Abstract

Test Case Optimization and Prioritization techniques have always had an inevitable role in Regression Testing and related activities. Regression testing of an application even of medium complexity requires repeated execution of Test Cases for every minor change in the requirements. In Agile environments, where the software requirements are volatile, even a trivial change in the application increases the size of the existing Test suite. Prioritizing test cases in a test suite acuminates the Testing process, by detecting faults early in the process. Here, the Test cases are prioritized by a rank based method; depending on the how early they are unveiled during the Testing process. This ranking is obtained using Swarm based intelligence methods. The base algorithm uses the group foraging behavior of Escherichia coli (E-Coli) bacteria present in the human intestine. This social foraging behaviour of E.coli bacteria has been used to solve complex optimization problems. The Bacterial Foraging optimization (BFOA) algorithm prioritizes the Test cases, resulting in an optimized Test suite that minimizes the overall number of Test cases and eliminating the test cases that are obsolete. In the proposed system, the nodes that represent the main functionalities are identified using the social foraging behavior of E.Coli. The objectives in this research are maximizing the statement coverage and fault coverage for getting a test suite of prioritized test cases that reveals the bug at the initial stage of testing itself. The swarm intelligence based optimization approach is used in this paper to attain the optimal results in test case prioritization by identifying the critical areas of the Software Under Test (SUT) that requires code and fault coverage. In this research, based on the BFO approach, an optimal result is obtained when compared to other Swarm Intelligence Algorithms.