CN112148595A - Software change level defect prediction method for removing repeated change - Google Patents
Software change level defect prediction method for removing repeated change Download PDFInfo
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Abstract
The invention provides a software change level defect prediction method for removing repeated changes, and belongs to the technical field of software defect prediction. The method comprises the following steps: extracting the change data of all branches in the project warehouse; marking the extracted change data, wherein the marking information comprises: defect changes and non-defect changes; removing repeated changes in the extracted change data; calculating the defect prediction characteristics of the alteration level software for removing the repeatedly altered alteration data; and training a defect prediction model according to the calculated change-level software defect prediction characteristics and the marking information so as to enable the trained defect prediction model to judge whether the change data to be predicted is defect change. By adopting the method and the device, the prediction performance of the software change-level defect prediction model can be improved.
Description
Technical Field
The invention relates to the technical field of software defect prediction, in particular to a software change level defect prediction method for removing repeated change.
Background
In recent years, thanks to the rapid development of computer technology, the software industry is getting larger and larger, and meanwhile, great challenges are brought to the high-quality development of software. Software defects become the primary factors influencing the software quality, and software defect prediction is an important activity for ensuring the software quality and an important means for software maintenance. Compared with the traditional software defect prediction technology, the change-level defect prediction technology has the advantages of fine granularity, instantaneity, easiness in tracing and the like. The requirements of high behavior interaction and large-scale cross-region collaborative development mode in modern software production can be met.
Change-level bug prediction techniques refer to techniques that predict whether a code change submitted by a developer each time is flawed. The prediction technology can analyze the defects of the changed codes after a developer submits a code change once, and predict the possibility of the defects. The software project historical change data is the data source for change-level defect prediction techniques. However, due to the branching nature of the software version control system, there may be a large number of repeated changes in the software project repository, which refers to changes that a developer may re-implement similar functionality on a certain branch. The existing research shows that repeated change can influence the calculation of change-level defect prediction characteristics, so that the performance of a software defect prediction model is reduced.
Disclosure of Invention
The embodiment of the invention provides a software change-level defect prediction method for removing repeated changes, and the prediction performance of a software change-level defect prediction model can be improved by removing the repeated changes. The technical scheme is as follows:
in one aspect, a software change level defect prediction method for removing repeated changes is provided, and the method is applied to an electronic device and comprises the following steps:
extracting the change data of all branches in the project warehouse;
marking the extracted change data, wherein the marking information comprises: defect changes and non-defect changes;
removing repeated changes in the extracted change data;
calculating the defect prediction characteristics of the alteration level software for removing the repeatedly altered alteration data;
and training a defect prediction model according to the calculated change-level software defect prediction characteristics and the marking information so as to enable the trained defect prediction model to judge whether the change data to be predicted is defect change.
Further, the extracting change data of all branches in the project warehouse comprises:
and the project warehouse is mined, code change meta information is extracted from the project warehouse, a code front and back change relation graph is established according to the time sequence of code change in the meta information, and change data of all branches in the project warehouse are extracted according to the established code front and back change relation graph.
Further, the labeling the extracted change data includes:
scanning and traversing all change data stored in the version control system, performing keyword analysis according to the change log, and searching all changes of the repaired defect codes;
identifying the changed code line of the repaired defect code by using a diff command of a version control system, removing noise and identifying the defect code line;
and utilizing an annotate command of the version control system to trace back the code change submission history, identifying the change of the introduced defect code line, marking the change as a defect change, and marking the other changes except the defect change as non-defect changes.
Further, the removing of the repeated changes in the extracted changed data includes:
and traversing and comparing the extracted change data, and if the two changes meet the following 3 conditions:
condition 1, two changes on different branches;
condition 2, both changes modify the same file;
condition 3, both changes modify the same code;
then the two changes are judged to be a pair of repeated changes, and the change with the later change time is removed.
Further, the calculating alteration-level software defect prediction characteristics of the altered data after removing the repeated alteration comprises:
and calculating the change-level software defect prediction characteristics of the changed data after repeated change removal by using the extracted code change meta information and the label information of the changed data.
Further, the alteration level software bug prediction features include: the number of code lines added by change, the number of code lines deleted by change, the number of file code lines before change, the number of subsystems modified by change, the number of code directories modified by change, the number of files modified by change, the dispersion degree of change at file level, the number of developers related to change files, the average time interval of change files from the last change, the number of modifications related to change files, whether the change is a repair type change, the number of changes submitted by developers, the number of changes submitted recently by developers and the number of changes of developers at subsystem level.
