Abstract
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 language | English |
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Title of host publication | 2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017 |
Editors | L. O'Conner |
Place of Publication | Piscataway |
Publisher | IEEE |
Pages | 638-642 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-5386-0992-7 |
ISBN (Print) | 978-1-5386-0993-4 |
DOIs | |
Publication status | Published - 2017 |
Event | ICSME 2017: 33rd International Conference on Software Maintenance and Evolution - Shanghai, China Duration: 17 Sept 2017 → 24 Sept 2017 Conference number: 33 https://icsme2017.github.io/ |
Conference
Conference | ICSME 2017 |
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Abbreviated title | ICSME |
Country/Territory | China |
City | Shanghai |
Period | 17/09/17 → 24/09/17 |
Internet address |
Keywords
- Learning Automata
- Tools
- Software algorithms
- Software
- Heuristic algorithms
- Machine learning algorithms
- Algorithm design and analysis