DEVELOPMENT AND VALIDATION OF AN IMPROVED TEST SELECTION AND PRIORITIZATION ALGORITHM BASED ON ACO

Abstract

Regression testing is an important and often costly software maintenance activity. Retesting the software using existing test suite whenever modifications are made to the system, in order to regain confidence in correctness of the system, is called as Regression Testing. Regression test suites are often too large to re-execute in the given time and cost constraints. Reordering of the test suite is done according to appropriate criteria like code, branch, condition and fault coverage, etc. This process is known as Test Suite Prioritization. We can also select a subset of the original test suite on the basis of some criteria, often called as Regression Test Selection. The research problem that arises from this is the choice of technique or process to be used for selecting and prioritizing according to one or more of the chosen criteria(s). Ant Colony Optimization (ACO) is one such technique that was used by Singh et al. for solving Time-Constrained Test Suite Selection and Prioritization problem using Fault Exposing Potential (FEP). In this paper, we propose improvements to the existing algorithm along with details of the time complexity of the algorithm. It was very convincing to implement the technique considering the results obtained. Implementation of the proposed algorithm has also been demonstrated. The tool was repeatedly run on sample programs by changing the time constraint criterion. The analysis shows the usefulness and effectiveness of using ACO technique for test suite selection and prioritization.