Analyzing Software Evolution through Feature Views

Greevy, Orla; Ducasse, Stéphane; Gîrba, Tudor (2006). Analyzing Software Evolution through Feature Views. Journal of software maintenance and evolution - research and practice, 18(6), pp. 425-456. Chichester: John Wiley & Sons, Ltd. 10.1002/smr.340

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Features encapsulate the domain knowledge of a software system and thus are valuable sources of information for a reverse engineer. When analyzing the evolution of a system, we need to know how and which features were modified to recover both the change intention and its extent, namely which source artifacts are affected. Typically, the implementation of a feature crosscuts a number of source artifacts. To obtain a mapping between features to the source artifacts, we exercise the features and capture their execution traces. However this results in large traces that are difficult to interpret. To tackle this issue we compact the traces into simple sets of source artifacts that participate in a feature's runtime behavior. We refer to these compacted traces as feature views. Within a feature view, we partition the source artifacts into disjoint sets of characterized software entities. The characterization defines the level of participation of a source entity in the features. We then analyze the features over several versions of a system and we plot their evolution to reveal how and hich features were affected by changes in the code. We show the usefulness of our approach by applying it to a case study where we address the problem of merging parallel development tracks of the same system.

Item Type: Journal Article (Original Article)
Division/Institute: 08 Faculty of Science > Institute of Computer Science (INF)
UniBE Contributor: Greevy, Orla; Ducasse, Stephane and Girba, Tudor Adrian
ISSN: 1532-060X
Publisher: John Wiley & Sons, Ltd.
Language: English
Submitter: Factscience Import
Date Deposited: 04 Oct 2013 14:47
Last Modified: 06 Dec 2013 13:42
Publisher DOI: 10.1002/smr.340
Web of Science ID: 000245695700002
URI: (FactScience: 2014)

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