WIFLF: An approach independent of the target project for cross‐project defect prediction

Cui, Can; Liu, Bin; Wang, Shihai

doi:10.1002/smr.2497

Cited by 3 publications

(13 citation statements)

References 86 publications

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“…In this paper, we defer to the way most other papers 35,31,36 choose to define our problem, where the source and target items are defined as D S = {(…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Given the inherent imbalance in our datasets with respect to defects, we refrain from using metrics like accuracy and precision, as recommended by prior research. 41,42,35 Instead, we opt for common performance metrics tailored for evaluating unbalanced data. These include the Area Under the Curve (AUC), G-Mean, Balance, F1-measure, Recall, and PF.…”

Section: Evaluation Measuresmentioning

confidence: 99%

“…Therefore, we use z-score normalization to scale each metric of the unique source and target data to have a mean of 0 and a standard deviation of 1, as done in previous papers. 9,35 After the source project data and the target project data are processed, the following experiments are carried out.…”

Section: Experimental Designmentioning

confidence: 99%

“…The RF, LR, NB, and SVC are chosen as the base machine learning classifiers because they are widely used for defect prediction. 35,44,20…”

Section: Question 1 Does Iskmm Generate Practical Performance Compare...mentioning

confidence: 99%

“…We keep one parameter constant and change the other parameter unchanged to discuss the performance results as in the other paper. 35 In addition, SVC is used as the base learner, changing K from 0.1 to 1.0 with a step size of 0.1. Similar to K, we change P from 0 to 0.5 × S n with a step size of 0.1 × S n .…”

Section: 6mentioning

confidence: 99%

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A novel instance‐based method for cross‐project just‐in‐time defect prediction

Zhu,

Qiu,

Wang

et al. 2024

Softw Pract Exp

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SummaryCross‐project (CP) just‐in‐time software defect prediction (JIT‐SDP) uses CP data to overcome initial data scarcity for training high‐performing JIT‐SDP classifiers in the early stages of software projects. The primary challenge faced by JIT‐SDP in a cross‐project context lies in the distinct distributions between training and test data. To tackle this issue, we select source data instances that closely resemble target data for building classifiers. Software datasets commonly exhibit a class imbalance problem, where the ratio of the defective class to the clean class is notably low. This imbalance typically diminishes classifier performance. In this study, we propose an instance selection method utilizing kernel mean matching (ISKMM) that addresses both knowledge transfer and class imbalance in cross‐project defect prediction (CPDP). The method employs the kernel mean matching (KMM) technique to assess the similarity between training and target data. It selects instances with high similarity, retains them, and resamples the data based on similarity weighting to mitigate the class imbalance problem. Our experiments, conducted on 10 open‐source projects, reveal that the ISKMM algorithm outperforms existing CP single‐source software defect prediction (SDP) algorithms. Moreover, when employing the proposed algorithm, defect predictors constructed from cross‐project data demonstrate an overall performance comparable to predictors learned from within‐project data.

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“…In this paper, we defer to the way most other papers 35,31,36 choose to define our problem, where the source and target items are defined as D S = {(…”

Section: Problem Descriptionmentioning

confidence: 99%

Section: Evaluation Measuresmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

“…The RF, LR, NB, and SVC are chosen as the base machine learning classifiers because they are widely used for defect prediction. 35,44,20…”

Section: Question 1 Does Iskmm Generate Practical Performance Compare...mentioning

confidence: 99%

Section: 6mentioning

confidence: 99%

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A novel instance‐based method for cross‐project just‐in‐time defect prediction

Zhu,

Qiu,

Wang

et al. 2024

Softw Pract Exp

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Evolutionary measures and their correlations with the performance of cross‐version defect prediction for object‐oriented projects

Yu,

Zhu,

Han

et al. 2023

J Software Evolu Process

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Cross‐version defect prediction (CVDP) for evolutionary projects has attracted much attention from researchers in recent years. For multiple versions of an object‐oriented project, the degree of evolution (e.g., the degree of class change) between successive versions can reflect the differences between versions, which could affect the performance of CVDP. Therefore, how to measure the degree of evolution between successive versions and explore the correlations with the performance of CVDP are very important for software defect prediction. Based on the successive versions of evolutionary projects, this paper proposes six evolutionary measures from three aspects of class change, metric change, and label change, including the Ratio of New Classes (RNC), the Ratio of Deleted Classes (RDC), the Average Ratio of Metric Change (ARMC), the Ratio of Label Changed Classes (RLCC), the Ratio of Unchanged Classes (RUC), and the Ratio of Interference Classes (RIC). An empirical study was conducted on 40 versions of 11 object‐oriented projects from the PROMISE repository. Precision, Recall, F‐measure, and AUC were used as the performance indicators. Three correlation approaches (Pearson, Spearman, and Kendall) are applied to show the correlations between evolutionary measures and the performance of CVDP. The statistical results show that RNC, RDC, and RUC show no correlation with four performance indicators. ARMC shows weak or medium positive correlations with Recall and F‐measure. RLCC and RIC show very strong or strong negative correlations with Recall and F‐measure. The results indicate that the correlations between the proposed evolutionary measures and the performance of CVDP are different, which can guide the training set selection of CVDP.

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Two sides of the same coin: A study on developers' perception of defects

Santos,

Muzetti,

Figueiredo

2024

J Software Evolu Process

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SummarySoftware defect prediction is a subject of study involving the interplay of software engineering and machine learning. The current literature proposed numerous machine learning models to predict software defects from software data, such as commits and code metrics. Further, the most recent literature employs explainability techniques to understand why machine learning models made such predictions (i.e., predicting the likelihood of a defect). As a result, developers are expected to reason on the software features that may relate to defects in the source code. However, little is known about the developers' perception of these machine learning models and their explanations. To explore this issue, we focus on a survey with experienced developers to understand how they evaluate each quality attribute for the defect prediction. We chose the developers based on their contributions at GitHub, where they contributed to at least 10 repositories in the past 2 years. The results show that developers tend to evaluate code complexity as the most important quality attribute to avoid defects compared with the other target attributes such as source code size, coupling, and documentation. At the end, a thematic analysis reveals that developers evaluate testing the code as a relevant aspect not covered by the static software features. We conclude that, qualitatively, there exists a misalignment between developers' perceptions and the outputs of machine learning models. For instance, while machine learning models assign high importance to documentation, developers often overlook documentation and prioritize assessing the complexity of the code instead.

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WIFLF: An approach independent of the target project for cross‐project defect prediction

Cited by 3 publications

References 86 publications

A novel instance‐based method for cross‐project just‐in‐time defect prediction

A novel instance‐based method for cross‐project just‐in‐time defect prediction

Evolutionary measures and their correlations with the performance of cross‐version defect prediction for object‐oriented projects

Two sides of the same coin: A study on developers' perception of defects

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