Yu, Yijun; Bandara, Arosha; Tun, Thein Than and Nuseibeh, Bashar
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|DOI (Digital Object Identifier) Link:||http://doi.org/10.1145/2070821.2070828|
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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 . 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:||
|Academic Unit/Department:||Mathematics, Computing and Technology > Computing & Communications
Mathematics, Computing and Technology
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Arosha Bandara|
|Date Deposited:||11 Jan 2012 13:52|
|Last Modified:||25 Feb 2016 04:32|
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