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 conc...
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| Format: | Article |
| Language: | English |
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Babes-Bolyai University, Cluj-Napoca
2021-07-01
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| Series: | Studia Universitatis Babes-Bolyai: Series Informatica |
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| Online Access: | http://193.231.18.162/index.php/subbinformatica/article/view/1173 |
| _version_ | 1797322236463415296 |
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| author | Elena-Manuela MANOLE |
| author_facet | Elena-Manuela MANOLE |
| author_sort | Elena-Manuela MANOLE |
| collection | DOAJ |
| description |
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.
Received by the editors: 24 May 2021.
2010 Mathematics Subject Classification. 68T05, 68T20.
1998 CR Categories and Descriptors. I.2.6 [Artificial Intelligence]: Learning - Connectionism and neural nets; D.2.2 [Software Engineering]: Design Tools and Techniques - Computer-aided software engineering (CASE).
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| first_indexed | 2024-03-08T05:11:16Z |
| format | Article |
| id | doaj.art-851e5d997cef41f9bf10991d5326671f |
| institution | Directory Open Access Journal |
| issn | 2065-9601 |
| language | English |
| last_indexed | 2024-03-08T05:11:16Z |
| publishDate | 2021-07-01 |
| publisher | Babes-Bolyai University, Cluj-Napoca |
| record_format | Article |
| series | Studia Universitatis Babes-Bolyai: Series Informatica |
| spelling | doaj.art-851e5d997cef41f9bf10991d5326671f2024-02-07T10:03:35ZengBabes-Bolyai University, Cluj-NapocaStudia Universitatis Babes-Bolyai: Series Informatica2065-96012021-07-0166110.24193/subbi.2021.1.04Detecting the Most Important Classes from Software Systems with Self Organizing MapsElena-Manuela MANOLE0Department of Computer Science, Faculty of Mathematics and Computer Science, Babes-Bolyai University, 1 Kogalniceanu St., 400084 Cluj-Napoca, Romania; meic2001@scs.ubbcluj.ro 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. Received by the editors: 24 May 2021. 2010 Mathematics Subject Classification. 68T05, 68T20. 1998 CR Categories and Descriptors. I.2.6 [Artificial Intelligence]: Learning - Connectionism and neural nets; D.2.2 [Software Engineering]: Design Tools and Techniques - Computer-aided software engineering (CASE). http://193.231.18.162/index.php/subbinformatica/article/view/1173self organizing maps, artificial neural networks, machine learning, classification algorithms, search based software engineering, software visualisation, key class detection, condensing class diagrams |
| spellingShingle | Elena-Manuela MANOLE Detecting the Most Important Classes from Software Systems with Self Organizing Maps Studia Universitatis Babes-Bolyai: Series Informatica self organizing maps, artificial neural networks, machine learning, classification algorithms, search based software engineering, software visualisation, key class detection, condensing class diagrams |
| title | Detecting the Most Important Classes from Software Systems with Self Organizing Maps |
| title_full | Detecting the Most Important Classes from Software Systems with Self Organizing Maps |
| title_fullStr | Detecting the Most Important Classes from Software Systems with Self Organizing Maps |
| title_full_unstemmed | Detecting the Most Important Classes from Software Systems with Self Organizing Maps |
| title_short | Detecting the Most Important Classes from Software Systems with Self Organizing Maps |
| title_sort | detecting the most important classes from software systems with self organizing maps |
| topic | self organizing maps, artificial neural networks, machine learning, classification algorithms, search based software engineering, software visualisation, key class detection, condensing class diagrams |
| url | http://193.231.18.162/index.php/subbinformatica/article/view/1173 |
| work_keys_str_mv | AT elenamanuelamanole detectingthemostimportantclassesfromsoftwaresystemswithselforganizingmaps |