Estimating the regression test case selection probability using fuzzy rules

Software maintenance is performed regularly for enhancing and adapting the functionalities of the existing software, which modifies the software and breaks the previously verified functionalities. This sets a requirement for software regression testing, making it a necessary maintenance activity. As the evolution of software takes place the size of the test suite tends to grow, which makes it difficult to execute the entire test suite in a time constrained environment. There are many existing techniques for regression test case selection. Some are based on dataflow analysis technique, slicing-based technique, bio-inspired techniques, and genetic algorithm based techniques. This paper gives a regression test case selection technique based on fuzzy model, which reduces the size of the test suite by selecting test cases from existing test suite. The test cases, which are necessary for validating the recent changes in the software and have the ability to find the faults and cover maximum coding under testing in minimum time, are selected. A fuzzy model is designed which takes three parameters namely code covered, execution time and faults covered as input and produces the estimation for the test case selection probability as very low, low, medium, high and very high.