Knowledge Extraction from source code based on Hidden Markov Models

Tracking #: 1943-3156

This paper is currently under review
Authors: 
Azanzi Jiomekong
Gaoussou Camara

Responsible editor: 
Guest Editors Knowledge Graphs 2018

Submission type: 
Full Paper
Abstract: 
Large software systems evolve rapidly and these evolutions are usually integrated directly into source code without updating the conceptual model. As a consequence, implementation platforms evolve faster than business logic. Indeed, when extracting knowledge to enrich or build an ontology, business logic is not always a complete data source. To solve this problem, some authors have suggested to adopt an ontology learning approach in order to extract knowledge from the source code. In this paper, we show how to realize this task using Hidden Markov Models. Experiments on EPICAM(a tuberculosis surveillance system developed in JAVA) shows the relevance of this approach.
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Under Review