Increasing the efficiency of search-based unit test generation using parameter control

Abstract

Automatically generating test suites with high coverage is of great importance to software engineers, but this process is hindered by the vast amount of parameters the tools use to generate tests. Developers usually lack knowledge about the workings of the tools that generate test suites to set the parameters to optimal values, and the optimal values usually change during runtime. Parameter Control automatically adapts parameters during test generation, and has shown to help solve this problem in other areas. To investigate any improvements parameter control could have in search-based generation of test suites, we adapted multiple methods of controlling mutation and crossover rate in EvoSuite, a tool that automatically generates unit test suites. Upon evaluation, clear benefits to controlling parameters were found, but surprisingly, controlling some parameters can sometimes be more harmful to the search than beneficial through increased computation costs.