Multiobjective Genetic Algorithm for Class Testing using OCL Class Contract Specifications: A Framework

Abstract

It has been a software trend to build large-scale complex systems with high reliability. Due to the size of the software and the dynamic requirements of the stakeholders, it becomes hard to test those software systems manually. This may lead the software to fatal failures and cause irrecoverable catastrophic damage. To be safe, the software system must be investigated thoroughly before it is too late. Test sequence generation for Unified Modeling Language (UML) class models from their semiformal Object Constraint Language specifications can be helpful in identifying the defects in the early phase of the software life cycle. The existing approaches suffer from inherent problems of exhaustive exploration of finite state machines (infeasible paths, exponential number of test sequences, and uncertainty of completion of testing). Evolutionary algorithms can greatly help by optimizing the test sequences to get optimal coverage, minimal cost, and higher quality. The proposed approach helps us to improve the testing of Unified Modeling Language (UML) model-based software, by testing the conformance to semiformal class operation contract specifications (specified in the form of Object Management Group (OMG) standard and Object Constraint Language (OCL) semiformal language). The presented research achieved two main goals: (1) automation of testing process and conformance to standards of the current technique of test sequence generation, bridging the gap between the research and industry; (2) improvement in the state of the art approach through the application of multiobjective genetic algorithms (MOGAs). A case study along with the results achieved through the proposed technique is presented as well, clearly reflecting the significance of the proposed research.