Beyond Support and Confidence: Exploring Interestingness Measures for Rule-based Specification Mining

Numerous rule-based specification mining approaches have been proposed in the literature. Many of these approaches analyze a set of execution traces to discover interesting usage rules, e.g., whenever lock() is invoked, eventually unlock() is invoked. These techniques often generate and enumerate a...

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Main Authors: LE, Bui Tien Duy, David LO
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Language:English
Published: Institutional Knowledge at Singapore Management University 2015
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Online Access:https://ink.library.smu.edu.sg/sis_research/2862
https://ink.library.smu.edu.sg/context/sis_research/article/3862/viewcontent/InterestingnessMeasure_saner2015.pdf
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spelling sg-smu-ink.sis_research-38622018-03-14T06:45:13Z Beyond Support and Confidence: Exploring Interestingness Measures for Rule-based Specification Mining LE, Bui Tien Duy David LO, Numerous rule-based specification mining approaches have been proposed in the literature. Many of these approaches analyze a set of execution traces to discover interesting usage rules, e.g., whenever lock() is invoked, eventually unlock() is invoked. These techniques often generate and enumerate a set of candidate rules and compute some interestingness scores. Rules whose interestingness scores are above a certain threshold would then be output. In past studies, two measures, namely support and confidence, which are well-known measures, are often used to compute these scores. However, aside from these two, many other interestingness measures have been proposed. It is thus unclear if support and confidence are the best interestingness measures for specification mining. In this work, we perform an empirical study that investigates the utility of 38 interestingness measures in recovering correct specifications of classes from Java libraries. We used a ground truth dataset consisting of 683 rules and recorded execution traces that are produced when we run the DaCapo test suite. We apply 38 different interestingness measures to identify correct rules from a pool of candidate rules. Our study highlights that many measures are on par to support and confidence. Some of the measures are even better than support or confidence and at least one of the measures is statistically significantly better than the two measures. We also find that compositions of several measures with support statistically significantly outperform the composition of support and confidence. Our findings highlight the need to look beyond standard support and confidence to find interesting rules. 2015-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2862 info:doi/10.1109/SANER.2015.7081843 https://ink.library.smu.edu.sg/context/sis_research/article/3862/viewcontent/InterestingnessMeasure_saner2015.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 Computer Sciences Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Computer Sciences
Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Computer Sciences
Databases and Information Systems
Numerical Analysis and Scientific Computing
LE, Bui Tien Duy
David LO,
Beyond Support and Confidence: Exploring Interestingness Measures for Rule-based Specification Mining
description Numerous rule-based specification mining approaches have been proposed in the literature. Many of these approaches analyze a set of execution traces to discover interesting usage rules, e.g., whenever lock() is invoked, eventually unlock() is invoked. These techniques often generate and enumerate a set of candidate rules and compute some interestingness scores. Rules whose interestingness scores are above a certain threshold would then be output. In past studies, two measures, namely support and confidence, which are well-known measures, are often used to compute these scores. However, aside from these two, many other interestingness measures have been proposed. It is thus unclear if support and confidence are the best interestingness measures for specification mining. In this work, we perform an empirical study that investigates the utility of 38 interestingness measures in recovering correct specifications of classes from Java libraries. We used a ground truth dataset consisting of 683 rules and recorded execution traces that are produced when we run the DaCapo test suite. We apply 38 different interestingness measures to identify correct rules from a pool of candidate rules. Our study highlights that many measures are on par to support and confidence. Some of the measures are even better than support or confidence and at least one of the measures is statistically significantly better than the two measures. We also find that compositions of several measures with support statistically significantly outperform the composition of support and confidence. Our findings highlight the need to look beyond standard support and confidence to find interesting rules.
format text
author LE, Bui Tien Duy
David LO,
author_facet LE, Bui Tien Duy
David LO,
author_sort LE, Bui Tien Duy
title Beyond Support and Confidence: Exploring Interestingness Measures for Rule-based Specification Mining
title_short Beyond Support and Confidence: Exploring Interestingness Measures for Rule-based Specification Mining
title_full Beyond Support and Confidence: Exploring Interestingness Measures for Rule-based Specification Mining
title_fullStr Beyond Support and Confidence: Exploring Interestingness Measures for Rule-based Specification Mining
title_full_unstemmed Beyond Support and Confidence: Exploring Interestingness Measures for Rule-based Specification Mining
title_sort beyond support and confidence: exploring interestingness measures for rule-based specification mining
publisher Institutional Knowledge at Singapore Management University
publishDate 2015
url https://ink.library.smu.edu.sg/sis_research/2862
https://ink.library.smu.edu.sg/context/sis_research/article/3862/viewcontent/InterestingnessMeasure_saner2015.pdf
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