demonstration

Logic-based learning in software engineering

Authors:

Dalal Alrajeh,

Alessandra Russo,

Sebastian Uchitel,

Jeff KramerAuthors Info & Claims

ICSE '16: Proceedings of the 38th International Conference on Software Engineering Companion

Pages 892 - 893

https://doi.org/10.1145/2889160.2891050

Published: 14 May 2016 Publication History

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Abstract

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.

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Cited By

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Shi LChen CWang QBoehm B(2021)Automatically detecting feature requests from development emails by leveraging semantic sequence miningRequirements Engineering10.1007/s00766-020-00344-y26:2(255-271)Online publication date: 30-Mar-2021
https://doi.org/10.1007/s00766-020-00344-y

Index Terms

Logic-based learning in software engineering
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Logical and relational learning
2. Software and its engineering
  1. Software creation and management
    1. Designing software

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Published In

ICSE '16: Proceedings of the 38th International Conference on Software Engineering Companion

May 2016

946 pages

ISBN:9781450342056

DOI:10.1145/2889160

General Chair:
Laura Dillon
Michigan State University
,
Program Chairs:
Willem Visser
Stellenbosch University, South Africa
,
Laurie Williams
North Carolina State University

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 14 May 2016

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ICSE '16: 38th International Conference on Software Engineering

May 14 - 22, 2016

Texas, Austin

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Shi LChen CWang QBoehm B(2021)Automatically detecting feature requests from development emails by leveraging semantic sequence miningRequirements Engineering10.1007/s00766-020-00344-y26:2(255-271)Online publication date: 30-Mar-2021
https://doi.org/10.1007/s00766-020-00344-y

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Machine Learning and Software Engineering

Automating Software Engineering with Machine Learning

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