SMArTIC: towards building an accurate, robust and scalable specification miner. Improper management of software evolution, compounded by imprecise, and changing requirements, along with the ”short time to market” requirement, commonly leads to a lack of up-to-date specifications. This can result in software that is characterized by 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 traces should be pruned from the input traces to a miner, and (2) Clustering related traces will localize inaccuracies and reduce over-generalizationin learning. Correspondingly, SMArTIC comprises four components: an erroneous-trace filtering block, a related-trace clustering block, a learner, and a merger. We show through experiments that the quality of specification mining can be significantly improved using SMArTIC.
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References in zbMATH (referenced in 3 articles )
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