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Software regression testing is one of the most critical phases of software development life cycle, used by developers with the intent of detecting new faults to validate modified software prior to delivery to the customer. To validate updated features, new test cases are generated by the testers which increment the test suite size automatically. The resulting test suite may contain obsolete, redundant, and ambiguous test cases. Therefore, there is a strong requirement of an intelligent testing approach to reduce the test suite size by removing those unessential test cases economically. This paper proposed an interesting approach, which involves the combination of regression testing techniques: minimization, and prioritization both. The main focus is on multiple regression activities with multiple criteria rather than using only single activity to produce an optimal solution. In this paper clustering approach is also considered, which could simplify and enhance the minimization and prioritization task. To evaluate the effectiveness of the strategy, we performed an experimental investigation together with an eminent heuristic Harrold Gupta and Soffa (HGS), considering the testing measures of the minimized test suite size and fault coverage. The results show that, similarity-based greedy approach with multiple coverage criteria can be quite effective in terms of fault detection loss of reduced test suite without much affecting the percentage of suite size reduction.

A New Similarity-Based Greedy Approach for Generating Effective Test Suite