STAR: Stack Trace Based Automatic Crash Reproduction via Symbolic Execution.
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| Title: | STAR: Stack Trace Based Automatic Crash Reproduction via Symbolic Execution. |
|---|---|
| Authors: | Chen, Ning1, Kim, Sunghun1 |
| Source: | IEEE Transactions on Software Engineering. Feb2015, Vol. 41 Issue 2, p198-220. 23p. |
| Subjects: | Software failures, Computer debugging software, System failures, Open source software, Scalability |
| Abstract: | Software crash reproduction is the necessary first step for debugging. Unfortunately, crash reproduction is often labor intensive. To automate crash reproduction, many techniques have been proposed including record-replay and post-failure-process approaches. Record-replay approaches can reliably replay recorded crashes, but they incur substantial performance overhead to program executions. Alternatively, post-failure-process approaches analyse crashes only after they have occurred. Therefore they do not incur performance overhead. However, existing post-failure-process approaches still cannot reproduce many crashes in practice because of scalability issues and the object creation challenge. This paper proposes an automatic crash reproduction framework using collected crash stack traces. The proposed approach combines an efficient backward symbolic execution and a novel method sequence composition approach to generate unit test cases that can reproduce the original crashes without incurring additional runtime overhead. Our evaluation study shows that our approach successfully exploited 31 (59.6 percent) of 52 crashes in three open source projects. Among these exploitable crashes, 22 (42.3 percent) are useful reproductions of the original crashes that reveal the crash triggering bugs. A comparison study also demonstrates that our approach can effectively outperform existing crash reproduction approaches. [ABSTRACT FROM PUBLISHER] |
| Copyright of IEEE Transactions on Software Engineering is the property of IEEE Computer Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 100979675 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: STAR: Stack Trace Based Automatic Crash Reproduction via Symbolic Execution. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Ning%22">Chen, Ning</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Kim%2C+Sunghun%22">Kim, Sunghun</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Software+Engineering%22">IEEE Transactions on Software Engineering</searchLink>. Feb2015, Vol. 41 Issue 2, p198-220. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Software+failures%22">Software failures</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+debugging+software%22">Computer debugging software</searchLink><br /><searchLink fieldCode="DE" term="%22System+failures%22">System failures</searchLink><br /><searchLink fieldCode="DE" term="%22Open+source+software%22">Open source software</searchLink><br /><searchLink fieldCode="DE" term="%22Scalability%22">Scalability</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Software crash reproduction is the necessary first step for debugging. Unfortunately, crash reproduction is often labor intensive. To automate crash reproduction, many techniques have been proposed including record-replay and post-failure-process approaches. Record-replay approaches can reliably replay recorded crashes, but they incur substantial performance overhead to program executions. Alternatively, post-failure-process approaches analyse crashes only after they have occurred. Therefore they do not incur performance overhead. However, existing post-failure-process approaches still cannot reproduce many crashes in practice because of scalability issues and the object creation challenge. This paper proposes an automatic crash reproduction framework using collected crash stack traces. The proposed approach combines an efficient backward symbolic execution and a novel method sequence composition approach to generate unit test cases that can reproduce the original crashes without incurring additional runtime overhead. Our evaluation study shows that our approach successfully exploited 31 (59.6 percent) of 52 crashes in three open source projects. Among these exploitable crashes, 22 (42.3 percent) are useful reproductions of the original crashes that reveal the crash triggering bugs. A comparison study also demonstrates that our approach can effectively outperform existing crash reproduction approaches. [ABSTRACT FROM PUBLISHER] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Software Engineering is the property of IEEE Computer Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TSE.2014.2363469 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 198 Subjects: – SubjectFull: Software failures Type: general – SubjectFull: Computer debugging software Type: general – SubjectFull: System failures Type: general – SubjectFull: Open source software Type: general – SubjectFull: Scalability Type: general Titles: – TitleFull: STAR: Stack Trace Based Automatic Crash Reproduction via Symbolic Execution. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Ning – PersonEntity: Name: NameFull: Kim, Sunghun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 00985589 Numbering: – Type: volume Value: 41 – Type: issue Value: 2 Titles: – TitleFull: IEEE Transactions on Software Engineering Type: main |
| ResultId | 1 |
