Detecting the Most Important Classes from Software Systems with Self Organizing Maps

Self Organizing Maps (SOM) are unsupervised neural networks suited for visualisation purposes and clustering analysis. This study uses SOM to solve a software engineering problem: detecting the most important (key) classes from software projects. Key classes are meant to link the most valuable conce...

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Vydané v:Studia Universitatis Babes-Bolyai: Series Informatica Ročník 66; číslo 1; s. 54
Hlavný autor: Manole, E.-M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Babes-Bolyai University, Cluj-Napoca 01.07.2021
Predmet:
self organizing maps, artificial neural networks, machine learning, classification algorithms, search based software engineering, software visualisation, key class detection, condensing class diagrams
ISSN:1224-869X, 2065-9601
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Popis
Shrnutí:Self Organizing Maps (SOM) are unsupervised neural networks suited for visualisation purposes and clustering analysis. This study uses SOM to solve a software engineering problem: detecting the most important (key) classes from software projects. Key classes are meant to link the most valuable concepts of a software system and in general these are found in the solution documentation. UML models created in the design phase become deprecated in time and tend to be a source of confusion for large legacy software. Therefore, developers try to reconstruct class diagrams from the source code using reverse engineering. However, the resulting diagram is often very cluttered and difficult to understand. There is an interest for automatic tools for building concise class diagrams, but the machine learning possibilities are not fully explored at the moment. This paper proposes two possible algorithms to transform SOM in a classification algorithm to solve this task, which involves separating the important classes - that should be on the diagrams - from the others, less important ones. Moreover, SOM is a reliable visualization tool which able to provide an insight about the structure of the analysed projects.
ISSN:1224-869X
2065-9601
DOI:10.24193/subbi.2021.1.04