Further, the training of the defect prediction model according to the calculated change-level software defect prediction features and the labeling information includes:
and inputting the calculated change-level software defect prediction characteristics and the marking information into a classifier, training the classifier, and taking the classifier at the moment as a trained defect prediction model when the loss function of the classifier is minimum.
Further, the step of judging whether the change data to be predicted is the defect change by the trained defect prediction model comprises:
and inputting the defect prediction characteristics of the change-level software of the change data to be predicted into a trained defect prediction model, and judging whether the change data to be predicted is defect change or not by the defect prediction model.
In one aspect, an electronic device is provided, where the electronic device includes a processor and a memory, where the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the software change level defect prediction method for removing repeated changes.
In one aspect, a computer-readable storage medium is provided, in which at least one instruction is stored, and the at least one instruction is loaded and executed by a processor to implement the above-mentioned software change-level defect prediction method for removing repeated changes.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
in the embodiment of the invention, the change data of all branches in the project warehouse is extracted; marking the extracted change data, wherein the marking information comprises: defect changes and non-defect changes; removing repeated changes in the extracted change data; calculating the defect prediction characteristics of the alteration level software for removing the repeatedly altered alteration data; and training a defect prediction model according to the calculated change-level software defect prediction characteristics and the marking information so as to enable the trained defect prediction model to judge whether the change data to be predicted is defect change. Therefore, by removing repeated changes, the performance of the trained software change-level defect prediction model can be improved, the software quality and the software quality can be improved, and the software safety can be maintained
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flowchart of a software change level defect prediction method for removing repetitive changes according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating an operation principle of a software change level defect prediction method for removing repeated changes according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a workflow of identifying repeated changes according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
To better understand the present embodiment, the relationship between Git, project warehouse, Git tool is briefly described first:
git is a version control system;
the project warehouse is stored in Git, and can be called as Git repository, and is used for storing source codes and change logs, namely, the source codes and the change logs are both stored in the version control system;
the Git tool refers to the wrapper function of Git.
As shown in fig. 1 and fig. 2, an embodiment of the present invention provides a software change level defect prediction method for removing repeated changes, where the method may be implemented by an electronic device, and the electronic device may be a terminal or a server, and the method includes:
s101, extracting the change data of all branches in the project warehouse;
s102, labeling the extracted change data, wherein the labeling information comprises: defect changes and non-defect changes;
s103, removing repeated changes in the extracted change data;
s104, calculating the defect prediction characteristics of the alteration level software for removing the repeatedly altered alteration data;
and S105, training a defect prediction model according to the calculated change-level software defect prediction characteristics and the marking information so that the trained defect prediction model can judge whether the change data to be predicted is defect change.
The software change level defect prediction method for removing repeated changes extracts the change data of all branches in a project warehouse; marking the extracted change data, wherein the marking information comprises: defect changes and non-defect changes; removing repeated changes in the extracted change data; calculating the defect prediction characteristics of the alteration level software for removing the repeatedly altered alteration data; and training a defect prediction model according to the calculated change-level software defect prediction characteristics and the marking information so as to enable the trained defect prediction model to judge whether the change data to be predicted is defect change. Therefore, by removing repeated changes, the performance of the trained software change-level defect prediction model can be improved, the software quality and the software quality can be improved, and the software safety can be maintained.
In an embodiment of the foregoing software change-level defect prediction method for removing repeated changes, the extracting change data of all branches in the project warehouse further includes:
the project warehouse is mined, code change meta information is extracted from the project warehouse, a code front and back change relation graph is established according to the time sequence of code change in the meta information, and change data of all branches in the project warehouse are extracted according to the established code front and back change relation graph; wherein the change meta information includes: source code and a change log, code change time being stored in the change log.
In this embodiment, traversing all extracted change data, labeling the extracted change data by using a reconstruction perception-based introduced defect change identification method (RA-SZZ) algorithm, and labeling the change data as defect change and non-defect change, specifically including:
scanning and traversing all change data stored in the version control system, performing keyword analysis according to the change log, and searching all changes of the repaired defect codes;
identifying a changed code line of the repaired defect code by using a diff (contrast) command of a version control system, removing noise (for example, ignoring the change of the reconstructed code), and identifying a defect code line;
and utilizing an annotate command of the version control system to trace back the code change submission history, identifying the change of the introduced defect code line, marking the change as a defect change, and marking the other changes except the defect change as non-defect changes.
