PUBLICATION DATE

2003-06

AUTHORS

Jeffrey J.P. Tsai, Du Zhang

TITLE

Machine Learning and Software Engineering

ISSUE

2

VOLUME

11

ISSN (print)

0963-9314

ISSN (electronic)

1573-1367

ABSTRACT

Machine learning deals with the issue of how to build programs that improve their performance at some task through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. They are particularly useful for (a) poorly understood problem domains where little knowledge exists for the humans to develop effective algorithms; (b) domains where there are large databases containing valuable implicit regularities to be discovered; or (c) domains where programs must adapt to changing conditions. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This paper deals with the subject of applying machine learning in software engineering. In the paper, we first provide the characteristics and applicability of some frequently utilized machine learning algorithms. We then summarize and analyze the existing work and discuss some general issues in this niche area. Finally we offer some guidelines on applying machine learning methods to software engineering tasks and use some software development and maintenance tasks as examples to show how they can be formulated as learning problems and approached in terms of learning algorithms.

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34 TRIPLES      29 PREDICATES      33 URIs      18 LITERALS

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1 articles:585c6f546e89333bc68a666b26f671b4 sg:abstract Abstract Machine learning deals with the issue of how to build programs that improve their performance at some task through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. They are particularly useful for (a) poorly understood problem domains where little knowledge exists for the humans to develop effective algorithms; (b) domains where there are large databases containing valuable implicit regularities to be discovered; or (c) domains where programs must adapt to changing conditions. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This paper deals with the subject of applying machine learning in software engineering. In the paper, we first provide the characteristics and applicability of some frequently utilized machine learning algorithms. We then summarize and analyze the existing work and discuss some general issues in this niche area. Finally we offer some guidelines on applying machine learning methods to software engineering tasks and use some software development and maintenance tasks as examples to show how they can be formulated as learning problems and approached in terms of learning algorithms.
2 sg:articleType OriginalPaper
3 sg:coverYear 2003
4 sg:coverYearMonth 2003-06
5 sg:ddsId Art3
6 sg:ddsIdJournalBrand 11219
7 sg:doi 10.1023/A:1023760326768
8 sg:doiLink http://dx.doi.org/10.1023/A:1023760326768
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19 sg:indexingDatabase Web of Science
20 sg:issnElectronic 1573-1367
21 sg:issnPrint 0963-9314
22 sg:issue 2
23 sg:language English
24 sg:license http://scigraph.springernature.com/explorer/license/
25 sg:pageEnd 119
26 sg:pageStart 87
27 sg:publicationYear 2003
28 sg:publicationYearMonth 2003-06
29 sg:scigraphId 585c6f546e89333bc68a666b26f671b4
30 sg:title Machine Learning and Software Engineering
31 sg:volume 11
32 sg:webpage https://link.springer.com/10.1023/A:1023760326768
33 rdf:type sg:Article
34 rdfs:label Article: Machine Learning and Software Engineering
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