Local versus Global Models for Just-In-Time Software Defect Prediction

Yang, Xingguang; Yu, Huiqun; Fan, Guisheng; Shi, Kai; Chen, Liqiong

doi:10.1155/2019/2384706

Cited by 16 publications

(15 citation statements)

References 32 publications

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“…In the experiment of Kamei data set, this study follows LocalJIT [33] and Kamei's experimental methods and performance indicators (AUC, F1-score), and compares them with their experimental results. The experimental results are shown in Fig.…”

Section: Wpdp-kameimentioning

confidence: 99%

Software defect prediction based on nested-stacking and heterogeneous feature selection

Chen

Wang

Song

2022

Complex Intell. Syst.

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Software testing guarantees the delivery of high-quality software products, and software defect prediction (SDP) has become an important part of software testing. Software defect prediction is divided into traditional software defect prediction and just-in-time software defect prediction (JIT-SDP). However, most of the existing software defect prediction frameworks are relatively simplified, which makes it extremely difficult to provide developers with more detailed reference information. To improve the effectiveness of software defect prediction and realize effective software testing resource allocation, this paper proposes a software defect prediction framework based on Nested-Stacking and heterogeneous feature selection. The framework includes three stages: data set preprocessing and feature selection, Nested-Stacking classifier, and model classification performance evaluation. The novel heterogeneous feature selection and nested custom classifiers in the framework can effectively improve the accuracy of software defect prediction. This paper conducts experiments on two software defect data sets (Kamei, PROMISE), and demonstrates the classification performance of the model through two comprehensive evaluation indicators, AUC, and F1-score. The experiment carried out large-scale within-project defect prediction (WPDP) and cross-project defect prediction (CPDP). The results show that the framework proposed in this paper has an excellent classification performance on the two types of software defect data sets, and has been greatly improved compared with the baseline models.

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Section: Wpdp-kameimentioning

confidence: 99%

Software defect prediction based on nested-stacking and heterogeneous feature selection

Chen

Wang

Song

2022

Complex Intell. Syst.

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“…Yang et al [9] conducted a comparative study between local and global training of models for defect prediction. For local training data was divided homogeneously using k-medoids.…”

Section: Previous Work 21 Just-in-time Software Defect Predictionmentioning

confidence: 99%

Defect Prediction Using Akaike and Bayesian Information Criterion

Albahli¹,

Yar²

2022

Computer Systems Science and Engineering

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“…The majority of the reported change‐level SDP studies are conducted on open‐source projects 23,29,31,33‐38 . Fewer studies focus on the change‐level defect prediction in an industrial context 2,6‐8,25 …”

Section: Related Workmentioning

confidence: 99%

Deployment of a change‐level software defect prediction solution into an industrial setting

Eken

Tufan²,

Tunaboylu³

et al. 2021

J Software Evolu Process

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Applying change‐level software defect prediction (SDP) in practice has several challenges regarding model validation techniques, data accuracy, and prediction performance consistency. A few studies report on these challenges in an industrial context. We share our experience in integrating an SDP into an industrial context. We investigate whether an “offline” SDP could reflect its “online” (real‐life) performance, and other deployment decisions: the model re‐training process and update period. We employ an online prediction strategy by considering the actual labels of training commits at the time of prediction and compare its performance against an offline prediction. We empirically assess the online SDP's performance with various lengths of the time gap between the train and test set and model update periods. Our online SDP's performance could successfully reach its offline performance. The time gap between the train and test commits, and model update period significantly impacts the online performance by 37% and 18% in terms of probability of detection (pd), respectively. We deploy the best SDP solution (73% pd) with an 8‐month time gap and a 3‐day update period. Contextual factors may determine the model performance in practice, its consistency, and trustworthiness. As future work, we plan to investigate the reasons for fluctuations in model performance over time.

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Local versus Global Models for Just-In-Time Software Defect Prediction

Cited by 16 publications

References 32 publications

Software defect prediction based on nested-stacking and heterogeneous feature selection

Software defect prediction based on nested-stacking and heterogeneous feature selection

Defect Prediction Using Akaike and Bayesian Information Criterion

Deployment of a change‐level software defect prediction solution into an industrial setting

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