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S. Yoo and M. Harman, “Regression Testing Minimization, Selection, and Prioritization: A Survey,” Software Testing, Verification, and Reliability, Vol. 22, No. 2, 2010, pp. 67-120. doi:10.1002/stvr.430

has been cited by the following article:

• TITLE: Test Selection on Extended Finite State Machines with Provable Guarantees

  AUTHORS: Bo Guo, Mahadevan Subramaniam

  KEYWORDS: Formal Methods; Model-Based Software Testing; Regression Testing; Extended Finite State Machines

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.6 No.9, September 20, 2013

  ABSTRACT: Building high confidence regression test suites to validate new system versions is a challenging problem. A modelbased approach to build a regression test suite from a given test suite is described. The generated test suite includes every test that will traverse a change performed to produce the new version, and consists of only such tests to reduce the testing costs. Finite state machines extended with typed variables (EFSMs) are used to model systems and system changes are mapped to EFSM transition changes adding/deleting/replacing EFSM transitions and states. Tests are a sequence of input and expected output messages with concrete parameter values over the supported data types. An invariant is formulated to characterize tests whose runtime behavior can be accurately predicted by analyzing their descriptions along with the model. Incremental procedures to efficiently evaluate the invariant and to select tests for regression are developed. Overlaps among the test descriptions are exploited to extend the approach to simultaneously select multiple tests to reduce the test selection costs. Effectiveness of the approach is demonstrated by applying it to several protocols, Web services, and model programs extracted from a popular testing benchmark. Our experimental results show that the proposed approach is economical for regression test selection in all these examples. For all these examples, the proposed approach is able to identify all tests exercising changes more efficiently than brute-force symbolic evaluation.