In this embodiment, compared with the data labeling by using the RA-SZZ algorithm and the conventional code change method (SZZ algorithm) for automatically identifying introduced defects, the RA-SZZ algorithm used in this embodiment can automatically identify the changes introduced by defects and automatically sense the reconstructed codes, and removes noise caused by the reconstructed codes.
In this embodiment, the repeated change refers to a change that a developer can implement similar functions again on a certain branch, and removing the repeated change first requires identifying the repeated change in the project warehouse.
In the foregoing specific embodiment of the software change level defect prediction method for removing repeated changes, as shown in fig. 3, the step of traversing the change data in the version control system to compare whether there are other changes having the same change content as the traversed changes may further include the step of removing the repeated changes in the extracted change data by using the tool methods (GIT log, GIT diff) provided by the version control system (GIT):
and traversing and comparing the extracted change data, and if the two changes meet the following 3 conditions:
condition 1, two changes on different branches;
in this embodiment, all the project repositories start with a master branch, and the graph represented by the code context change relationship may be represented as a directed acyclic graph. For each change is based on one or several previous (parent) changes. In the GIT, each change has a different hashID (hash code identification). The present embodiment is based on the fact that the GIT tool compares the hashID of each change by running a "GIT log" (log extraction) command, for two changes with different ancestors from each other, they must be on different branches if their contents are the same.
Condition 2, both changes modify the same file;
in this embodiment, the change log information includes all information of the modified file. The present embodiment is based on the fact that the GIT tool traverses the change history of each change by running a "GIT log" command, obtains the name of the file and the number of file lines modified by the change, and for two changes from different branches, if they modify the same number of changed files and file lines, they are considered to implement similar or related functions.
Condition 3, both changes modify the same code;
in this embodiment, the code modification history of each change can be visually seen by tracing the code modification record of each change, and in this embodiment, the code modification position is checked by running a "GIT diff" (version comparison) command based on the GIT tool, a series of modified code lines in each change are obtained, and the line number of the modified code is calculated. For two changes from different branches, if they modify the same number of code lines, it indicates that they modified the same source code.
Then the two changes are judged to be a pair of repeated changes, and the change with the later change time is removed.
In the foregoing specific embodiment of the software modification level defect prediction method for removing repeated modifications, the calculating modification level software defect prediction characteristics of the modified data after removing repeated modifications further includes:
and calculating the change-level software defect prediction characteristics of the changed data after repeated change removal by using the extracted code change meta information and the label information of the changed data.
In this embodiment, the calculated change-level software defect prediction features have 5 dimensions, specifically include 14 features, as shown in table 1:
TABLE 1 Change level software Defect prediction features
In the foregoing specific embodiment of the software change-level defect prediction method for removing repeated changes, the training a defect prediction model according to the change-level software defect prediction features and the label information obtained by calculation includes:
and in the training stage, inputting the calculated change-level software defect prediction features and the marking information into a classifier, training the classifier, and taking the classifier at the moment as a trained defect prediction model when the loss function of the classifier is minimum.
In this embodiment, by using the machine learning technique, any one of a logistic regression classifier (LR), a random forest classifier (RF), and a naive bayes classifier (NB) may be used as a bottom classifier of the defect prediction model, wherein the working principle of each classifier is as follows:
the random forest contains a plurality of decision trees, each of which is constructed using a subset of random metrics. The decision tree may report different results when deciding on the category of the sample. Voting of a random forest aggregation decision tree to determine a final category of the sample;
logistic regression is a regression-based technique that is typically used to estimate the relationship between a binary dependent variable (i.e., a defect alteration introduced erroneously or in embodiments, a non-defect alteration) and one or more independent variables (i.e., a defect prediction feature);
naive Bayes is a probabilistic classifier based on Bayes' theorem, assuming that each feature attribute is independent.
In this embodiment, in consideration of the influence of the skewness (skewness includes unbalanced data distribution, inconsistent data dimension, and the like) and the correlation of the original features (i.e., the modified software defect prediction features obtained by calculation) on the performance of the defect prediction model, the original features may be preprocessed, including normalizing the skewness of the processed features by using logarithmic transformation and removing highly correlated indexes, and then the preprocessed features are input into the classifier.
