Back to Search Start Over

Rule-based specification mining leveraging learning to rank.

Authors :
Cao, Zherui
Tian, Yuan
Le, Tien-Duy B.
Lo, David
Source :
Automated Software Engineering; Sep2018, Vol. 25 Issue 3, p501-530, 30p
Publication Year :
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 To deal with the problem of lack of and outdated specifications, rule-based specification mining approaches have been proposed.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%. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09288910
Volume :
25
Issue :
3
Database :
Complementary Index
Journal :
Automated Software Engineering
Publication Type :
Academic Journal
Accession number :
131051477
Full Text :
https://doi.org/10.1007/s10515-018-0231-z