PCA: Memory leak detection using partial call-path analysis

Li, W; Cai, H; Sui, Y; Manz, D

PCA: Memory leak detection using partial call-path analysis

Li, W Cai, H Sui, Y

Manz, D

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Publisher:: ACM
Publication Type:: Conference Proceeding
Citation:: ESEC/FSE 2020 - Proceedings of the 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2020, pp. 1621-1625
Issue Date:: 2020-11-08

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Field	Value	Language
dc.contributor.author	Li, W
dc.contributor.author	Cai, H
dc.contributor.author	Sui, Y https://orcid.org/0000-0002-9510-6574
dc.contributor.author	Manz, D
dc.date.accessioned	2021-04-11T20:31:09Z
dc.date.available	2021-04-11T20:31:09Z
dc.date.issued	2020-11-08
dc.identifier.citation	ESEC/FSE 2020 - Proceedings of the 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2020, pp. 1621-1625
dc.identifier.isbn	9781450370431
dc.identifier.uri	http://hdl.handle.net/10453/148001
dc.description.abstract	Data dependence analysis underlies various applications in software quality assurance, yet existing frameworks/tools for this analysis commonly suffer scalability challenges. We present PCA, a static interprocedural data dependence analyzer for real-world C programs. PCA performs interprocedural points-to and data-flow analyses with a lightweight design. Most of all, it features a partial call-path (PCA) analysis that consists of optimization options to further speed up data dependence computation. As an example application of it, PCA readily supports memory leak detection, for which it helps achieve close or better performance and precision relative to the same application based on a state-of-the-art value flow analysis. In particular, it found four more memory leaks in an industry-scale system which have been fixed by the developers. Through the data dependence it computes, PCA can enable other applications (e.g., impact analysis and taint analysis).
dc.language	en
dc.publisher	ACM
dc.relation.ispartof	ESEC/FSE 2020 - Proceedings of the 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering
dc.relation.ispartof	ESEC/FSE '20: 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
dc.relation.isbasedon	10.1145/3368089.3417923
dc.rights	info:eu-repo/semantics/openAccess
dc.title	PCA: Memory leak detection using partial call-path analysis
dc.type	Conference Proceeding
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2021-04-11T20:31:07Z
pubs.publication-status	Published

Abstract:

Data dependence analysis underlies various applications in software quality assurance, yet existing frameworks/tools for this analysis commonly suffer scalability challenges. We present PCA, a static interprocedural data dependence analyzer for real-world C programs. PCA performs interprocedural points-to and data-flow analyses with a lightweight design. Most of all, it features a partial call-path (PCA) analysis that consists of optimization options to further speed up data dependence computation. As an example application of it, PCA readily supports memory leak detection, for which it helps achieve close or better performance and precision relative to the same application based on a state-of-the-art value flow analysis. In particular, it found four more memory leaks in an industry-scale system which have been fixed by the developers. Through the data dependence it computes, PCA can enable other applications (e.g., impact analysis and taint analysis).

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http://hdl.handle.net/10453/148001