Mining Software Defects: Should We Consider Affected Releases?

Yatish, S G; Jiarpakdee, Jirayus; Thongtanunam, Patanamon; Tantithamthavorn, Chakkrit

doi:10.1109/icse.2019.00075

Cited by 92 publications

(87 citation statements)

References 63 publications

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“…Other lines that are not impacted by the bug-fixing commits are identified as clean lines. Similar to Yatish et al [91], we also identify the files that are impacted by the bug-fixing commits as defective files, otherwise clean.…”

Section: Extracting Defective Linesmentioning

confidence: 99%

“…In this work, we use a corpus of publicly-available defect datasets provided by Yatish et al [91] where the groundtruths are labelled based on the affected releases. The datasets consist of 32 releases that span 9 open-source software systems from the Apache open source software projects.…”

Section: Studied Software Systemsmentioning

confidence: 99%

“…Those commits that have the ID of a bug report are identified as bug-fixing commits. This technique allows us to generate a defect dataset with fewer false positives than using a defect-related keyword search (like "bug", "defect") which lead to a better performance of defect prediction models [63,91]. Identifying defective lines: To identify defective lines, we first examine the diff (a.k.a.…”

Section: Extracting Defective Linesmentioning

confidence: 99%

“…However, some bugfixing commits may not record such an ID of a bug report in the commit message. To ensure the quality of the dataset, we followed an approach suggested by prior work [13,91], i.e., focusing on the issues that are reported after a studied release; labelled as bugs; affected only the studied release; and already closed or fixed. Nevertheless, additional approaches that improve the quality of the dataset (e.g., recovering missing defective lines) may further improve the accuracy of our results.…”

Section: Threats To Validitymentioning

confidence: 99%

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Predicting Defective Lines Using a Model-Agnostic Technique

Wattanakriengkrai

Thongtanunam

Tantithamthavorn

et al. 2022

IIEEE Trans. Software Eng.

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Defect prediction models are proposed to help a team prioritize source code areas files that need Software Quality Assurance (SQA) based on the likelihood of having defects. However, developers may waste their unnecessary effort on the whole file while only a small fraction of its source code lines are defective. Indeed, we find that as little as 1%-3% of lines of a file are defective. Hence, in this work, we propose a novel framework (called LINE-DP) to identify defective lines using a model-agnostic technique, i.e., an Explainable AI technique that provides information why the model makes such a prediction. Broadly speaking, our LINE-DP first builds a file-level defect model using code token features. Then, our LINE-DP uses a state-of-the-art model-agnostic technique (i.e., LIME) to identify risky tokens, i.e., code tokens that lead the file-level defect model to predict that the file will be defective. Then, the lines that contain risky tokens are predicted as defective lines. Through a case study of 32 releases of nine Java open source systems, our evaluation results show that our LINE-DP achieves an average recall of 0.61, a false alarm rate of 0.47, a top 20%LOC recall of 0.27, and an initial false alarm of 16, which are statistically better than six baseline approaches. Our evaluation shows that our LINE-DP requires an average computation time of 10 seconds including model construction and defective identification time. In addition, we find that 63% of defective lines that can be identified by our LINE-DP are related to common defects (e.g., argument change, condition change). These results suggest that our LINE-DP can effectively identify defective lines that contain common defects while requiring a smaller amount of inspection effort and a manageable computation cost. The contribution of this paper builds an important step towards line-level defect prediction by leveraging a model-agnostic technique.

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Section: Extracting Defective Linesmentioning

confidence: 99%

Section: Studied Software Systemsmentioning

confidence: 99%

Section: Extracting Defective Linesmentioning

confidence: 99%

Section: Threats To Validitymentioning

confidence: 99%

See 2 more Smart Citations

Predicting Defective Lines Using a Model-Agnostic Technique

Wattanakriengkrai

Thongtanunam

Tantithamthavorn

et al. 2022

IIEEE Trans. Software Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recent work point out that the selection [39,76] and the quality [84] of datasets dataset selection might impact conclusions of a study. Thus, our conclusions may alter when changing a set of the studieds datasets.…”

Section: Threats To Validitymentioning

confidence: 99%

The Impact of Correlated Metrics on the Interpretation of Defect Models

Jiarpakdee

Tantithamthavorn

Hassan

2021

IIEEE Trans. Software Eng.

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Defect models are analytical models for building empirical theories related to software quality. Prior studies often derive knowledge from such models using interpretation techniques, e.g., ANOVA Type-I. Recent work raises concerns that correlated metrics may impact the interpretation of defect models. Yet, the impact of correlated metrics in such models has not been investigated. In this paper, we investigate the impact of correlated metrics on the interpretation of defect models and the improvement of the interpretation of defect models when removing correlated metrics. Through a case study of 14 publicly-available defect datasets, we find that (1) correlated metrics have the largest impact on the consistency, the level of discrepancy, and the direction of the ranking of metrics, especially for ANOVA techniques. On the other hand, we find that removing all correlated metrics (2) improves the consistency of the produced rankings regardless of the ordering of metrics (except for ANOVA Type-I); (3) improves the consistency of ranking of metrics among the studied interpretation techniques; (4) impacts the model performance by less than 5 percentage points. Thus, when one wishes to derive sound interpretation from defect models, one must (1) mitigate correlated metrics especially for ANOVA analyses; and (2) avoid using ANOVA Type-I even if all correlated metrics are removed.

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

Cui

Liu

Wang

2022

J Software Evolu Process

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Cross-project defect prediction (CPDP) is used to build defect prediction models when data from the target project are not enough. There has been several approaches to improve the performance of CPDP, such as feature transformation and instance selection methods. However, existing techniques are strongly dependent on the target data to reduce the distribution discrepancy between source and target projects. That is, the performance of these methods is determined by the effectiveness of feature transformation or the similarity between two projects. Additionally, when there is a large amount of source data that needs to be matched with target data, it will take much time and reduce the efficiency of model construction.Therefore, it is vital to explore a target project-agnostic approach to build CPDP models. This paper presents a Weighted Isolation Forest with class Label information Filter (WIFLF) to relieve the issues above. Four groups of datasets from AEEEM, Relink and PROMISE Data Repository are used to conduct CPDP models. Besides, WIFLF is compared with 12 approaches. The experimental results indicate that WIFLF significantly outperforms all the baselines. Specifically, WIFLF with random forest significantly improves the performance over the baselines on average by at least 14.64% and 4.90% with respect to Skewed F-Measure and G-Measure, respectively.

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Mining Software Defects: Should We Consider Affected Releases?

Cited by 92 publications

References 63 publications

Predicting Defective Lines Using a Model-Agnostic Technique

Predicting Defective Lines Using a Model-Agnostic Technique

The Impact of Correlated Metrics on the Interpretation of Defect Models

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

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