Abstract
Software engineering has grown to be one of the most important disciplines with noticeable impact to business and everyday life. However, there are still several challenges posed when develo** and maintaining software, which often result in lost time and effort. In this chapter, we discuss how current challenges can be confronted using mining techniques toward applying software reuse. We initially focus on defining the scope and purpose of this book given the current state of the practice in software engineering. After that, the underlying context of software reuse is discussed with respect to the areas of requirements mining, source code mining, and quality assessment. For each of these areas, we outline the contributions of the book, and finally we provide an overview of the different chapters.
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Software intelligence can be actually seen as a specialization of business intelligence, which, according to Gartner analyst Howard Dresner [7], is an umbrella term used to describe “concepts and methods to improve business decision-making by using fact-based support systems”. Although this contemporary definition can be traced in the late 1980s, the term business intelligence is actually older than a century and is first attributed to Richard M. Devens, who used it (in his book “Cyclopaedia of Commercial and Business Anecdotes: Comprising Interesting Reminiscences and Facts, Remarkable Traits and Humors ... of Merchants, Traders, Bankers ... etc. in All Ages and Countries”, published by D. Appleton & Company in 1868) to describe the ability of the banker Sir Henry Furnese to gain profit by taking successful decisions according to information received by his environment.
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Diamantopoulos, T., Symeonidis, A.L. (2020). Introduction. In: Mining Software Engineering Data for Software Reuse. Advanced Information and Knowledge Processing. Springer, Cham. https://doi.org/10.1007/978-3-030-30106-4_1
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DOI: https://doi.org/10.1007/978-3-030-30106-4_1
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