Hongyu Kuang, Hui Gao, Hao Hu, Xiaoxing Ma, Jian Lü, Patrick Mäder, Alexander Egyed,
"Using frugal user feedback with closeness analysis on code to improve IR-based traceability recovery"
, in Yann-Gael Gueheneuc and Foutse Khomh and Federica Sarro: Proceedings of the 27th International Conference on Program Comprehension, ICPC 2019, Montreal, QC, Canada, May 25-31, 2019, IEEE / ACM, Seite(n) 369-379, 5-2019
Using frugal user feedback with closeness analysis on code to improve IR-based traceability recovery
Sprache des Titels:
Proceedings of the 27th International Conference on Program Comprehension, ICPC 2019, Montreal, QC, Canada, May 25-31, 2019
Traceability recovery allows developers to extract and comprehend the trace links among software artifacts (e.g., requirements and code). These trace links can provide important support to software maintenance and evolution tasks. Information Retrieval (IR) is now widely accepted as the key technique of semi-automatic tools to recover candidate trace links based on textual similarities among artifacts. However, the vocabulary mismatch problem between different artifacts hinders the performance of these IR-based approaches. Thus, a growing body of enhancing strategies were proposed based on user feedback. They allow to adjust the textual similarities of candidate links after users accept or reject part of these links. Recently, several approaches successfully used this strategy to improve the performance of IR-based traceability recovery. However, these approaches require a large amount of user feedback, which is infeasible in practice. In this paper, we propose to improve IR-based traceability recovery by introducing only a small amount of user feedback into the closeness analysis on call and data dependencies in code. Specifically, our approach iteratively asks users to verify a chosen candidate link based on the quantified functional similarity for each code dependency (called closeness) and the generated IR values. The verified link is then used as the input to re-rank the unverified candidate links. An empirical evaluation based on five real-world systems shows that our approach can outperform four baseline approaches by using only a small amount of user feedback.