An empirical study of BM25 and BM25F based feature location techniques

Feature location is a software comprehension activity which aims at identifying source code entities that implement functionalities. Manual feature location is a labor-insensitive task, and developers need to find the target entities from thousands of software artifacts. Recent research has developed automatic and semiautomatic methods mainly based on Information Retrieval (IR) techniques to help developers locate the entities which are textually similar to the feature. In this paper, we focus on individual IR-based methods and try to find a suitable IR technique for feature location, which could be chosen as a part of hybrid methods to achieve good performance. We present two feature location approaches based on BM25 and its variant BM25F algorithm. We compared the two algorithms to the Vector Space Model (VSM), Unigram Model (UM), and Latent Dirichlet Allocation (LDA) on four open source projects. The result shows that BM25 and BM25F are consistently better than other IR methods such as VSM, UM and LDA on the four selected software systems in their best configurations respectively.