This paper focuses on the problem of software defect prediction models built using online learning, which can be affected by the overlooking of defects during software testing. When a module is predicted as "non-defective", fewer test cases are allocated for that module, leading to potential overlooking of defects. This overlooking distorts the learning data utilized by online learning, negatively impacting the prediction accuracy.
To address this issue, the authors propose two methods:
Fixed prediction method: This method forcibly turns the prediction as "defective" during the initial stage of online learning to suppress the influence of Type 1 overlooking (overlooking due to fewer test cases on negatively predicted modules).
Proposed method: This method builds on the fixed prediction method, but discontinues the fixed prediction when the rate of Type 1 overlooking is low, to avoid the potential degradation of precision caused by the fixed prediction.
The authors conducted experiments using three datasets and artificially manipulated the probability of overlooking. The results showed that:
The proposed method can effectively mitigate the negative impact of defect overlooking on online learning-based software defect prediction models, ensuring high accuracy and recall even when testing resources are drastically reduced for modules predicted as defective.
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by Nikolay Fedo... at arxiv.org 04-18-2024
https://arxiv.org/pdf/2404.11033.pdfDeeper Inquiries