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2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) (2017)
Urbana, IL, USA
Oct. 30, 2017 to Nov. 3, 2017
ISBN: 978-1-5386-3976-4
pp: 147-152
Yan Sun , University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
Celia Chen , Department of Computer Science, Occidental College, Los Angeles, CA
Qing Wang , University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
Barry Boehm , Department of Computer Science, Occidental College, Los Angeles, CA
ABSTRACT
Links between issue reports and corresponding fix commits are widely used in software maintenance. The quality of links directly affects maintenance costs. Currently, such links are mainly maintained by error-prone manual efforts, which may result in missing links. To tackle this problem, automatic link recovery approaches have been proposed by building traditional classifiers with positive and negative links. However, these traditional classifiers may not perform well due to the inherent characteristics of missing links. Positive links, which can be used to build link recovery model, are quite limited as the result of missing links. Since the construction of negative links depends on the number of positive links in many existing approaches, the available negative links also become restricted. In this paper, we point out that it is better to consider the missing link problem as a model learning problem by using positive and unlabeled data, rather than the construction of traditional classifier. We propose PULink, an approach that constructs the link recovery model with positive and unlabeled links. Our experiment results show that compared to existing state-of-the-art technologies built on traditional classifier, PULink can achieve competitive performance by utilizing only 70% positive links that are used in those approaches.
INDEX TERMS
Feature extraction, Indexes, Metadata, Training, Software maintenance
CITATION

Y. Sun, C. Chen, Q. Wang and B. Boehm, "Improving missing issue-commit link recovery using positive and unlabeled data," 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE), Urbana, IL, USA, 2017, pp. 147-152.
doi:10.1109/ASE.2017.8115627
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