Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2018

Abstract

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.

Keywords

Specification Mining, Deep Learning

Discipline

Programming Languages and Compilers | Software Engineering

Research Areas

Data Science and Engineering

Publication

ESEC/FSE '18: Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering: November 4-9, Lake Buena Vista, FL

First Page

896

Last Page

899

ISBN

9781450355735

Identifier

10.1145/3236024.3264597

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3236024.3264597

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