Impact Analysis of Granularity Levels on Feature Location Technique

Due to the increasing of software requirements and software features, modern software systems continue to grow in size and complexity. Locating source code entities that required to implement a feature in millions lines of code is labor and cost intensive for developers. To this end, several studies have proposed the use of Information Retrieval (IR) to rank source code entities based on their textual similarity to an issue report. The ranked source code entities could be at a class or function granularity level. Source code entities at the class-level are usually large in size and might contain a lot of functions that are not implemented for the feature. Hence, we conjecture that the class-level feature location technique requires more effort than function-level feature location technique. In this paper, we investigate the impact of granularity levels on a feature location technique. We also presented a new evaluation method using effort-based evaluation. The results indicated that function-level feature location technique outperforms class-level feature location technique. Moreover, function-level feature location technique also required 7 times less effort than class-level feature location technique to localize the first relevant source code entity. Therefore, we conclude that feature location technique at the function-level of program elements is effective in practice.