Clustering and recommending collections of code relevant to tasks

摘要

When performing software evolution tasks, programmers spend a significant amount of time exploring the code base to find methods, fields or classes that are relevant to the task at hand. We propose a new clustering approach called NavClus to recommend collections of code relevant to tasks. By gradually aggregating navigation sequences from programmers' interaction history, NavClus clusters pieces of code that are contextually related. The resulting clusters become the basis for NavClus to recommend collections of code that are likely to be relevant to the programmer's given task. We compare NavClus and TeamTracks, the state of the art code recommender for sharing navigation data among programmers. The results show that NavClus recommends pieces of code relevant to tasks considerably better than TeamTracks.