Deriving Thresholds of Object-Oriented Metrics to Predict Defect-Proneness of Classes: A Large-Scale Meta-Analysis

Mei, Yuanqing; Rong, Yi; Liu, Shiran; Guo, Zhaoqiang; Yang, Yibiao; Lü, Hongmin; Tang, Yutian; Zhou, Yuming

doi:10.1142/s0218194023500110

Cited by 1 publication

(1 citation statement)

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“…A software metric 4 is the quantification of a software characteristic, which can be used to find potential problems with quality improvement and provide a quantitative evaluation of software quality. Because each metric evaluates the module of the source code from a different aspect, we can discover the relationship between the quality level and the risk level of the code with the help of a metric threshold.…”

Section: Related Workmentioning

confidence: 99%

Cross‐version defect prediction using threshold‐based active learning

Mei

Liu

et al. 2023

J Software Evolu Process

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Because defects in software modules (e.g., classes) might lead to product failure and financial loss, software defect prediction enables us to better understand and control software quality. Software development is a dynamic evolutionary process that may result in data distributions (e.g., defect characteristics) varying from version to version. In this case, effective cross‐version defect prediction (CVDP) is not easy to achieve. In this paper, we aim to investigate whether the defect prediction method of the threshold‐based active learning (TAL) can tackle the problem of the different data distribution between successive versions. Our TAL method includes two stages. At the active learning stage, a committee of investigated metrics is constructed to vote on the unlabeled modules of the current version. We pick up the unlabeled module with the median of voting scores to domain experts. The domain experts test and label the selected unlabeled module. Then, we merge the selected labeled module and the remaining modules with pseudo‐labels from the current version into the labeled modules of the prior version to form enhanced training data. Based on the training data, we derive the metric thresholds used for the next iteration. At the defect prediction stage, the iterations stop when a predefined threshold is reached. Finally, we use the cutoff threshold of voting scores, that is, 50%, to predict the defect‐prone of the remaining unlabeled modules. We evaluate the TAL method on 31 versions of 10 projects with three prevalent performance indicators. The results show that TAL outperforms the baseline methods, including three variations methods, two common supervised methods, and the state‐of‐the‐art method Hybrid Active Learning and Kernel PCA (HALKP). The results indicate that TAL can effectively address the different data distribution between successive versions. Furthermore, to keep the cost of extensive testing low in practice, selecting 5% of candidate modules from the current version is sufficient for TAL to achieve a good performance of defect prediction.

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Section: Related Workmentioning

confidence: 99%