Publication Type
Journal Article
Version
publishedVersion
Publication Date
9-2018
Abstract
Software systems are often released without formal specifications. To deal with the problem of lack of and outdated specifications, rule-based specification mining approaches have been proposed. These approaches analyze execution traces of a system to infer the rules that characterize the protocols, typically of a library, that its clients must obey. Rule-based specification mining approaches work by exploring the search space of all possible rules and use interestingness measures to differentiate specifications from false positives. Previous rule-based specification mining approaches often rely on one or two interestingness measures, while the potential benefit of combining multiple available interestingness measures is not yet investigated. In this work, we propose a learning to rank based approach that automatically learns a good combination of 38 interestingness measures. Our experiments show that the learning to rank based approach outperforms the best performing approach leveraging single interestingness measure by up to 66%.
Keywords
Specification mining; Learning to rank; Automated software development; Software maintenance and evolution
Discipline
Programming Languages and Compilers | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Automated Software Engineering
Volume
25
Issue
3
First Page
501
Last Page
530
ISSN
0928-8910
Identifier
10.1007/s10515-018-0231-z
Publisher
Springer Verlag (Germany)
Citation
CAO, Zherui; TIAN, Yuan; LE, Bui Tien Duy; and LO, David.
Rule-based specification mining leveraging learning to rank. (2018). Automated Software Engineering. 25, (3), 501-530.
Available at: https://ink.library.smu.edu.sg/sis_research/3988
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s10515-018-0231-z