Logic-based learning in software engineering
In recent years, research efforts have been directed towards the use of Machine Learning (ML) techniques to support and automate activities such as program repair, specification mining and risk assessment. The focus has largely been on techniques for classification, clustering and regression. Althou...
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2016
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_02705257_v_n_p892_Alrajeh http://hdl.handle.net/20.500.12110/paper_02705257_v_n_p892_Alrajeh |
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paper:paper_02705257_v_n_p892_Alrajeh |
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paper:paper_02705257_v_n_p892_Alrajeh2023-06-08T15:24:41Z Logic-based learning in software engineering Application programs Computer circuits Learning systems Risk assessment Software design Software engineering Technical presentations Automated support Future challenges Interpretable representation Learning approach Research efforts Rule based Software model Specification mining Engineering education In recent years, research efforts have been directed towards the use of Machine Learning (ML) techniques to support and automate activities such as program repair, specification mining and risk assessment. The focus has largely been on techniques for classification, clustering and regression. Although beneficial, these do not produce a declarative, interpretable representation of the learned information. Hence, they cannot readily be used to inform, revise and elaborate software models. On the other hand, recent advances in ML have witnessed the emergence of new logic-based learning approaches that differ from traditional ML in that their output is represented in a declarative, rule-based manner, making them well-suited for many software engineering tasks. In this technical briefing, we will introduce the audience to the latest advances in logic-based learning, give an overview of how logic-based learning systems can successfully provide automated support to a variety of software engineering tasks, demonstrate the application to two real case studies from the domain of requirements engineering and software design and highlight future challenges and directions. © 2016 Authors. 2016 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_02705257_v_n_p892_Alrajeh http://hdl.handle.net/20.500.12110/paper_02705257_v_n_p892_Alrajeh |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Application programs Computer circuits Learning systems Risk assessment Software design Software engineering Technical presentations Automated support Future challenges Interpretable representation Learning approach Research efforts Rule based Software model Specification mining Engineering education |
| spellingShingle |
Application programs Computer circuits Learning systems Risk assessment Software design Software engineering Technical presentations Automated support Future challenges Interpretable representation Learning approach Research efforts Rule based Software model Specification mining Engineering education Logic-based learning in software engineering |
| topic_facet |
Application programs Computer circuits Learning systems Risk assessment Software design Software engineering Technical presentations Automated support Future challenges Interpretable representation Learning approach Research efforts Rule based Software model Specification mining Engineering education |
| description |
In recent years, research efforts have been directed towards the use of Machine Learning (ML) techniques to support and automate activities such as program repair, specification mining and risk assessment. The focus has largely been on techniques for classification, clustering and regression. Although beneficial, these do not produce a declarative, interpretable representation of the learned information. Hence, they cannot readily be used to inform, revise and elaborate software models. On the other hand, recent advances in ML have witnessed the emergence of new logic-based learning approaches that differ from traditional ML in that their output is represented in a declarative, rule-based manner, making them well-suited for many software engineering tasks. In this technical briefing, we will introduce the audience to the latest advances in logic-based learning, give an overview of how logic-based learning systems can successfully provide automated support to a variety of software engineering tasks, demonstrate the application to two real case studies from the domain of requirements engineering and software design and highlight future challenges and directions. © 2016 Authors. |
| title |
Logic-based learning in software engineering |
| title_short |
Logic-based learning in software engineering |
| title_full |
Logic-based learning in software engineering |
| title_fullStr |
Logic-based learning in software engineering |
| title_full_unstemmed |
Logic-based learning in software engineering |
| title_sort |
logic-based learning in software engineering |
| publishDate |
2016 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_02705257_v_n_p892_Alrajeh http://hdl.handle.net/20.500.12110/paper_02705257_v_n_p892_Alrajeh |
| _version_ |
1768545507804708864 |