Searching for better configurations: a rigorous approach to clone evaluation

Abstract

Clone detection finds application in many software engineering activities such as comprehension and refactoring. However, the confounding configuration choice problem poses a widely-acknowledged threat to the validity of previous empirical analyses. We introduce desktop and parallelised cloud-deployed versions of a search based solution that finds suitable configurations for empirical studies. We evaluate our approach on 6 widely used clone detection tools applied to the Bellon suite of 8 subject systems. Our evaluation reports the results of 9.3 million total executions of a clone tool; the largest study yet reported. Our approach finds significantly better configurations (p < 0.05) than those currently used, providing evidence that our approach can ameliorate the confounding configuration choice problem.