Exploiting an Ontology-Based Solution to Study Code Smells

Betancourt, Ivian Castellano; Martínez, Nemury Silega; Noguera, Manuel; Grass, Olga Rojas; Vázquez, Osmar Capote

doi:10.1007/978-981-16-1480-4_21

“…This work introduces an ontology-based approach to represent and analyze the knowledge about Code Smells (CS) [26]. This ontology may be a useful instrument to train architects and programmers.…”

Section: Fig 1 General View Of the Methods [25] E Ontology-based Appr...mentioning

confidence: 99%

Towards a Software Development Process Based on Ontologies

Nemury¹,

Rogozov²,

Lapshin³

2023

Proceedings of X All-Russian Scientific Conference &Amp;quot;System Synthesis and Applied Synergetics"

0

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The relevance and complexity of software are growing more and more. Hence, it is necessary to create and apply new approaches that contribute to deal with the complexity of software development process. Ensuring the consistency and traceability of the software artefacts are two of the challenges that software engineers face during the development of a complex software. On the other hand, the ontologies are one of the main technologies of the semantic web for representing knowledge. Adopting ontologies to support the modelling and validation in software development projects is a feasible alternative that has been widely discussed. Therefore, this paper aims to analyze some ontology-based approaches which address some of the gaps that can be faced during software development projects. For example, the lack of consistency and traceability of software artefacts.

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“…Prior calculating a natural language query embedding, the text is 'cleaned' by removing stop words using NLTK 11 -NLTK's stopword list 12 .Later, embeddings are generated using, multi-qa-distilbert-cos-v1 13 . This model utilises the Sentence-BERT model to 'derive semantically meaningful sentence embeddings' [12], and it has been selected because it has been trained with data from the StackExchange website 14 , which includes significant content 11 NLTK: https://www.nltk.org/ 12 NLTK Stopwords: https://gist.github.com/sebleier/554280 13…”

Section: E Search Servicementioning

confidence: 99%

“…Several works [14], [15] utilise knowledge graphs to detect 'bad code smells'. These are anti-patterns and other features that may indicate suboptimal code.…”

Section: Code Smellsmentioning

confidence: 99%

2023 IEEE 19th International Conference on e-Science (e-Science)

2023

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This work presents RepoGraph, an integrated semantic code exploration web tool that combines information extraction, knowledge graphs, and deep learning models. It offers new capabilities for software developers (from academia and industry) to represent and query Python repositories. Unlike existing tools, RepoGraph not only provides a novel search interface powered by deep learning techniques but also exposes the underlying features and representations of repositories to users. Additionally, it offers several interactive visualizations. We also introduce RepoPyOnto, a new ontology that captures the features of Python code repositories and is used by RepoGraph for representing the captured knowledge. Finally, we successfully evaluate RepoGraph against several criteria, including function summarization performance, the correctness and relevance of search results, as well as the processing time for constructing graphs of various sizes.

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“…Prior calculating a natural language query embedding, the text is 'cleaned' by removing stop words using NLTK 11 -NLTK's stopword list 12 .Later, embeddings are generated using, multi-qa-distilbert-cos-v1 13 . This model utilises the Sentence-BERT model to 'derive semantically meaningful sentence embeddings' [12], and it has been selected because it has been trained with data from the StackExchange website 14 , which includes significant content 11 NLTK: https://www.nltk.org/ 12 NLTK Stopwords: https://gist.github.com/sebleier/554280 13…”

Section: E Search Servicementioning

confidence: 99%

“…Several works [14], [15] utilise knowledge graphs to detect 'bad code smells'. These are anti-patterns and other features that may indicate suboptimal code.…”

Section: Code Smellsmentioning

confidence: 99%

RepoGraph: A Novel Semantic Code Exploration Tool for Python Repositories Based on Knowledge Graphs and Deep Learning

Williams,

Filgueira

2023

2023 IEEE 19th International Conference on E-Science (E-Science)

1

0

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This work presents RepoGraph, an integrated semantic code exploration web tool that combines information extraction, knowledge graphs, and deep learning models. It offers new capabilities for software developers (from academia and industry) to represent and query Python repositories. Unlike existing tools, RepoGraph not only provides a novel search interface powered by deep learning techniques but also exposes the underlying features and representations of repositories to users. Additionally, it offers several interactive visualizations. We also introduce RepoPyOnto, a new ontology that captures the features of Python code repositories and is used by RepoGraph for representing the captured knowledge. Finally, we successfully evaluate RepoGraph against several criteria, including function summarization performance, the correctness and relevance of search results, as well as the processing time for constructing graphs of various sizes.

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Exploiting an Ontology-Based Solution to Study Code Smells

Cited by 3 publications

References 10 publications

Towards a Software Development Process Based on Ontologies

Towards a Software Development Process Based on Ontologies

2023 IEEE 19th International Conference on e-Science (e-Science)

RepoGraph: A Novel Semantic Code Exploration Tool for Python Repositories Based on Knowledge Graphs and Deep Learning

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