Fixing bugs and implementing enhancements are very relevant activities in a typical software life cycle. They require, as a pre-requisite, the location of a portion of impacted code within a possibly large codebase. This operation can be extremely difficult and time-consuming particularly for developers not much familiar with the software. With that perspective we focus on a simple research question: is it possible to support impact analysis using the information available in software repositories, in particular code comments and version control log? We devised a simple and novel approach, based on Natural Language Processing techniques, that provides support in impact analysis. On the average the proposed approach is very selective with a 99% specificity and achieves a recall of 96% and a precision of 13.6% with respect to a manually built gold standard.
Impact Analysis by means of Unstructured Knowledge in the Context of Bug Repositories / Torchiano, Marco; Filippo, Ricca. - (2010), pp. 47:1-47:4. (Intervento presentato al convegno ESEM, IEEE International Symposium on Empirical Software Engineering and Measurement tenutosi a Bolzano (Italy) nel 16-17 September) [10.1145/1852786.1852847].
Impact Analysis by means of Unstructured Knowledge in the Context of Bug Repositories
TORCHIANO, MARCO;
2010
Abstract
Fixing bugs and implementing enhancements are very relevant activities in a typical software life cycle. They require, as a pre-requisite, the location of a portion of impacted code within a possibly large codebase. This operation can be extremely difficult and time-consuming particularly for developers not much familiar with the software. With that perspective we focus on a simple research question: is it possible to support impact analysis using the information available in software repositories, in particular code comments and version control log? We devised a simple and novel approach, based on Natural Language Processing techniques, that provides support in impact analysis. On the average the proposed approach is very selective with a 99% specificity and achieves a recall of 96% and a precision of 13.6% with respect to a manually built gold standard.Pubblicazioni consigliate
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https://hdl.handle.net/11583/2372239
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