In this embodiment, the gradient of the feature using the logarithmic conversion normalization process specifically includes: using standard logarithmic conversion ln (x)i+1) pairs of original features x1,x2,......xiAnd (6) carrying out normalization processing.
In this embodiment, the removing the highly relevant index specifically includes: after the rank correlation coefficient of every two features, namely the rank correlation analysis, is obtained, clustering analysis is carried out to obtain a numerical value, if the numerical value is more than 0.8, the two features are highly correlated, and one feature is removed.
In the foregoing specific embodiment of the software change-level defect prediction method for removing repeated changes, further, the determining, by the trained defect prediction model, whether the change data to be predicted is a defect change includes:
in the prediction stage, change data to be predicted is submitted to a project warehouse, then S101, S103 and S104 are executed to obtain change-level software defect prediction characteristics of the change data to be predicted, and then the change-level software defect prediction characteristics of the change data to be predicted are input into a trained defect prediction model, and the defect prediction model judges whether the change data to be predicted is defect change.
In order to verify the superiority of the software change-level defect prediction method for removing repeated changes provided by the embodiment of the invention, the embodiment extracts the historical change data of eight Apache open source items, the method comprises the steps of obtaining a project data set without repeated change by removing repeated change data of an original project, respectively using the original project data set and the project data set without repeated change to construct a defect prediction model, comparing the performances of the defect prediction model constructed by the original project data set and the project data set without repeated change according to experimental results by using three performance evaluation indexes of AUC (area of a working characteristic curve of a subject), MCC (Markushes correlation coefficient) and F1-measure (a weighted average of accuracy and recall ratio), as shown in table 2, Old in table 2 indicates a defect prediction model constructed using an original data set, and New indicates a defect prediction model constructed using an item data set excluding repeated alterations. The experimental results in Table 2 show that removing repeated alterations can significantly improve the performance of the alteration-level software defect prediction model, and the range of the performance of the enhancement model is 1% -125%.
TABLE 2 Defect prediction model Performance
Fig. 4 is a schematic structural diagram of an electronic device 600 according to an embodiment of the present invention, where the electronic device 600 may generate a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 601 and one or more memories 602, where the memory 602 stores at least one instruction, and the at least one instruction is loaded and executed by the processor 601 to implement the software change level defect prediction method for removing the repeated changes.
In an exemplary embodiment, a computer-readable storage medium, such as a memory, is also provided that includes instructions executable by a processor in a terminal to perform the software change level defect prediction method of removing duplicate changes described above. For example, the computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. A software change level bug prediction method that removes duplicate changes, comprising:
extracting the change data of all branches in the project warehouse;
marking the extracted change data, wherein the marking information comprises: defect changes and non-defect changes;
removing repeated changes in the extracted change data;
calculating the defect prediction characteristics of the alteration level software for removing the repeatedly altered alteration data;
and training a defect prediction model according to the calculated change-level software defect prediction characteristics and the marking information so as to enable the trained defect prediction model to judge whether the change data to be predicted is defect change.
2. The software change level bug prediction method of removing duplicate changes of claim 1, wherein extracting change data for all branches in a project warehouse comprises:
and the project warehouse is mined, code change meta information is extracted from the project warehouse, a code front and back change relation graph is established according to the time sequence of code change in the meta information, and change data of all branches in the project warehouse are extracted according to the established code front and back change relation graph.
3. The software change level defect prediction method of removing duplicate changes of claim 1, wherein said labeling the extracted change data comprises:
scanning and traversing all change data stored in the version control system, performing keyword analysis according to the change log, and searching all changes of the repaired defect codes;
identifying the changed code line of the repaired defect code by using a diff command of a version control system, removing noise and identifying the defect code line;
and utilizing an annotate command of the version control system to trace back the code change submission history, identifying the change of the introduced defect code line, marking the change as a defect change, and marking the other changes except the defect change as non-defect changes.
4. The software change level defect prediction method of removing duplicate changes of claim 1, wherein the removing of duplicate changes in the extracted change data comprises:
and traversing and comparing the extracted change data, and if the two changes meet the following 3 conditions:
condition 1, two changes on different branches;
condition 2, both changes modify the same file;
condition 3, both changes modify the same code;
then the two changes are judged to be a pair of repeated changes, and the change with the later change time is removed.
5. The software change level defect prediction method of removing duplicate changes of claim 2, wherein the calculating change level software defect prediction features of the changed data after removing duplicate changes comprises:
and calculating the change-level software defect prediction characteristics of the changed data after repeated change removal by using the extracted code change meta information and the label information of the changed data.
