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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/16543

    Title: Machine Learning and Software Engineering
    Authors: 蔡進發;Jeffrey, J.P.Tsai
    Contributors: 生物與醫學資訊學系
    Keywords: machine learning;software engineering;learning algorithms
    Date: 2003
    Issue Date: 2012-11-23 17:14:24 (UTC+8)
    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.
    Appears in Collections:[生物資訊與醫學工程學系 ] 期刊論文

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