Just-In-Time Defect Prediction on JavaScript Projects: A Replication Study

Ni, Chuanfa; Xia, Xin; Lo, David; Yang, Xiaohu; Hassan, Ahmed E.

doi:10.1145/3508479

Cited by 19 publications

(5 citation statements)

References 89 publications

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“…Motivations: Although CBS+ has been verified to have the best performance on some datasets (e.g., change-level projects [17], JavaScript projects [21], and Alibaba projects [22]), the performance of CBS+ on the file-level PROMISE datasets is still unknown. Therefore, we would like to figure out how CBS+ performs on our experimental datasets.…”

Section: Rq1: Does Cbs+ Perform the Best In The File-level Eadp?mentioning

confidence: 99%

“…Yan et al [22] validated the effectiveness of CBS+ on Alibaba projects. Ni et al [21] investigated the change-level EADP models on JavaScript projects and found that CBS+ statistically significantly outperformed EALR, OneWay, and Man-ualUp. Therefore, we employ CBS+ as the EADP model in our study.…”

Section: Effort-aware Defect Predictionmentioning

confidence: 99%

“…[20] investigated CBS+ for cross‐project EADP and showed its superiority, and Ni et al. [21] have indicated CBS+ still outperforms the baselines on 20 JavaScript projects. Recently, Yan et al.…”

Section: Introductionmentioning

confidence: 99%

“…The experimental results show that CBS+ outperforms Effort-Aware Linear Regression (EALR) [18] and ManualUp [19]. Subsequently, Ni et al [20] investigated CBS+ for crossproject EADP and showed its superiority, and Ni et al [21] have indicated CBS+ still outperforms the baselines on 20 JavaScript projects. Recently, Yan et al [22] pointed out that Alibaba's Development Efficiency department took considerable interest in effort-aware bug identification and found that CBS+ performed better than EALR, ManualUp, and OneWay on Alibaba real-world software projects.…”

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confidence: 99%

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The impact of feature selection techniques on effort‐aware defect prediction: An empirical study

Keung

et al. 2023

IET Software

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Effort‐Aware Defect Prediction (EADP) methods sort software modules based on the defect density and guide the testing team to inspect the modules with high defect density first. Previous studies indicated that some feature selection methods could improve the performance of Classification‐Based Defect Prediction (CBDP) models, and the Correlation‐based feature subset selection method with the Best First strategy (CorBF) performed the best. However, the practical benefits of feature selection methods on EADP performance are still unknown, and blindly employing the best‐performing CorBF method in CBDP to pre‐process the defect datasets may not improve the performance of EADP models but possibly result in performance degradation. To assess the impact of the feature selection techniques on EADP, a total of 24 feature selection methods with 10 classifiers embedded in a state‐of‐the‐art EADP model (CBS+) on the 41 PROMISE defect datasets were examined. We employ six evaluation metrics to assess the performance of EADP models comprehensively. The results show that (1) The impact of the feature selection methods varies in classifiers and datasets. (2) The four wrapper‐based feature subset selection methods with forwards search, that is, AdaBoost with Forwards Search, Deep Forest with Forwards Search, Random Forest with Forwards Search, and XGBoost with Forwards Search (XGBF) are better than other methods across the studied classifiers and the used datasets. And XGBF with XGBoost as the embedded classifier in CBS+ performs the best on the datasets. (3) The best‐performing CorBF method in CBDP does not perform well on the EADP task. (4) The selected features vary with different feature selection methods and different datasets, and the features noc (number of children), ic (inheritance coupling), cbo (coupling between object classes), and cbm (coupling between methods) are frequently selected by the four wrapper‐based feature subset selection methods with forwards search. (5) Using AdaBoost, deep forest, random forest, and XGBoost as the base classifiers embedded in CBS+ can achieve the best performance. In summary, we recommend the software testing team should employ XGBF with XGBoost as the embedded classifier in CBS+ to enhance the EADP performance.

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Section: Rq1: Does Cbs+ Perform the Best In The File-level Eadp?mentioning

confidence: 99%

Section: Effort-aware Defect Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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The impact of feature selection techniques on effort‐aware defect prediction: An empirical study

Keung

et al. 2023

IET Software

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“…However, if there s a tiny crack within the metal, even though it is small in size, it might lead to the plate breaking under pressure, significantly affecting its performance. Additionally, documenting the category and location of defects can pave the way for predictive maintenance and provide valuable insights for refining product repair processes (Ni et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Datasets and Methods for Boosting Infrastructure Inspection: A Survey on Defect Classification

Yang

Liu

Zhao

et al. 2022

2022 IEEE 17th International Conference on Control &Amp; Automation (ICCA)

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Defect inspection is paramount within the closed-loop manufacturing system. However, existing datasets for defect inspection often lack precision and semantic granularity required for practical applications. In this paper, we introduce the Defect Spectrum, a comprehensive benchmark that offers precise, semanticabundant, and large-scale annotations for a wide range of industrial defects. Building on four key industrial benchmarks, our dataset refines existing annotations and introduces rich semantic details, distinguishing multiple defect types within a single image. Furthermore, we introduce Defect-Gen, a two-stage diffusion-based generator designed to create high-quality and diverse defective images, even when working with limited datasets. The synthetic images generated by Defect-Gen significantly enhance the efficacy of defect inspection models. Overall, The Defect Spectrum dataset demonstrates its potential in defect inspection research, offering a solid platform for testing and refining advanced models. Our project page is in https://envision-research.github.io/Defect_Spectrum.

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An empirical study of data sampling techniques for just-in-time software defect prediction

Li,

Du,

Zhang

et al. 2024

Autom Softw Eng

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Just-In-Time Defect Prediction on JavaScript Projects: A Replication Study

Cited by 19 publications

References 89 publications

The impact of feature selection techniques on effort‐aware defect prediction: An empirical study

The impact of feature selection techniques on effort‐aware defect prediction: An empirical study

Datasets and Methods for Boosting Infrastructure Inspection: A Survey on Defect Classification

An empirical study of data sampling techniques for just-in-time software defect prediction

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