.png){kind=icon}
Return
Static change impact analysis techniques: A comparative study
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jss.2015.07.047.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Software Change Impact Analysis (CIA) is an essential technique to identify the potential effects caused by software changes during software maintenance and evolution. A rich body of CIA techniques, especially static CIA techniques, have continuously emerged in recent years such as structural static analysis, textual analysis, and historical analysis. However, there were only a few works focusing on comparison of static CIA techniques. This article attempts to bridge this gap by presenting a comparative study of three class-level static CIA techniques, i.e., Columbus, ROSE, and (IRCM)-M-2. We compare them based on a CIA comparative framework and conduct an empirical study to evaluate these three CIA techniques and their combinations based on five real-world programs. The empirical results show that: (1)(IRCM)-M-2 and ROSE achieve relatively better precision, recall and F-measure compared to Columbus; (2) combination of any two CIA techniques can improve the precision and recall over their individual one; moreover, combining ROSE with (IRCM)-M-2 produces the best impact results; and (3) combining all three CIA techniques obtain a similar precision and recall as combining ROSE with (IRCM)-M-2. (C) 2015 Elsevier Inc. All rights reserved.
Keywords:
Change impact analysis
Comparative study
Empirical study
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.1
Papers:
5.5K
Citations:
8.4K
Organization
View more organizations
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
ShareSave no more