Blogs (1) >>
ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Tue 12 Nov 2019 11:40 - 12:00 at Cortez 2&3 - AI and SE Chair(s): Kaiyuan Wang

Context: The challenge of locating bugs in mostly large-scale software systems has led to the development of bug localization techniques. However, the lexical mismatch between bug reports and source codes degrades the performances of existing information retrieval or machine learning-based approaches. Objective: To bridge the lexical gap and improve the effectiveness of localizing buggy files by leveraging the extracted semantic information from bug reports and source code. Method: We present BugTranslator, a novel deep learning-based machine translation technique composed of an attention-based recurrent neural network (RNN) Encoder-Decoder with long short-term memory cells. One RNN encodes bug reports into several context vectors that are decoded by another RNN into code tokens of buggy files. The technique studies and adopts the relevance between the extracted semantic information from bug reports and source files. Results: The experimental results show that BugTranslator outperforms a current state-of-the-art word embed- ding technique on three open-source projects with higher MAP and MRR. The results show that BugTranslator can rank actual buggy files at the second or third places on average.
Conclusion: BugTranslator distinguishes bug reports and source code into different symbolic classes and then extracts deep semantic similarity and relevance between bug reports and the corresponding buggy files to bridge the lexical gap at its source, thereby further improving the performance of bug localization.

Tue 12 Nov

10:40 - 12:20: Papers - AI and SE at Cortez 2&3
Chair(s): Kaiyuan WangGoogle, Inc.
ase-2019-papers10:40 - 11:00
Assessing the Generalizability of code2vec Token Embeddings
Kang Hong JinSchool of Information Systems, Singapore Management University, Tegawendé F. BissyandéSnT, University of Luxembourg, David LoSingapore Management University
ase-2019-papers11:00 - 11:20
Multi-Modal Attention Network Learning for Semantic Source Code Retrieval
Yao WanZhejiang University, Jingdong ShuZhejiang University, Yulei SuiUniversity of Technology Sydney, Australia, Guandong XuUniversity of Technology, Sydney, Zhou ZhaoZhejiang University, Jian WuZhejiang University, philip yuUniversity of Illinois at Chicago
ase-2019-papers11:20 - 11:40
Experience Paper: Search-based Testing in Automated Driving Control ApplicationsACM SIGSOFT Distinguished Paper Award
Christoph GladischCorporate Research, Robert Bosch GmbH, Thomas HeinzCorporate Research, Robert Bosch GmbH, Christian HeinzemannCorporate Research, Robert Bosch GmbH, Jens OehlerkingCorporate Research, Robert Bosch GmbH, Anne von VietinghoffCorporate Research, Robert Bosch GmbH, Tim PfitzerRobert Bosch Automotive Steering GmbH
ase-2019-Journal-First-Presentations11:40 - 12:00
Machine Translation-Based Bug Localization Technique for Bridging Lexical Gap
Yan XiaoDepartment of Computer Science, City University of Hong Kong, Jacky KeungDepartment of Computer Science, City University of Hong Kong, Kwabena E. BenninBlekinge Institute of Technology, SERL Sweden, Qing MiDepartment of Computer Science, City University of Hong Kong
Link to publication
ase-2019-papers12:00 - 12:10
AutoFocus: Interpreting Attention-based Neural Networks by Code Perturbation
Nghi Duy Quoc BuiSingapore Management University, Singapore, Yijun YuThe Open University, UK, Lingxiao JiangSingapore Management University
ase-2019-Demonstrations12:10 - 12:20
A Quantitative Analysis Framework for Recurrent Neural Network
Xiaoning DuNanyang Technological University, Xiaofei XieNanyang Technological University, Yi LiNanyang Technological University, Lei MaKyushu University, Yang LiuNanyang Technological University, Singapore, Jianjun ZhaoKyushu University