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ESEM 2021
Mon 11 - Fri 15 October 2021
Thu 14 Oct 2021 15:45 - 16:00 at ESEM ROOM - Defect Prediction Chair(s): Valentina Lenarduzzi

Background: Just-In-Time (JIT) defect prediction models predict if a commit will introduce defects in the future. DeepJIT and CC2Vec are two state-of-the-art JIT Deep Learning (DL) techniques. Usually, defect prediction techniques are evaluated, treating all training data equally. However, data is usually imbalanced not only in terms of the overall class label (e.g., defect and non-defect) but also in terms of characteristics such as File Count, Edit Count, Multiline Comments, Inward Dependency Sum etc. Prior research has investigated the impact of class imbalance on prediction technique’s performance but not the impact of imbalance of other characteristics.

Aims: We aim to explore the impact of different commit related characteristic’s imbalance on DL defect prediction.

Method: We investigated different characteristic’s impact on the overall performance of DeepJIT and CC2Vec. We also propose a Siamese network based few-shot learning framework for JIT defect prediction (SifterJIT) combining Siamese network and DeepJIT.

Results: Our results show that DeepJIT and CC2Vec lose out on the performance by around 20% when trained and tested on imbalanced data. However, SifterJIT can outperform state-of-the-art DL techniques with an average of 8.65% AUC score, 11% precision, and 6% F1-score improvement.

Conclusions: Our results highlight that dataset imbalanced in terms of commit characteristics can significantly impact prediction performance, and few-shot learning based techniques can help alleviate the situation.

Thu 14 Oct

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

15:30 - 16:00
Defect PredictionTechnical Papers at ESEM ROOM
Chair(s): Valentina Lenarduzzi LUT University
15:30
15m
Talk
Continuous Software Bug Prediction
Technical Papers
Song Wang York University, Junjie Wang Institute of Software at Chinese Academy of Sciences, Jaechang Nam Handong Global University, Nachiappan Nagappan Facebook
Pre-print
15:45
15m
Talk
An Empirical Examination of the Impact of Bias on Just-in-time Defect Prediction
Technical Papers
Jiri Gesi University of California, Irvine, Jiawei Li University of california, Irvine, Iftekhar Ahmed University of California, Irvine

Information for Participants
Thu 14 Oct 2021 15:30 - 16:00 at ESEM ROOM - Defect Prediction Chair(s): Valentina Lenarduzzi
Info for room ESEM ROOM:

https://www.youtube.com/c/ESEM_Conference