Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/37102
Title: Data Mining for Software Engineering and Humans in the Loop
Authors: Minku, Leandro L.
Mendes, E.
Turhan, B.
First Published: 16-Apr-2016
Publisher: Springer Verlag (Germany)
Citation: Progress in Artificial Intelligence (PRAI), 2016, 5(4), pp 307–314
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.
DOI Link: 10.1007/s13748-016-0092-2
ISSN: 2192-6360
Links: https://link.springer.com/article/10.1007/s13748-016-0092-2
http://hdl.handle.net/2381/37102
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: © The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Appears in Collections:Published Articles, Dept. of Computer Science

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