Concurrently searching branches in software tests generation through multitask evolution
Multitask evolutionary computation (MT EC) has been recently identified as a potentially useful paradigm for significant real world domains. One such domain is the field of software testing. Although a number of evolutionary approaches exist already, there is a lack of strategies that can leverage knowledge from different sources to enhance the search process. In this work, we focus on branch testing and explore the capability of MT EC to guide the search by exploiting inter branch information. To the best of our knowledge, this is the first application of MT EC to real world problems with more than two tasks. Precisely, we evince that selection, together with the preference relation used to compare individuals, form a mechanism capable of achieving a concurrent search for the branches while exploiting inter branch knowledge in the process. Further, we demonstrate that the intensity of the transfer can be altered with the implemented selection. The experimental results on benchmark programs suggest that MT EC can be specially useful in situations where the budget for the search process is limited.
2016 IEEE Symposium Series on Computational Intelligence (SSCI)