Identifying Crashing Fault Residence Based on Cross Project Model

Zhou Xu, Tao Zhang, Yifeng Zhang, Yutian Tang, Jin Liu, Xiapu Luo, Jacky Keung, Xiaohui Cui

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Analyzing the crash reports recorded upon software crashes is a critical activity for software quality assurance. Predicting whether or not the fault causing the crash (crashing fault for short) resides in the stack traces of crash reports can speed-up the program debugging process and determine the priority of the debugging efforts. Previous work mostly collected label information from bug-fixing logs, and extracted crash features from stack traces and source code to train classification models for the Identification of Crashing Fault Residence (ICFR) of newly-submitted crashes. However, labeled data are not always fully available in real applications. Hence the classifier training is not always feasible. In this work, we make the first attempt to develop a cross project ICFR model to address the data scarcity problem. This is achieved by transferring the knowledge from external projects to the current project via utilizing a state-of-the-art Balanced Distribution Adaptation (BDA) based transfer learning method. BDA not only combines both marginal distribution and conditional distribution across projects but also assigns adaptive weights to the two distributions for better adjusting specific cross project pair. The experiments on 7 software projects show that BDA is superior to 9 baseline methods in terms of 6 indicators overall.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 30th International Symposium on Software Reliability Engineering, ISSRE 2019
EditorsKatinka Wolter, Ina Schieferdecker, Barbara Gallina, Michel Cukier, Roberto Natella, Naghmeh Ivaki, Nuno Laranjeiro
PublisherIEEE Computer Society
Pages183-194
Number of pages12
ISBN (Electronic)9781728149813
DOIs
Publication statusPublished - Oct 2019
Event30th IEEE International Symposium on Software Reliability Engineering, ISSRE 2019 - Berlin, Germany
Duration: 28 Oct 201931 Oct 2019

Publication series

NameProceedings - International Symposium on Software Reliability Engineering, ISSRE
Volume2019-October
ISSN (Print)1071-9458

Conference

Conference30th IEEE International Symposium on Software Reliability Engineering, ISSRE 2019
CountryGermany
CityBerlin
Period28/10/1931/10/19

Keywords

  • Crashing fault
  • Cross project model
  • Stack trace
  • Transfer learning

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality

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