Paper Info
Title
An Approach for Knowledge Extraction from Source Code (KNESC) of Typed Programming Languages.
Abstract
Knowledge extraction is the discovery of knowledge from structured and/or unstructured sources. This knowledge can be used to build or enrich a domain ontology. Source code is rarely used. But implementation platforms evolve faster than business logic and these evolutions are usually integrated directly into source code without updating the conceptual model. In this paper, we present a generic approach for knowledge extraction from source code of typed programming languages using Hidden Markov Models. This approach consist of the definition of the HMM so that it can be used to extract any type of knowledge from the source code. The method is experimented on EPICAM and GeoServer developed in Java and on MapServer developed in C/C++. Structural evaluation shows that source code contains a structure that permit to build a domain ontology and functional evaluation shows that source code contains more knowledge than those contained in both databases and meta-models.
Year
Venue
Field
2018
WorldCIST
Ontology,Programming language,Conceptual model,Source code,Computer science,Business logic,Knowledge extraction,Hidden Markov model,Java,Ontology learning
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
3
2
Name
Order
Citations
PageRank
Fidèl Jiomekong Azanzi100.34
Gaoussou Camara225.20