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 Nov 2011, Lawrence, Kansas, ACM, pp. 51–54.

DOI: https://doi.org/10.1145/2070821.2070828

URL: http://doi.acm.org/10.1145/2070821.2070828

Abstract

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.

Viewing alternatives

Metrics

Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions

Item Actions

Export

About

Recommendations