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ChangeLocator: locate crash-inducing changes based on crash reports

Authors:

Shing-Chi Cheung,

Hongyu ZhangAuthors Info & Claims

Empirical Software Engineering, Volume 23, Issue 5

Pages 2866 - 2900

https://doi.org/10.1007/s10664-017-9567-4

Published: 01 October 2018 Publication History

Abstract

Software crashes are severe manifestations of software bugs. Debugging crashing bugs is tedious and time-consuming. Understanding software changes that induce a crashing bug can provide useful contextual information for bug fixing and is highly demanded by developers. Locating the bug inducing changes is also useful for automatic program repair, since it narrows down the root causes and reduces the search space of bug fix location. However, currently there are no systematic studies on locating the software changes to a source code repository that induce a crashing bug reflected by a bucket of crash reports. To tackle this problem, we first conducted an empirical study on characterizing the bug inducing changes for crashing bugs (denoted as crash-inducing changes). We also propose ChangeLocator, a method to automatically locate crash-inducing changes for a given bucket of crash reports. We base our approach on a learning model that uses features originated from our empirical study and train the model using the data from the historical fixed crashes. We evaluated ChangeLocator with six release versions of Netbeans project. The results show that it can locate the crash-inducing changes for 44.7%, 68.5%, and 74.5% of the bugs by examining only top 1, 5 and 10 changes in the recommended list, respectively. It significantly outperforms the existing state-of-the-art approach.

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Cited By

Zhang ZHao YChen WZou XLi XLi HZhai YQian ZLau BBalzarotti DXu W(2024)SymBisectProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699040(2493-2510)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.5555/3698900.3699040
Gu KZhang YCao JTan XYang Md'Amorim M(2024)How Well Industry-Level Cause Bisection Works in Real-World: A Study on Linux KernelCompanion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering10.1145/3663529.3663828(62-73)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3663529.3663828
Medeiros MKulesza UCoelho RBonifacio RTreude CBarbosa ERoychoudhury APaiva AAbreu RStorey MAniche MNagappan N(2024)The Impact Of Bug Localization Based on Crash Report Mining: A Developers' PerspectiveProceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice10.1145/3639477.3639730(13-24)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639477.3639730
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1. Software and its engineering
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cover image Empirical Software Engineering

Empirical Software Engineering Volume 23, Issue 5

October 2018

551 pages

ISSN:1382-3256

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Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

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Published: 01 October 2018

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Zhang ZHao YChen WZou XLi XLi HZhai YQian ZLau BBalzarotti DXu W(2024)SymBisectProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699040(2493-2510)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.5555/3698900.3699040
Gu KZhang YCao JTan XYang Md'Amorim M(2024)How Well Industry-Level Cause Bisection Works in Real-World: A Study on Linux KernelCompanion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering10.1145/3663529.3663828(62-73)Online publication date: 10-Jul-2024
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Medeiros MKulesza UCoelho RBonifacio RTreude CBarbosa ERoychoudhury APaiva AAbreu RStorey MAniche MNagappan N(2024)The Impact Of Bug Localization Based on Crash Report Mining: A Developers' PerspectiveProceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice10.1145/3639477.3639730(13-24)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639477.3639730
Wimalasooriya CLicorish Sda Costa DMacDonell S(2024)Just-in-Time crash prediction for mobile appsEmpirical Software Engineering10.1007/s10664-024-10455-729:3Online publication date: 8-May-2024
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Du YYu ZChandra SBlincoe KTonella P(2023)Pre-training Code Representation with Semantic Flow Graph for Effective Bug LocalizationProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3616338(579-591)Online publication date: 30-Nov-2023
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