Parameter tuning of evolutionary algorithm by Meta-EAs for WCET analysis

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. The disadvantage of evolutionary testing is that its time consuming and it depends on the parameter settings. Evolutionary algorithms can be used to find the optimal parameter settings of another evolutionary algorithm. In this research paper, a Meta level Evolutionary Algorithm (Meta-EA) is utilized to tune the parameters of evolutionary algorithm for Worst Case Execution Time (WCET) analysis. A number of experiments have been conducted for analysis using X32 (32-bit soft processor core implemented on FPGA) as the target hardware. Famous sorting algorithms have been used as programs under test for these experiments. Results have shown an average improvement of 25% in finding WCET by an evolutionary algorithm with tuned parameters compared to evolutionary algorithm with standard parameters. Furthermore, performance gap was found to be increasing with increase in test input size.