Model driven test case generation and optimization using adaptive cuckoo search algorithm

Abstract

Software testing is leading toward automation that reduces the effort to find errors or bugs. The identification of test cases and its critical domain requirements is done with generation of test cases. The brooding characteristic of the cuckoo bird is explained through the adaptive cuckoo search meta-heuristic algorithm (ACSA) that further narrates that host nest is used by the cuckoo bird for laying their eggs and the next generation also sees the best quality eggs from the host bird’s nest. This paper focuses on the adoption of ACSA for analysis, generation, and optimization of random test cases. In addition to that, the present work also explains the model driven approach to automatically generate and optimize the test cases with the help of unified modeling language diagram like sequence diagram. Then, the respective sequence diagram is converted into a sequence diagram graph that shows the flow of sequences being produced. Thereafter, it is optimized using ACSA by taking a case study of withdrawal operation of ATM transaction. The said approach is also evaluated in terms of efficiency and usefulness for generating the test cases through simulated experiments. In addition to that, the projected approach also identifies the operational faults as well as message faults.