Browsing by Subject "Data cleaning"

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Open Access
Cleaning ground truth data in software task assignment
(Elsevier BV, 2022-05-25) Tecimer, K. A.; Tüzün, Eray; Moran, Cansu; Erdogmus, H.
Context: In the context of collaborative software development, there are many application areas of task assignment such as assigning a developer to fix a bug, or assigning a code reviewer to a pull request. Most task assignment techniques in the literature build and evaluate their models based on datasets collected from real projects. The techniques invariably presume that these datasets reliably represent the “ground truth”. In a project dataset used to build an automated task assignment system, the recommended assignee for the task is usually assumed to be the best assignee for that task. However, in practice, the task assignee may not be the best possible task assignee, or even a sufficiently qualified one. Objective: We aim to clean up the ground truth by removing the samples that are potentially problematic or suspect with the assumption that removing such samples would reduce any systematic labeling bias in the dataset and lead to performance improvements. Method: We devised a debiasing method to detect potentially problematic samples in task assignment datasets. We then evaluated the method’s impact on the performance of seven task assignment techniques by comparing the Mean Reciprocal Rank (MRR) scores before and after debiasing. We used two different task assignment applications for this purpose: Code Reviewer Recommendation (CRR) and Bug Assignment (BA). Results: In the CRR application, we achieved an average MRR improvement of 18.17% for the three learning-based techniques tested on two datasets. No significant improvements were observed for the two optimization-based techniques tested on the same datasets. In the BA application, we achieved a similar average MRR improvement of 18.40% for the two learning-based techniques tested on four different datasets. Conclusion: Debiasing the ground truth data by removing suspect samples can help improve the performance of learning-based techniques in software task assignment applications.
Open Access
Detection and elimination of systematic labeling bias in code reviewer recommendation systems
(Association for Computing Machinery, 2021-06-21) Tecimer, K. Ayberk; Tüzün, Eray; Dibeklioğlu, Hamdi; Erdoğmuş, Hakan
Reviewer selection in modern code review is crucial for effective code reviews. Several techniques exist for recommending reviewers appropriate for a given pull request (PR). Most code reviewer recommendation techniques in the literature build and evaluate their models based on datasets collected from real projects using open-source or industrial practices. The techniques invariably presume that these datasets reliably represent the “ground truth.” In the context of a classification problem, ground truth refers to the objectively correct labels of a class used to build models from a dataset or evaluate a model’s performance. In a project dataset used to build a code reviewer recommendation system, the recommended code reviewer picked for a PR is usually assumed to be the best code reviewer for that PR. However, in practice, the recommended code reviewer may not be the best possible code reviewer, or even a qualified one. Recent code reviewer recommendation studies suggest that the datasets used tend to suffer from systematic labeling bias, making the ground truth unreliable. Therefore, models and recommendation systems built on such datasets may perform poorly in real practice. In this study, we introduce a novel approach to automatically detect and eliminate systematic labeling bias in code reviewer recommendation systems. The bias that we remove results from selecting reviewers that do not ensure a permanently successful fix for a bug-related PR. To demonstrate the effectiveness of our approach, we evaluated it on two open-source project datasets —HIVE and QT Creator— and with five code reviewer recommendation techniques —Profile-Based, RSTrace, Naive Bayes, k-NN, and Decision Tree. Our debiasing approach appears promising since it improved the Mean Reciprocal Rank (MRR) of the evaluated techniques up to 26% in the datasets used.