document: Efficient mining of iterative patterns for software specification discovery 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 .
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