Publication Type

Journal Article

Version

acceptedVersion

Publication Date

3-2023

Abstract

Over the past few years, SMT string solvers have found their applications in an increasing number of domains, such as program analyses in mobile and Web applications, which require the ability to reason about string values. A series of research has been carried out to find quality issues of string solvers in terms of its correctness and performance. Yet, none of them has considered the performance regressions happening across multiple versions of a string solver. To fill this gap, in this paper, we focus on solver performance regressions (SPRs), i.e., unintended slowdowns introduced during the evolution of string solvers. To this end, we develop SPRFinder to not only generate test cases demonstrating SPRs, but also localize the probable causes of them, in terms of commits. We evaluated the effectiveness of SPRFinder on three state-of-the-art string solvers, i.e., Z3Seq, Z3Str3, and CVC4. The results demonstrate that SPRFinder is effective in generating SPR-inducing test cases and also able to accurately locate the responsible commits. Specifically, the average running time on the target versions is 13.2 slower than that of the reference versions. Besides, we also conducted the first empirical study to peek into the characteristics of SPRs, including the impact of random seed configuration for SPR detection, understanding the root causes of SPRs, and characterizing the regression test cases through case studies.

Keywords

Computer Bugs, Testing, Fuzzing, Codes, Runtime, Location Awareness, Cognition, SMT String Solver, Performance Regression, SPR Finder

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Transactions on Software Engineering

Volume

49

Issue

3

First Page

947

Last Page

961

ISSN

0098-5589

Identifier

10.1109/TSE.2022.3168373

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TSE.2022.3168373

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