Regression test selection for C++ software

Rothermel, Gregg; Harrold, Mary Jean; Dedhia, Jeinay

doi:10.1002/1099-1689(200006)10:2<77::aid-stvr197>3.0.co;2-e

Cited by 121 publications

(92 citation statements)

References 40 publications

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“…The cost-effectiveness of specific techniques under these four methodologies varies with characteristics of test suites [11,38,39]. One prominent factor in this variance involves the way in which test inputs are composed into test cases within a test suite.…”

Section: Introductionmentioning

confidence: 99%

On test suite composition and cost-effective regression testing

Rothermel

Elbaum

Malishevsky

et al. 2004

ACM Trans. Softw. Eng. Methodol.

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Regression testing is an expensive testing process used to re-validate software as it evolves. Various methodologies for improving regression testing processes have been explored, but the cost-effectiveness of these methodologies has been shown to vary with characteristics of regression test suites. One such characteristic involves the way in which test inputs are composed into test cases within a test suite. This article reports the results of controlled experiments examining the effects of two factors in test suite composition -test suite granularity and test input grouping -on the costs and benefits of several regression-testing-related methodologies: retest-all, regression test selection, test suite reduction, and test case prioritization. These experiments consider the application of several specific techniques, from each of these methodologies, across ten releases each of two substantial software systems, using seven levels of test suite granularity and two types of test input grouping. The effects of granularity, technique, and grouping on the cost and fault-detection effectiveness of regression testing under the given methodologies are analyzed. This analysis shows that test suite granularity significantly affects several cost-benefit factors for the methodologies considered, while test input grouping has limited effects. Further, the results expose essential tradeoffs affecting the relationship between test suite design and regression testing cost-effectiveness, with several implications for practice.

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Section: Introductionmentioning

confidence: 99%

On test suite composition and cost-effective regression testing

Rothermel

Elbaum

Malishevsky

et al. 2004

ACM Trans. Softw. Eng. Methodol.

Self Cite

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“…Rothermel etal. [14] suggested a regression test selection technique for use with object-oriented software; the technique constructs graph representations for software, and uses these graphs to select test cases. Reference [15] presented a methodology and a tool to support test selection from regression test suites based on change analysis in object-oriented designs.…”

Section: Related Workmentioning

confidence: 99%

Extracting The Minimized Test Suite for Revised Simulink/Stateflow Model

Park¹,

Park²,

Chung³

et al. 2017

IJAIA

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“…It combines the features of Class Control Flow Graph [25] and Finite State Machine [26] representation of the component. They generated black box test cases based on the CBSFG constructed from the component-based system.…”

Section: Comparison With Related Workmentioning

confidence: 99%

“…They generated black box test cases based on the CBSFG constructed from the component-based system. They also proposed a test selection technique based on the technique proposed by Rothermel et al [25]. However, it is not clear how application developers can obtain the necessary information for constructing the CBSFG when source code is unavailable.…”

Section: Comparison With Related Workmentioning

confidence: 99%

State-Model-Based Regression Test Reduction for Component-Based Software

Sen

Mall

2012

ISRN Software Engineering

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We present a novel regression test selection approach based on analysis of state and dependence models of components. Our technique targets to select a smaller regression test suite compared to the pure dependence-based RTS approaches while maintaining the fault revealing effectiveness. In our approach, after a modification, control and data dependencies are analyzed to identify the potentially affected statements. Subsequently, the state model of the component is analyzed to compute a precise publishable change information to support efficient regression test selection by the application developers.

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Regression test selection for C++ software

Cited by 121 publications

References 40 publications

On test suite composition and cost-effective regression testing

On test suite composition and cost-effective regression testing

Extracting The Minimized Test Suite for Revised Simulink/Stateflow Model

State-Model-Based Regression Test Reduction for Component-Based Software

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