Blogs (1) >>
ICSE 2019
Sat 25 - Fri 31 May 2019 Montreal, QC, Canada
Fri 31 May 2019 11:30 - 11:50 at Laurier - Defect Prediction Chair(s): Burak Turhan

Just-in-Time Software Defect Prediction (JIT-SDP) is an SDP approach that makes defect predictions at the software change level. Most existing JIT-SDP work assumes that the characteristics of the problem remain the same over time. However, JIT-SDP may suffer from class imbalance evolution. Specifically, the imbalance status of the problem (i.e., how much underrepresented the defect-inducing changes are) may be intensified or reduced over time. If occurring, this could render existing JIT-SDP approaches unsuitable, including those that re-build classifiers over time using only recent data. This work thus provides the first investigation of whether class imbalance evolution poses a threat to JIT-SDP. This investigation is performed in a realistic scenario by taking into account verification latency – the often overlooked fact that labeled training examples arrive with a delay. Based on 10 GitHub projects, we show that JIT-SDP suffers from class imbalance evolution, significantly hindering the predictive performance of existing JIT-SDP approaches. Compared to state-of-the-art class imbalance evolution learning approaches, the predictive performance of JIT-SDP approaches was up to 47.92% lower in terms of g-mean. Hence, it is essential to tackle class imbalance evolution in JIT-SDP. We therefore propose a novel class imbalance evolution approach for the specific context of JIT-SDP. While maintaining top ranked g-means, this approach managed to produce up to 12% more balanced recalls on the defect-inducing and clean classes than state-of-the-art class imbalance evolution approaches. We thus recommended it to avoid overemphasizing the defect-inducing class at the cost of too many false alarms in JIT-SDP.

Fri 31 May

Displayed time zone: Eastern Time (US & Canada) change

11:00 - 12:30
11:00
10m
Talk
Perceptions, Expectations, and Challenges in Defect PredictionJournal-First
Journal-First Papers
Zhiyuan Wan Zhejiang University, Xin Xia Monash University, Ahmed E. Hassan Queen's University, David Lo Singapore Management University, Jianwei Yin , Xiaohu Yang
11:10
20m
Talk
Mining Software Defects: Should We Consider Affected Releases?Artifacts AvailableArtifacts Evaluated ReusableTechnical Track
Technical Track
Suraj Yatish The University of Adelaide, Jirayus Jiarpakdee Monash University, Patanamon Thongtanunam The University of Melbourne, Chakkrit Tantithamthavorn Monash University, Australia
11:30
20m
Talk
Class Imbalance Evolution and Verification Latency in Just-in-Time Software Defect PredictionTechnical Track
Technical Track
George Cabral University of Birmingham, Leandro Minku , Emad Shihab Concordia University, Suhaib Mujahid Concordia University
11:50
10m
Talk
The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction ModelsJournal-First
Journal-First Papers
Chakkrit Tantithamthavorn Monash University, Australia, Ahmed E. Hassan Queen's University, Kenichi Matsumoto Nara Institute of Science and Technology
Pre-print
12:00
10m
Talk
On the Relative Value of Data Resampling Approaches for Software Defect PredictionJournal-First
Journal-First Papers
Kwabena E. Bennin Blekinge Institute of Technology, SERL Sweden, Jacky Keung , Akito Monden
Authorizer link
12:10
10m
Talk
Energy-Based Anomaly Detection A New Perspective for Predicting Software FailuresNIER Distinguished Paper AwardNIER
New Ideas and Emerging Results
Cristina Monni Università della Svizzera Italiana, Mauro Pezze Università della Svizzera italiana (USI) (Switzerland) and Università degli Studi di Milano Bicocca (Italy)
Pre-print
12:20
10m
Talk
Discussion Period
Papers