Finite state models, such as Mealy machines or state charts, are often used to express and specify protocol and software behavior. Consequently, these models are often used in verification, testing, and for assistance in the development and maintenance process. Reverse engineering these models from execution traces and log files, in turn, can accelerate and improve the software development and inform domain experts about the processes actually executed in a system. We present name, an open-source software tool to learn variants of finite state automata from traces using a state-of-the-art evidence-driven state-merging algorithm at its core. We embrace the need for customized models and tailored learning heuristics in different application domains by providing a flexible, extensible interface.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017
EditorsL. O'Conner
Place of PublicationPiscataway
PublisherIEEE
Pages638-642
Number of pages5
ISBN (Electronic)978-1-5386-0992-7
ISBN (Print)978-1-5386-0993-4
DOIs
Publication statusPublished - 2017
EventICSME 2017: 33rd International Conference on Software Maintenance and Evolution - Shanghai, China
Duration: 17 Sep 201724 Sep 2017
Conference number: 33
https://icsme2017.github.io/

Conference

ConferenceICSME 2017
Abbreviated titleICSME
CountryChina
CityShanghai
Period17/09/1724/09/17
Internet address

    Research areas

  • Learning Automata, Tools, Software algorithms, Software, Heuristic algorithms, Machine learning algorithms, Algorithm design and analysis

ID: 33113662