Refinement Techniques in Mining Software Behavior

Abstract

Mining software behavior has been well studied to assist in numerous software engineering tasks for the past two decades. Two research topics which received much attention are specification mining and statistical debugging. Among the execution traces analyzed by both studies, there exist a significant number of useless elements. To enhance the efficiency and effectiveness of software behavior mining, refinement techniques are required to remove unwanted elements from raw execution traces. This dissertation presents a specific systematic refinement technique for each of the above two studies. For specification mining, we propose a semantics-directed specification mining framework which filters out the semantically irrelevant events from execution traces before mining and thus discover semantically significant specifications. For statistical debugging, we devise a novel hierarchical instrumentation (HI) technique to refine the execution traces. Based on HI, we safely and effectively prune away unnecessary instrumentation, and thus greatly reduce the overhead of statistical debugging.