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Title: Data Mining for Software Engineering and Humans in the Loop
Authors: Minku, Leandro L.
Mendes, E.
Turhan, B.
First Published: 29-Mar-2016
Publisher: Springer Verlag (Germany)
Citation: Progress in Artificial Intelligence (PRAI), 2016
Abstract: The field of data mining for software engineering has been growing over the last decade. This field is concerned with the use of data mining to provide useful insights into how to improve software engineering processes and software itself, supporting decision making. For that, data produced by software engineering processes and products during and after software development is used. Despite promising results, there is frequently a lack of discussion on the role of software engineering practitioners amidst the data mining approaches. This makes adoption of data mining by software engineering practitioners difficult. Moreover, the fact that experts’ knowledge is frequently ignored by data mining approaches, together with the lack of transparency of such approaches, can hinder the acceptability of data mining by software engineering practitioners. In order to overcome these problems, this position paper provides a discussion of the role of software engineering experts when adopting data mining approaches. It also argues that this role can be extended in order to increase experts’ involvement in the process of building data mining models. We believe that such extended involvement is not only likely to increase software engineers’ acceptability of the resulting models, but also improve the models themselves. We also provide some recommendations aimed at increasing the success of experts involvement and model acceptability.
ISSN: 2192-6360
Links: TBA
Embargo on file until: 29-Mar-2017
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Creative Commons “Attribution Non-Commercial No Derivatives” licence CC BY-NC-ND, further details of which can be found via the following link:
Appears in Collections:Published Articles, Dept. of Computer Science

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