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Title: Efficient mining of iterative patterns for software specification discovery
Authors: Lo, D.
Khoo, S.-C. 
Liu, C.
Keywords: Closed iterative patterns
Software specification discovery
Issue Date: 2007
Citation: Lo, D.,Khoo, S.-C.,Liu, C. (2007). Efficient mining of iterative patterns for software specification discovery. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : 460-469. ScholarBank@NUS Repository.
Abstract: Studies have shown that program comprehension takes up to 45% of software development costs. Such high costs are caused by the lack-of documented specification and further aggravated by the phenomenon of software evolution. There is a need for automated tools to extract specifications to aid program comprehension. In this paper, a novel technique to efficiently mine common software temporal patterns from traces is proposed. These patterns shed light on program behaviors, and are termed iterative patterns. They capture unique characteristic of software traces, typically not found in arbitrary sequences. Specifically, due to loops, interesting iterative patterns can occur multiple times within a trace. Furthermore, an occurrence of an iterative pattern in a trace can extend across a sequence of indefinite length. Since a program behavior can be manifested in numerous ways, analyzing a single trace will not be sufficient. Iterative pattern mining extends sequential pattern and episode minings to discover frequent iterative patterns which occur repetitively both within a program trace and across multiple traces. In this paper, we present CLIPER (CLosed Iterative Pattern minER) to efficiently mine a closed set of iterative patterns. A performance study on several simulated and real datasets shows the efficiency of our mining algorithm and effectiveness of our pruning strategy. Our case study on JBoss Application Server confirms the usefulness of mined patterns in discovering interesting software behavioral specification. © 2007 ACM.
Source Title: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
ISBN: 1595936092
DOI: 10.1145/1281192.1281243
Appears in Collections:Staff Publications

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