ISSTA 2019
Mon 15 - Fri 19 July 2019 Beijing, China
Thu 18 Jul 2019 14:45 - 15:07 at Grand Ballroom - Testing and Machine Learning Chair(s): Hongyu Zhang

Learning-based fault localization has been intensively studied recently. Prior studies have shown that traditional Learning-to-Rank techniques can help precisely diagnose fault locations using various dimensions of fault-diagnosis features, such as suspiciousness values computed by various off-the-shelf fault localization techniques. However, with the increasing dimensions of features considered by advanced fault localization techniques, it can be quite challenging for the traditional Learning-to-Rank algorithms to automatically identify effective existing/latent features. In this work, we propose DeepFL, a deep learning approach to automatically learn the most effective existing/latent features for precise fault localization. Although the approach is general, in this work, we collect various suspiciousness-value-based, fault-proneness-based and textual-similarity-based features from the fault localization, defect prediction and information retrieval areas, respectively. The corresponding DeepFL techniques have been studied on 395 real bugs from the widely used Defects4J benchmark. The experimental results show that DeepFL can significantly outperform state-of-the-art TraPT/FLUCCS (e.g., localizing 50+ more faults within Top-1). We also investigate the impacts of deep model configurations (e.g., loss functions and epoch settings) and features. Furthermore, DeepFL is also surprisingly effective for cross-project prediction.

Thu 18 Jul

14:00 - 15:30: Technical Papers - Testing and Machine Learning at Grand Ballroom
Chair(s): Hongyu ZhangThe University of Newcastle
issta-2019-Technical-Papers14:00 - 14:22
Xiaofei XieNanyang Technological University, Lei MaKyushu University, Felix Juefei-XuCarnegie Mellon University, Minhui Xue, Hongxu ChenNanyang Technological University, Yang LiuNanyang Technological University, Singapore, Jianjun ZhaoKyushu University, Bo LiUIUC, Jianxiong YinNVIDIA AI Tech Centre, Simon SeeNVIDIA AI Tech Centre
issta-2019-Technical-Papers14:22 - 14:45
Maxime CordySnT, University of Luxembourg, Steve Mullerunaffiliated, Mike PapadakisUniversity of Luxembourg, Yves Le TraonUniversity of Luxembourg
issta-2019-Technical-Papers14:45 - 15:07
Xia LiUniversity of Texas at Dallas, USA, Wei LiSouthern University of Science and Technology, Yuqun ZhangSouthern University of Science and Technology, Lingming Zhang
issta-2019-Technical-Papers15:07 - 15:30
Goran PiskachevFraunhofer IEM, Lisa Nguyen Quang DoPaderborn University, Eric BoddenHeinz Nixdorf Institut, Paderborn University and Fraunhofer IEM
DOI Pre-print Media Attached File Attached