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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Autores principales: Alrajeh, D., Russo, A., Uchitel, S., Kramer, J.
Formato: CONF
Materias:
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
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_02705257_v_n_p892_Alrajeh
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_02705257_v_n_p892_Alrajeh
record_format dspace
spelling todo:paper_02705257_v_n_p892_Alrajeh2023-10-03T15:14:33Z Logic-based learning in software engineering Alrajeh, D. Russo, A. Uchitel, S. Kramer, J. 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. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar 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
Alrajeh, D.
Russo, A.
Uchitel, S.
Kramer, J.
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.
format CONF
author Alrajeh, D.
Russo, A.
Uchitel, S.
Kramer, J.
author_facet Alrajeh, D.
Russo, A.
Uchitel, S.
Kramer, J.
author_sort Alrajeh, D.
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
url http://hdl.handle.net/20.500.12110/paper_02705257_v_n_p892_Alrajeh
work_keys_str_mv AT alrajehd logicbasedlearninginsoftwareengineering
AT russoa logicbasedlearninginsoftwareengineering
AT uchitels logicbasedlearninginsoftwareengineering
AT kramerj logicbasedlearninginsoftwareengineering
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