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Towards learning to detect meaningful changes in software

Yu, Yijun; Bandara, Arosha; Tun, Thein Than and Nuseibeh, Bashar (2011). Towards learning to detect meaningful changes in software. In: Proceedings of the International Workshop on Machine Learning Technologies in Software Engineering, 12 November 2011, Lawrence, Kansas, ACM, pp. 51–54.

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Software developers are often concerned with particular changes that are relevant to their current tasks: not all changes to evolving software are equally important. Specified at the language-level, we have developed an automated technique to detect only those changes that are deemed meaningful, or relevant, to a particular development task [1]. In practice, however, it is realised that programmers are not always familiar with the production rules of a programming language. Rather, they may prefer to specify the meaningful changes using concrete program examples. In this position paper, we are proposing an inductive learning procedure that involves the programmers in constructing such language-level specifications through examples. Using the efficiently generated meaningful changes detector, programmers are presented with quicker feedback for adjusting the learnt specifications. An illustrative example is used to show how such an inductive learning procedure might be applied.

Item Type: Conference Item
Copyright Holders: 2011 ACM Press
Project Funding Details:
Funded Project NameProject IDFunding Body
FP7 Security Engineering of Lifelong Evolvable Systems (SecureChange)Not SetEuropean Union
SEIF 2011 AwardNot SetMicrosoft Software Engineering Innovative Foundation
CSET2 programmeNot SetScience Foundation Ireland
Academic Unit/Department: Mathematics, Computing and Technology > Computing & Communications
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 30523
Depositing User: Arosha Bandara
Date Deposited: 11 Jan 2012 13:52
Last Modified: 03 Aug 2013 18:10
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