An important application of information retrieval technology is software change impact analysis. Existing information retrieval-based change impact analysis methods select a single method to transform the source code corpus into vectors in a process known as indexing. The single method is chosen from two primary methods, known as the bag-of-words and word embedding models, each having their specific advantages and disadvantages. The bag-of-words model records every word in the source code but ignores contextual information in the corpus. The word embedding model records the contextual information but loses detail for individual words. To address this problem, we propose a structure-driven method for information retrieval-based change impact analysis (named SDM-CIA). SDM-CIA integrates the bag-of-words and word embedding models based on the software’s structure. Our experiments using a standard benchmark shows that when compared with the existing methods, SDM-CIA improves on precision performance, recall performance, F-score performance, and MRR performance by an average of 3.65%, 3.82%, 3.6%, and 10.28%, respectively. Our experiments confirm the effectiveness of SDM-CIA.
Blending Conceptual and Evolutionary Couplings to Support Change Impact Analysis in Source Code
