 3,
defect density ═ total number of defects ÷ amount of code;
the defect removal rate is the number of repaired defects divided by the total number of defects;
the average defect repairing time is the sum of the repairing time of the defects and the total number of the defects;
the defect proportion of delayed modification is the defect quantity of delayed modification divided by the total number of defects;
the defect proportion of the multiple repairing is equal to the defect quantity repaired for multiple times divided by the total number of the defects;
the defect proportion of new defects is divided into the number of the new defects introduced after the repair and the total number of the defects;
the number of various grades of defects is the number of various grades of defects and the total number of the defects;
the code static inspection defect rate is the number of defects detected by the code static inspection divided by the code amount.
5. The method of predicting the quality of an item of software of claim 4, wherein the level of defects includes critical defects, heavy defects, general defects, and light defects, wherein:
the ratio of fatal defect is the number of fatal defect and the total number of defect;
the ratio of serious defects is the quantity of serious defects divided by the total number of defects;
the ratio of the heavier defects is the number of the heavier defects divided by the total number of the defects;
general defect ratio is general defect number divided by defect total number;
light defect ratio is light defect number divided by defect total number.
6. The method for predicting the quality of a software project according to claim 1, wherein the training and verifying the software quality prediction model based on the first defect item list and the quality evaluation result comprises:
extracting data with a preset proportion from the first defect item list;
training a software quality prediction model based on the data of the preset proportion and the quality evaluation result;
extracting data of a residual proportion from the first defect item list;
and verifying the software quality prediction model based on the data of the residual proportion and the quality evaluation result.
7. The method of claim 6, wherein the predetermined ratio + the remaining ratio is 100%.
8. The method of predicting the quality of a software project of claim 1, wherein an SVM algorithm is employed to train the software quality prediction model.
9. A system for predicting quality of a software project, comprising:
the first data acquisition module is used for acquiring quality evaluation results of a plurality of software items and a first defect item list corresponding to the plurality of software items;
the software quality prediction model establishing module is used for establishing a software quality prediction model;
the machine learning module is used for training and verifying a software quality prediction model based on the first defect item list and the quality evaluation result;
the second data acquisition module is used for acquiring a second defect item list corresponding to the current software item;
and the software quality prediction module is used for obtaining a quality prediction result of the current software project based on the second defect item list and the software prediction model.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when being executed by a processor, carries out the method for predicting the quality of an item of software as claimed in any one of claims 1 to 8.
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