Recommending Stack Overflow Posts for Fixing Runtime Exceptions using Fa... (Teaser, ESEC/FSE 2020

Recommending Stack Overflow Posts for Fixing Runtime Exceptions using Failure Scenario Matching (Teaser, ESEC/FSE 2020)
Sonal Mahajan, Negarsadat Abolhassani, and Mukul R. Prasad
(Fujitsu Labs, USA; University of Southern California, USA; Fujitsu Labs, USA)

Abstract: Using online Q&A forums, such as Stack Overflow (SO), for guidance to resolve program bugs, among other development issues, is commonplace in modern software development practice. Runtime exceptions (RE) is one such important class of bugs that is actively discussed on SO. In this work we present a technique and prototype tool called MAESTRO that can automatically recommend an SO post that is most relevant to a given Java RE in a developer's code. MAESTRO compares the exception-generating program scenario in the developer's code with that discussed in an SO post and returns the post with the closest match. To extract and compare the exception scenario effectively, MAESTRO first uses the answer code snippets in a post to implicate a subset of lines in the post's question code snippet as responsible for the exception and then compares these lines with the developer's code in terms of their respective Abstract Program Graph (APG) representations. The APG is a simplified and abstracted derivative of an abstract syntax tree, proposed in this work, that allows an effective comparison of the functionality embodied in the high-level program structure, while discarding many of the low-level syntactic or semantic differences. We evaluate MAESTRO on a benchmark of 78 instances of Java REs extracted from the top 500 Java projects on GitHub and show that MAESTRO can return either a highly relevant or somewhat relevant SO post corresponding to the exception instance in 71% of the cases, compared to relevant posts returned in only 8% - 44% instances, by four competitor tools based on state-of-the-art techniques. We also conduct a user experience study of MAESTRO with 10 Java developers, where the participants judge MAESTRO reporting a highly relevant or somewhat relevant post in 80% of the instances. In some cases the post is judged to be even better than the one manually found by the participant.

Article: https://doi.org/10.1145/3368089.3409764

Supplementary archive: https://doi.org/10.6084/m9.figshare.11948619.v1 (Badges: Artifacts Available, Artifacts Evaluated — Functional)

Submitted to the conference by Sonal Mahajan on 2020-10-31

Video Tags: code search, static analysis, runtime exceptions, crowd intelligence, fse20main-p894-p, DOI: 10.1145/3368089.3409764, DOI: 10.6084/m9.figshare.11948619.v1, Artifacts Available, Artifacts Evaluated — Functional

Presentation at the ESEC/FSE 2020 conference, November 8–13, 2020, https://2020.esec-fse.org/
Sponsored by ACM SIGSOFT, https://www.sigsoft.org/

Twitter: https://twitter.com/fseconf
Reddit: https://www.reddit.com/r/ESECFSE