ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction (MSR 2022 - Data and Tool Showcase Track)

Who

Hossein Keshavarz, Mei Nagappan

Track

MSR 2022 Data and Tool Showcase Track

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When

Wed 18 May 2022 13:28 - 13:32 at MSR Main room - odd hours - Session 4: Software Quality (Bugs & Smells) Chair(s): Maxime Lamothe, Mahmoud Alfadel

Abstract

In this paper, we present ApacheJIT, a large dataset for Just-In-Time defect prediction. ApacheJIT consists of clean and bug-inducing software changes in popular Apache projects. ApacheJIT has a total of 106,674 commits (28,239 bug-inducing and 78,435 clean commits). Having a large number of commits makes ApacheJIT a suitable dataset for machine learning models, especially deep learning models that require large training sets to effectively generalize the patterns present in the historical data to future data.

Link to Preprint

https://arxiv.org/abs/2203.00101

Hossein Keshavarz

David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada

Mei Nagappan

University of Waterloo

Canada

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Session Program

Wed 18 May
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13:00 - 13:50	Session 4: Software Quality (Bugs & Smells)Data and Tool Showcase Track / Technical Papers at MSR Main room - odd hours Chair(s): Maxime Lamothe Polytechnique Montreal, Montreal, Canada, Mahmoud Alfadel University of Waterloo

13:00 7m Talk		Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue Technical Papers Rui Shu North Carolina State University, Tianpei Xia North Carolina State University, Laurie Williams North Carolina State University, Tim Menzies North Carolina State University
13:07 7m Talk		To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set? Technical Papers Matteo Ciniselli Università della Svizzera Italiana, Luca Pascarella Università della Svizzera italiana (USI), Gabriele Bavota Software Institute, USI Università della Svizzera italiana Pre-print
13:14 7m Talk		How to Improve Deep Learning for Software Analytics (a case study with code smell detection) Technical Papers Rahul Yedida , Tim Menzies North Carolina State University Pre-print
13:21 7m Talk		Using Active Learning to Find High-Fidelity Builds Technical Papers Harshitha Menon Lawrence Livermore National Lab, Konstantinos Parasyris Lawrence Livermore National Laboratory, Todd Gamblin Lawrence Livermore National Laboratory, Tom Scogland Lawrence Livermore National Laboratory Pre-print
13:28 4m Talk		ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction Data and Tool Showcase Track Hossein Keshavarz David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada, Mei Nagappan University of Waterloo Pre-print
13:32 4m Talk		ReCover: a Curated Dataset for Regression Testing Research Data and Tool Showcase Track Francesco Altiero Università degli Studi di Napoli Federico II, Anna Corazza Università degli Studi di Napoli Federico II, Sergio Di Martino Università degli Studi di Napoli Federico II, Adriano Peron Università degli Studi di Napoli Federico II, Luigi Libero Lucio Starace Università degli Studi di Napoli Federico II
13:36 14m Live Q&A		Discussions and Q&A Technical Papers

Information for Participants

Wed 18 May 2022 13:00 - 13:50 at MSR Main room - odd hours - Session 4: Software Quality (Bugs & Smells) Chair(s): Maxime Lamothe, Mahmoud Alfadel

Info for room MSR Main room - odd hours:

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