A Novel Technique for Testing an Aspect Oriented Software System using Genetic and Fuzzy Clustering Algorithm

Testing of software is a worthwhile aspect of software development life cycle. Effective and efficient test cases must be designed to test the software which will reduce the testing cost, time and effort. Nowadays, testing an aspect-oriented program is becoming a challenge for the testers. This paper proposes a novel approach to generate test case scenarios for an aspect oriented program derived from “Unified Modeling Language” (UML). Combined heuristic approach using Genetic and Fuzzy Clustering Algorithm is being employed to reduce or minimize the number of test case scenarios. This paper caters to save time, cost and effort by efficiently generating optimal and minimal test sequences in order to obtain the aspectual branch coverage criteria for testing the behavior of AOP. This approach has been applied on a well-known aspect oriented software testing problem namely, “Heating kettle problem”. Effectiveness of proposed technique is justified by analyzing it with GA and Fuzzy method individually. The results show that proposed approach tremendously reduces the number of test case scenarios by satisfying aspectual branch coverage criteria.