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Conference Proceeding Article



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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.


Computer Sciences | Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Software and Cyber-Physical Systems


2015 IEEE 22nd International Conference on Software Analysis, Evolution and Reengineering (SANER): March 2-6, Montréal: Proceedings

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City or Country

Piscataway, NJ

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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