Traceability establishes trace links among software artifacts (e.g., requirements and code) based on whether two artifacts relate to the same part of system functionalities. These trace links are valuable for software development process, but are difficult to obtain manually. To cope with the costly and fallible manual recovery, researchers proposed many automated approaches that help to recover trace links through the textual similarities among software artifacts, such as approaches based on Information Retrieval (IR). However, the low quality and the low quantity of artifact texts negatively impact the calculated textual similarities, thus greatly hindering the performance of IR-based approaches. In this study, we propose to extract co-occurred word pairs from the text structures of both requirements and code (i.e., consensual biterms) to improve IR-based traceability recovery. Specifically, we first collect a set of biterms based on the part-of-speech of requirement texts, and then filter them through the code texts. We then use these consensual biterms to both enrich the input corpus for IR techniques and enhance the calculations of IR values. An empirical evaluation based on nine real-world systems shows that our approach can not only outperform baseline approaches, but also achieve a significant complementary effect with other enhancing strategies from different perspectives.

Hui Gao

Nanjing University

China

Hongyu Kuang

Nanjing University

China

Kexin Sun

Nanjing University

Xiaoxing Ma

Nanjing University

China

Alexander Egyed

Johannes Kepler University Linz

Austria

Patrick Mäder

Technische Universität Ilmenau

Germany

Guoping Rong

Nanjing University

Dong Shao

Nanjing University

He Zhang

Nanjing University

China