Rule-based specification mining leveraging learning to rank

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 To deal with the problem of lack of and outdated specifications, rule-based specification mining approaches have been proposed.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,...

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Main Authors: CAO, Zherui, TIAN, Yuan, LE, Bui Tien Duy, LO, David
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Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/3988
https://ink.library.smu.edu.sg/context/sis_research/article/4990/viewcontent/Rule_based_specification_mining_leveraging_learning_to_rank_afv.pdf
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spelling sg-smu-ink.sis_research-49902019-06-07T06:26:38Z Rule-based specification mining leveraging learning to rank CAO, Zherui TIAN, Yuan LE, Bui Tien Duy LO, David 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 To deal with the problem of lack of and outdated specifications, rule-based specification mining approaches have been proposed.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%. 2018-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3988 info:doi/10.1007/s10515-018-0231-z https://ink.library.smu.edu.sg/context/sis_research/article/4990/viewcontent/Rule_based_specification_mining_leveraging_learning_to_rank_afv.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Specification mining; Learning to rank; Automated software development; Software maintenance and evolution Programming Languages and Compilers Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Specification mining; Learning to rank; Automated software development; Software maintenance and evolution
Programming Languages and Compilers
Software Engineering
spellingShingle Specification mining; Learning to rank; Automated software development; Software maintenance and evolution
Programming Languages and Compilers
Software Engineering
CAO, Zherui
TIAN, Yuan
LE, Bui Tien Duy
LO, David
Rule-based specification mining leveraging learning to rank
description 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%.
format text
author CAO, Zherui
TIAN, Yuan
LE, Bui Tien Duy
LO, David
author_facet CAO, Zherui
TIAN, Yuan
LE, Bui Tien Duy
LO, David
author_sort CAO, Zherui
title Rule-based specification mining leveraging learning to rank
title_short Rule-based specification mining leveraging learning to rank
title_full Rule-based specification mining leveraging learning to rank
title_fullStr Rule-based specification mining leveraging learning to rank
title_full_unstemmed Rule-based specification mining leveraging learning to rank
title_sort rule-based specification mining leveraging learning to rank
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/3988
https://ink.library.smu.edu.sg/context/sis_research/article/4990/viewcontent/Rule_based_specification_mining_leveraging_learning_to_rank_afv.pdf
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