DSM: A specification mining tool using recurrent neural network based language model

• Main Authors: LE, Tien-Duy B., BAO, Lingfeng, LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Specification Mining; Deep Learning; Programming Languages and Compilers; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4301; https://ink.library.smu.edu.sg/context/sis_research/article/5304/viewcontent/fse18demo_id36_p.pdf
• Institution: Singapore Management University

Formal specifications are important but often unavailable. Furthermore, writing these specifications is time-consuming and requires skills from developers. In this work, we present Deep Specification Miner (DSM), an automated tool that applies deep learning to mine finite-state automaton (FSA) based specifications. DSM accepts as input a set of execution traces to train a Recurrent Neural Network Language Model (RNNLM). From the input traces, DSM creates a Prefix Tree Acceptor (PTA) and leverages the inferred RNNLM to extract many features. These features are then forwarded to clustering algorithms for merging similar automata states in the PTA for assembling a number of FSAs. Next, our tool performs a model selection heuristic to approximate F-measure of FSAs, and outputs the one with the highest estimated F-measure. Noticeably, our implementation of DSM provides several options that allows users to optimize quality of resultant FSAs. Our video demonstration on the performance of DSM is publicly available at https://goo.gl/Ju4yFS.