6. The software change level defect prediction method of removing duplicate changes of claim 1, wherein the change level software defect prediction features comprise: the number of code lines added by change, the number of code lines deleted by change, the number of file code lines before change, the number of subsystems modified by change, the number of code directories modified by change, the number of files modified by change, the dispersion degree of change at file level, the number of developers related to change files, the average time interval of change files from the last change, the number of modifications related to change files, whether the change is a repair type change, the number of changes submitted by developers, the number of changes submitted recently by developers and the number of changes of developers at subsystem level.
7. The software change-level defect prediction method for removing repeated changes according to claim 1, wherein the training of the defect prediction model according to the calculated change-level software defect prediction features and the labeling information comprises:
and inputting the calculated change-level software defect prediction characteristics and the marking information into a classifier, training the classifier, and taking the classifier at the moment as a trained defect prediction model when the loss function of the classifier is minimum.
8. The software change level defect prediction method of removing repetitive changes of claim 1, wherein the trained defect prediction model determining whether the changed data to be predicted is a defect change comprises:
and inputting the defect prediction characteristics of the change-level software of the change data to be predicted into a trained defect prediction model, and judging whether the change data to be predicted is defect change or not by the defect prediction model.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113986602A (en) * | 2021-12-27 | 2022-01-28 | 广州锦行网络科技有限公司 | Software identification method and device, storage medium and electronic equipment |
| CN116127417A (en) * | 2023-04-04 | 2023-05-16 | 山东浪潮科学研究院有限公司 | Code defect detection model construction method, device, equipment and storage medium |
| CN116339818A (en) * | 2023-05-30 | 2023-06-27 | 荣耀终端有限公司 | Code change type screening method, electronic device and readable storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110321007A1 (en) * | 2010-06-29 | 2011-12-29 | International Business Machines Corporation | Targeting code sections for correcting computer program product defects using records of a defect tracking system |
| CN111045920A (en) * | 2019-10-12 | 2020-04-21 | 浙江大学 | Workload-aware multi-branch software change-level defect prediction method |
| CN111078544A (en) * | 2019-12-04 | 2020-04-28 | 腾讯科技（深圳）有限公司 | Software defect prediction method, device, equipment and storage medium |
| CN111459799A (en) * | 2020-03-03 | 2020-07-28 | 西北大学 | Software defect detection model establishing and detecting method and system based on Github |
| CN111506504A (en) * | 2020-04-13 | 2020-08-07 | 扬州大学 | Software development process measurement-based software security defect prediction method and device |
| CN111597121A (en) * | 2020-07-24 | 2020-08-28 | 四川新网银行股份有限公司 | Precise test method based on historical test case mining |
-
2020
- 2020-09-03 CN CN202010917981.1A patent/CN112148595A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110321007A1 (en) * | 2010-06-29 | 2011-12-29 | International Business Machines Corporation | Targeting code sections for correcting computer program product defects using records of a defect tracking system |
| CN111045920A (en) * | 2019-10-12 | 2020-04-21 | 浙江大学 | Workload-aware multi-branch software change-level defect prediction method |
| CN111078544A (en) * | 2019-12-04 | 2020-04-28 | 腾讯科技（深圳）有限公司 | Software defect prediction method, device, equipment and storage medium |
| CN111459799A (en) * | 2020-03-03 | 2020-07-28 | 西北大学 | Software defect detection model establishing and detecting method and system based on Github |
| CN111506504A (en) * | 2020-04-13 | 2020-08-07 | 扬州大学 | Software development process measurement-based software security defect prediction method and device |
| CN111597121A (en) * | 2020-07-24 | 2020-08-28 | 四川新网银行股份有限公司 | Precise test method based on historical test case mining |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113986602A (en) * | 2021-12-27 | 2022-01-28 | 广州锦行网络科技有限公司 | Software identification method and device, storage medium and electronic equipment |
| CN116127417A (en) * | 2023-04-04 | 2023-05-16 | 山东浪潮科学研究院有限公司 | Code defect detection model construction method, device, equipment and storage medium |
| CN116339818A (en) * | 2023-05-30 | 2023-06-27 | 荣耀终端有限公司 | Code change type screening method, electronic device and readable storage medium |
| CN116339818B (en) * | 2023-05-30 | 2023-10-20 | 荣耀终端有限公司 | Code change type screening method, electronic device and readable storage medium |
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