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A workload-aware multi-branch software change-level defect prediction method

A multi-branch, workload technology, applied in software testing/debugging, error detection/correction, instruments, etc., can solve the problems of inconsistent development mode, no workload perception module, and a lot of time for review, and achieve the effect of improving efficiency

Inactive Publication Date: 2021-05-04
ZHEJIANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] 1. Only the main branch is analyzed, which is inconsistent with the company's common software development model
[0004] 2. There is no workload perception module, and the obtained results still need a lot of time to review, lacking operability

Method used

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  • A workload-aware multi-branch software change-level defect prediction method
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  • A workload-aware multi-branch software change-level defect prediction method

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Embodiment

[0076] The time-wise cross-validation method was used for verification. The experimental results were compared from the five dimensions of recall, precision, f1-score, pci, and ifa, and the existing methods EALR and OneWay in the field of defect prediction. The results are shown in Tables 1 and 2. Show. In the test of six projects, the method in this paper finds about 15% more defects than the EALR method, and the recall is increased by about 47% on average. The detailed results are as follows:

[0077] Table 1 Comparison of results between the method in this paper and the EALR method

[0078]

[0079] Table 2 Comparison of results between the method in this paper and the OneWay (OW) method

[0080]

[0081]

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Abstract

The invention discloses a workload-aware multi-branch software change level defect prediction method, which belongs to the field of change level code defect prediction. The method includes: extracting change meta information, data labeling, calculating the characteristics of each branch and multi-branch processing, training Apply a workload-aware model. This method adds a workload perception module to help developers find more defects in as little time as possible, and is operable.

Description

technical field [0001] The invention belongs to the field of change-level code defect prediction, and in particular relates to a workload-aware multi-branch software change-level defect prediction method. Background technique [0002] Take the Commit Guru tool as an example, (C.Rosen, R.Graw, E.Shihab.Commit Guru: Analytics and Risk prediction of software Commits.In Proceedings of the JointMeeting on Foundations of software Engineering 2015), this tool utilizes 14 Each measure (including the number of lines of code added or subtracted from files, developer experience, etc.) predicts defects in software changes through a logistic regression model. But it has the following disadvantages [0003] 1. Only the main branch is analyzed, which is inconsistent with the company's common software development model [0004] 2. Without a workload sensing module, the obtained results still need a lot of time to review and lack operability. [0005] These problems exist widely in other ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F11/36
CPCG06F11/366
Inventor 蔡亮钟文枫刘力华张昕东鄢萌夏鑫李善平
Owner ZHEJIANG UNIV
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