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

Tamal Sen | Zendy; Rajib Mall | Zendy

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

Author(s) -

Tamal Sen,

Rajib Mall

Publication year - 2012

Publication title -

isrn software engineering

Language(s) - English

Resource type - Journals

eISSN - 2090-7680

pISSN - 2090-7672

DOI - 10.5402/2012/561502

Subject(s) - regression testing , computer science , test suite , component (thermodynamics) , regression analysis , software regression , data mining , selection (genetic algorithm) , regression , software , state (computer science) , model selection , test (biology) , test case , artificial intelligence , machine learning , statistics , algorithm , software quality , software system , mathematics , software development , programming language , software construction , physics , thermodynamics , paleontology , biology

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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