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Thu 13 Oct 2022 17:40 - 18:00 at Ballroom C East - Technical Session 29 - AI for SE II Chair(s): Tim Menzies

With the rapid development of Deep Learning, deep predictive models have been widely applied to improve Software Engineering tasks, such as defect prediction and issue classification, and have achieved remarkable success. They are mostly trained in a supervised manner, which heavily relies on high-quality datasets. Unfortunately, due to the nature and source of software engineering data, the real-world datasets often suffer from the issues of sample mislabelling and class imbalance, thus undermining the effectiveness of deep predictive models in practice. This problem has become a major obstacle for deep learning-based Software Engineering. In this paper, we propose RobustTrainer, the first approach to learning deep predictive models on raw training datasets where the mislabelled samples and the imbalanced classes coexist. RobustTrainer consists of a two-stage training scheme, where the first learns feature representations robust to sample mislabelling and the second builds a classifier robust to class imbalance based on the learned representations in the first stage. We apply RobustTrainer to two popular Software Engineering tasks, i.e., Bug Report Classification and Software Defect Prediction. Evaluation results show that RobustTrainer effectively tackles the mislabelling and class imbalance issues and produces significantly better deep predictive models compared to the other six comparison approaches.

Thu 13 Oct

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16:00 - 18:00
Technical Session 29 - AI for SE IIResearch Papers / Journal-first Papers at Ballroom C East
Chair(s): Tim Menzies North Carolina State University
16:00
20m
Research paper
Are Neural Bug Detectors Comparable to Software Developers on Variable Misuse Bugs?
Research Papers
Cedric Richter University of Oldenburg, Jan Haltermann University of Oldenburg, Marie-Christine Jakobs Technical University of Darmstadt, Felix Pauck Paderborn University, Germany, Stefan Schott Paderborn University, Heike Wehrheim University of Oldenburg
DOI Pre-print Media Attached File Attached
16:20
20m
Research paper
Learning Contract Invariants Using Reinforcement Learning
Research Papers
Junrui Liu University of California, Santa Barbara, Yanju Chen University of California at Santa Barbara, Bryan Tan Amazon Web Services, I┼čil Dillig University of Texas at Austin, Yu Feng University of California at Santa Barbara
16:40
20m
Research paper
Compressing Pre-trained Models of Code into 3 MB
Research Papers
Jieke SHI Singapore Management University, Zhou Yang Singapore Management University, Bowen Xu School of Information Systems, Singapore Management University, Hong Jin Kang Singapore Management University, Singapore, David Lo Singapore Management University
DOI Pre-print Media Attached
17:00
20m
Research paper
A Transferable Time Series Forecasting Service using Deep Transformer model for Online SystemsVirtual
Research Papers
Tao Huang Tencent, Pengfei Chen Sun Yat-Sen University, Jingrun Zhang School of Data and Computer Science, Sun Yat-sen University, Ruipeng Li Tencent, Rui Wang Tencent
17:20
20m
Paper
The Weights can be Harmful: Pareto Search versus Weighted Search in Multi-Objective Search-Based Software EngineeringVirtual
Journal-first Papers
Tao Chen Loughborough University, Miqing Li University of Birmingham
Pre-print
17:40
20m
Research paper
Robust Learning of Deep Predictive Models from Noisy and Imbalanced Software Engineering DatasetsVirtual
Research Papers
Zhong Li Nanjing, Minxue Pan Nanjing University, Yu Pei Hong Kong Polytechnic University, Tian Zhang Nanjing University, Linzhang Wang Nanjing University, Xuandong Li Nanjing University