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ISSTA 2020
Sat 18 - Wed 22 July 2020
Tue 21 Jul 2020 12:10 - 12:30 at Zoom - BUG LOCALIZATION AND TEST ISOLATION Chair(s): Mattia Fazzini

Despite all efforts to avoid bugs, software sometimes crashes in the field, leaving crash traces as the only information to localize the problem. Prior approaches on localizing where to fix the root cause of a crash do not scale well to ultra-large scale, heterogeneous code bases that contain millions of code files written in multiple programming languages. This paper presents Scaffle, the first scalable bug localization technique, which is based on the key insight to divide the problem into two easier sub-problems. First, a trained machine learning model predicts which lines of a raw crash trace are most informative for localizing the bug. Then, these lines are fed to an information retrieval-based search engine to retrieve file paths in the code base, predicting which file to change to address the crash. The approach does not make any assumptions about the format of a crash trace or the language that produces it. We evaluate Scaffle with tens of thousands of crash traces produced by a large-scale industrial code base that contains millions of possible bug locations and that powers tools used by billions of people. The results show that the approach correctly predicts the file to fix for 40% to 60% (50% to 70%) of all crash traces within the top-1 (top-5) predictions. Moreover, Scaffle improves over several baseline approaches, including an existing classification-based approach, a scalable variant of existing information retrieval-based approaches, and a set of hand-tuned, industrially deployed heuristics.

Tue 21 Jul
Times are displayed in time zone: Tijuana, Baja California change

12:10 - 13:10
Chair(s): Mattia FazziniUniversity of Minnesota

Public Live Stream/Recording. Registered participants should join via the Zoom link distributed in Slack.

Scaffle: Bug Localization on Millions of Files
Technical Papers
Michael PradelUniversity of Stuttgart, Vijayaraghavan MuraliFacebook, Inc., Rebecca QianFacebook, Inc., Mateusz MachalicaFacebook, Inc., Erik Meijer, Satish ChandraFacebook
DOI Media Attached
Abstracting Failure-Inducing InputsArtifacts Evaluated – ReusableArtifacts AvailableArtifacts Evaluated – FunctionalACM SIGSOFT Distinguished Paper Award
Technical Papers
Rahul GopinathCISPA Helmholtz Center for Information Security, Alexander KampmannCISPA Helmholtz Center for Information Security, Nikolas HavrikovCISPA Helmholtz Center for Information Security, Ezekiel O. SoremekunCISPA Helmholtz Center for Information Security, Andreas ZellerCISPA Helmholtz Center for Information Security
DOI Pre-print Media Attached
Debugging the Performance of Maven’s Test Isolation: Experience Report
Technical Papers
Pengyu NieThe University of Texas at Austin, Ahmet CelikFacebook, Matthew Coley, Aleksandar Milicevic, Jonathan BellNortheastern University, Milos GligoricThe University of Texas at Austin