Does chronology matter in JIT defect prediction?

Jahanshahi, Hadi; Jothimani, Dhanya; Başar, Ayşe; Çevik, Mücahit

doi:10.1145/3345629.3351449

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…Machine learning in JIT-SDP looks at a wide range of classifier techniques, from standalone learners to ensemblebased learners, to find the best models. Commonly used standalone classifiers include Logistic Regression [3,23,24], Naïve bayes [25], Support Vector Machine [26], Decision Tree [27], and Neural Network [28]. Whereas for ensemblebased learners range from single ensemble learner such as Random Forest [29] to multi-layer ensembles [27,30,31].…”

Section: A Modelling Of Software Defect Predictionmentioning

confidence: 99%

Toward Reduction in False Positives Just-In-Time Software Defect Prediction Using Deep Reinforcement Learning

Ismail,

Hamid,

Sani

et al. 2024

IEEE Access

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Deep Q-Network (DQN) is a popular deep reinforcement learning algorithm that has demonstrated promising results across a variety of domains. DQN presents a promising solution to the challenge of lowering false positives in software defect prediction, thereby enhancing the reliability of the prediction performance. In software defect prediction, false positives occur when the prediction model incorrectly predicts code changes to be defective. Consequently, developers waste time and resources on nonexistent defects. This paper advocates for employing DQN in software defect prediction, focusing on minimizing false positives and maximizing the prediction performance. Throughout the training phase, the model learns to predict defect-prone following a reward policy aimed at reducing false results. Experimental findings show that the proposed DQN outperforms baseline classifier, improving the prediction accuracy of true defects by up to 27% when using only 20% efforts. The results show that the effectiveness of DQN in tackling false positives, thereby emphasizing the significance of incorporating dynamic reward in predicting software defects.INDEX TERMS Software defect prediction, deep q-network, false positives

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Section: A Modelling Of Software Defect Predictionmentioning

confidence: 99%

Toward Reduction in False Positives Just-In-Time Software Defect Prediction Using Deep Reinforcement Learning

Ismail,

Hamid,

Sani

et al. 2024

IEEE Access

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“…As a result, this method is good for both computational efficiency and interpretation. The Breakdown library [36] is a machine learning library that can be used to analyze the contributions of individual variables in a predictive model's output. The library can be used to construct both binary classifiers and regression models.…”

Section: Break-downmentioning

confidence: 99%

“…The authors of [36] investigated whether the chronological order of data used in just-in-time (JIT) defect prediction models affects their performance. The authors partially replicated a previous study and evaluated the impact of different training and testing data sets on the performance of the model.…”

Section: Supervised Learning Techniquesmentioning

confidence: 99%

Improving Bug Assignment and Developer Allocation in Software Engineering through Interpretable Machine Learning Models

2023

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Software engineering is a comprehensive process that requires developers and team members to collaborate across multiple tasks. In software testing, bug triaging is a tedious and time-consuming process. Assigning bugs to the appropriate developers can save time and maintain their motivation. However, without knowledge about a bug’s class, triaging is difficult. Motivated by this challenge, this paper focuses on the problem of assigning a suitable developer to a new bug by analyzing the history of developers’ profiles and analyzing the history of bugs for all developers using machine learning-based recommender systems. Explainable AI (XAI) is AI that humans can understand. It contrasts with “black box” AI, which even its designers cannot explain. By providing appropriate explanations for results, users can better comprehend the underlying insight behind the outcomes, boosting the recommender system’s effectiveness, transparency, and confidence. The trained model is utilized in the recommendation stage to calculate relevance scores for developers based on expertise and past bug handling performance, ultimately presenting the developers with the highest scores as recommendations for new bugs. This approach aims to strike a balance between computational efficiency and accurate predictions, enabling efficient bug assignment while considering developer expertise and historical performance. In this paper, we propose two explainable models for recommendation. The first is an explainable recommender model for personalized developers generated from bug history to know what the preferred type of bug is for each developer. The second model is an explainable recommender model based on bugs to identify the most suitable developer for each bug from bug history.

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“…Our dataset covers diverse domains and programming languages. Note that the datasets used in the previous studies [4], [14], [9], [15] do not contain our new metrics and cannot be directly used for our experiments.…”

Section: B Datasetmentioning

confidence: 99%

Extending Developer Experience Metrics for Better Effort-Aware Just-In-Time Defect Prediction

Cho

Kwon

et al. 2022

IEEE Access

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Developers use defect prediction models to efficiently allocate limited resources for quality assurance and appropriately make a plan for software quality improvement activities. Traditionally, defect predictions are conducted at the module level, such as the class or file level. However, a more recent trend is to perform defect prediction for a single or consecutive commits to the repository, which is known as just-in-time (JIT) defect prediction. JIT defect prediction finds error-prone changes instead of error-prone modules, and as a result, the developer only needs to investigate error-prone changed lines instead of the entire module. When building JIT defect prediction models, researchers used various metrics, including developer experience metrics which measure the developer's experiences. Despite the fact that software defectiveness is likely to be affected by the experience of developers, developer metrics were understudied in the literature. In this work, we investigate the impact of various novel developer experience metrics and their combinations on JIT defect prediction. Our experimental results are positive. We found that it is possible to improve the cost-effectiveness of defect prediction models consistently and statistically significantly by using our new developer experience metrics.

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Does chronology matter in JIT defect prediction?

Cited by 9 publications

References 30 publications

Toward Reduction in False Positives Just-In-Time Software Defect Prediction Using Deep Reinforcement Learning

Toward Reduction in False Positives Just-In-Time Software Defect Prediction Using Deep Reinforcement Learning

Improving Bug Assignment and Developer Allocation in Software Engineering through Interpretable Machine Learning Models

Extending Developer Experience Metrics for Better Effort-Aware Just-In-Time Defect Prediction

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