SCAN: an approach to label and relate execution trace segments

Program comprehension is a prerequisite to any maintenance and evolution task. In particular, when performing feature location, developers perform program comprehension by abstracting software features and identifying the links between high-level abstractions (features) and program elements. We present Segment Concept AssigNer (SCAN), an approach to support developers in feature location. SCAN uses a search-based approach to split execution traces into cohesive segments. Then, it labels the segments with relevant keywords and, finally, uses formal concept analysis to identify relations among segments. In a first study, we evaluate the performances of SCAN on six Java programs by 31 participants. We report an average precision of 69% and a recall of 63% when comparing the manual and automatic labels and a precision of 63% regarding the relations among segments identified by SCAN. After that, we evaluate the usefulness of SCAN for the purpose of feature location on two Java programs. We provide evidence that SCAN (i) identifies 69% of the gold set methods and (ii) is effective in reducing the quantity of information that developers must process to locate features—reducing the number of methods to understand by an average of 43% compared to the entire execution traces