Temporal White Box Testing Using Evolutionary Algorithm

Software testing is an investigation conducted to provide stakeholders with information about the quality of the product or service under test. Software testing can also provide an objective, independent view of the software to allow the business to appreciate and understand the risks of software implementation. Test techniques include, but are not limited to, the process of executing a program or application with the intent of finding software bugs. Embedded computer systems should fulfill real-time requirements which need to be checked in order to assure system quality. This paper stands to propose some ideas for testing the temporal behavior of real-time systems. The goal is to achieve white-box temporal testing using evolutionary techniques to detect system failures in reasonable time and little effort.

Testing is the most important Quality Assurance (QA) measure which consumes a significant portion of budget, time and effort in the development process. For real time systems, temporal testing is as crucial as functional testing. An important activity in dynamic testing is the test case design. Evolutionary testing has shown promising results for the automation of test case design process at a reasonable computational cost. Evolutionary white-box software testing has been extensively researched but is not yet applied in industry. In order to investigate the reasons for this, we evaluated a prototype version of a tool, representing the state-of-the-art for evolutionary structural testing, which is targeted at industrial use.. McMinn provides a survey on search based software test data generation. My future work include comparing the random testing and evolutionary testing and finding out the better result between the two and how temporal white box testing is carried out with the help of evolutionary algorithm. In this paper, a software measure will be introduced which estimates the test effort for every test goal of evolutionary white-box testing. With the aid of this software measure, it will be possible to individually adjust the termination criterion for every sub-goal. Experiments will show whether or not this increases the effectiveness of evolutionary white-box testing. We have developed a novel algorithm for generating test cases for the full system which achieve pairwise coverage of the sub-operations. We have evaluated the algorithm using a case study, which indicates the practicality and effectiveness of the approach.