Abstract
The estimated cost of software maintenance exceeds 70 percent of total software costs [1], and large portion of this maintenance expenses is devoted to regression testing. Regression testing is an expensive and frequently executed maintenance activity used to revalidate the modified software. Any reduction in the cost of regression testing would help to reduce the software maintenance cost. Test suites once developed are reused and updated frequently as the software evolves. As a result, some test cases in the test suite may become redundant when the software is modified over time since the requirements covered by them are also covered by other test cases. Due to the resource and time constraints for re-executing large test suites, it is important to develop techniques to minimize available test suites by removing redundant test cases. In general, the test suite minimization problem is NP complete. This paper focuses on proposing an effective approach for reducing the cost of regression testing process. The proposed approach is applied on real-time case study. It was found that the reduction in cost of regression testing for each regression testing cycle is ranging highly improved in the case of programs containing high number of selected statements which in turn maximize the benefits of using it in regression testing of complex software systems. The reduction in the regression test suite size will reduce the effort and time required by the testing teams to execute the regression test suite. Since regression testing is done more frequently in software maintenance phase, the overall software maintenance cost can be reduced considerably by applying the proposed approach.
Highlights
In regression testing as integration testing proceeds, number of regression tests increases and it is impractical and inefficient to re-execute every test for every program if one change occurs.Test suite reduction techniques decrease the cost of software testing by removing the redundant test cases from the test suite while still producing a reduced set of tests that covers the same level of code coverage as the original suite.Optimizing the cost of the regression testing without compromising the fault exposing capability is always challenging for the testing team
It is proposed by Harrold, Gupta and Soffa to test suite reduction “Selecting a representative set of test cases from a test suite, providing the same coverage as the entire test suite” that has received considerable attention
Given a set T of test cases {t1, t2, t3, ...., tn}, a set of testing requirements {r1, r2,· ·,rm} that must be covered to provide the desired coverage of the program, and the information about the testing requirements exercised by each test case in T, the test suite minimization problem focus on finding a minimal cardinality subset of T that exercises the same set of requirements as those exercised by the unminimized test suite T
Summary
In regression testing as integration testing proceeds, number of regression tests increases and it is impractical and inefficient to re-execute every test for every program if one change occurs. The traditional HGS algorithm is one of the most common algorithms aiming to reduce the cost of regression testing It is proposed by Harrold, Gupta and Soffa to test suite reduction “Selecting a representative set of test cases from a test suite, providing the same coverage as the entire test suite” that has received considerable attention. The traditional HGS algorithm suffers from some disadvantages since no clear reason is shown for the initial www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 5, No 8, 2014 choice of the test cases as starting point It did not assure the cover all tests with all possible cases of all the selection statements
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