A Workload-Aware Defect Prediction Method Based on Weighted Software Networks
A prediction method and workload technology, applied in software testing/debugging, error detection/correction, instruments, etc., can solve problems such as economic loss, lack of operability, lack of workload perception modules, etc., to reduce time cost and funds cost, the effect of accurate discovery
Active Publication Date: 2021-06-08
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology
[0012] 1. When building a software network, the impact of the correlation strength between modules on defect identification is not considered;
[0013] 2. Lack of workload perception module, the obtained defect classification results still need a lot of time to review the code, lack of operability
[0014] Software defects seriously affect the quality of software, and even cause serious economic losses
Method used
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Embodiment
[0099] Such as figure 1 As shown, the workload-aware defect prediction method based on weighted software network includes the following steps:
[0100] I. Data collection and fusion.
[0101] Collect software system data and defect reports from defect tracking systems such as Bugzilla and JIRA. Collected software system data includes source code, code commits, and version information data in version control repositories (such as Git and SVN).
[0102] Code submission data includes title, description, discussion, modification file information, submitter, and submission time information. Bug report data includes number, title, description, discussion, fixed by, and when it was fixed information.
[0103] The defect reports of the defect tracking system in this embodiment are defect reports that have been repaired in the defect tracking system.
[0104] Establish a connection between code submission and defect report to realize the fusion of code submission and defect report...
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The invention belongs to the field of software defect prediction, and specifically discloses a workload-aware defect prediction method based on a weighted software network. The workload-aware defect prediction method designs a software module according to two association relationships between software modules and collaborative developers The correlation strength calculation method among them is used to construct an effective weighted software network structure, and the powerful learning ability of graph embedding technology is used to independently learn the feature representation of software modules in the weighted software network graph, which can better reflect the data between software modules, Invoking dependencies and dependencies of collaborative developers; at the same time, the present invention takes into account the review code workload for finding defects in the construction of the defect prediction method, which meets the actual needs of software development, and facilitates the rapid and accurate discovery of software defects.
Description
technical field [0001] The invention belongs to the field of software defect prediction and relates to a workload-aware defect prediction method based on a weighted software network. Background technique [0002] There are inevitably defects in the software development process, and these defects will lead to serious economic losses. Therefore, how to quickly and accurately find software defects plays a vital role in ensuring the quality of software systems. [0003] Software defect prediction technology is committed to identifying high-risk defective modules, narrowing the scope of developers' review and testing code, and realizing the reasonable allocation of limited resources. The defect prediction method based on metric information is the most commonly used method, and its metric meta information mainly includes artificially designed metric elements and self-learning metric elements based on abstract syntax trees. [0004] However, these features based on abstract synta...
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IPC IPC(8): G06F11/36
CPCG06F11/3608
Inventor 宫丽娜周宇宫宜辉
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS