SMArTIC: Specification Mining Architecture with Trace fIltering and Clustering
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Date
2006-08-31
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Abstract
Improper management of software evolution, compounded by imprecise, and changing requirements and short time to market requirement, commonly leads to a lack of up-to-date specification. This can result in software that is characterized by presence of bugs, anomalies and even security threats. Software specification mining is a new technique to address this concern by inferring specifications automatically. In this paper, we propose a novel API specification mining architecture called SMArTIC (Specification Mining Architecture with Trace fIltering and Clustering) to improve the accuracy, robustness and scalability of specification miners. This architecture is constructed based on two hypotheses: (1) Erroneous transactions should be pruned from traces input to a miner, and (2) Clustering related traces will localize inaccuracies in learning and reduce over-generalization. Corresponding, SMArTIC comprises four components: an erroneous-trace filtering block, a related-trace clustering block, a learner, and a merger. We show through experiment that the quality of specification miner can be significantly improved using SMArTIC.