Contextual Recommendation of Relevant Program Elements in an Interactive Feature Location Process

When performing feature location tasks, developers often need to explore a large number of program elements by following a variety of clues (such as program element location, dependency, and content). As there are often complex relationships among program elements, it is likely that some relevant program elements are omitted, especially when the implementations for a feature or concern scatter across several source files. In this paper, we propose an approach for recommending potentially relevant program elements in an interactive feature location process. The two characteristics of our approach are: considering ongoing user context (i.e., confirmed or negated elements) in an interactive manner; performing an example-based reasoning to determine relevance of program elements. Based on an initial set of program elements confirmed by developers, our approach recommends additional program elements in an iterative process, in which developers can confirm relevant results, negate irrelevant results, and obtain an updated recommendation list. We have implemented our approach as an Eclipse plug-in called RecFL and conducted an experimental study. The results show that the participants using RecFL achieved a much better performance in their feature location tasks than the participants not using RecFL. The participants using RecFL also felt it easier to accomplish their feature location tasks with the support of RecFL.