Fixing software bugs, extending base applications with new functionalities, as well as adapting to changing environments are among the reasons for software evolution. To facilitate such a process and to help maintainers make informed decision, there is a need to be able to estimate and determine the impacts of evolution to the overall software system. While there are already quite a number of useful tools, termed impact analysis tools, developed either as research prototypes or commercial products that addresses such issues, much of which is static based and adopts traditional text based impact reporting. To address some of these issues, we propose a new change impact analysis, called JRegres, that can dynamically generate trails for impact analysis as well as support impact visualization. JRegres serves as a research vehicle to investigate the hypothesis that suggests impact visualization is useful for supporting regression testing.
Keeping up with the advancement in hardware technology, the size and complexity of software systems are increasing at a rapid rate, thus, making them difficult to maintain, expand, and evolve. To alleviate such difficulties, change impact analysis (CIA) and its implementations has been the subject of research for several years. Generally, CIA facilitates regression testing. Specifically, CIA helps to estimate the potential consequences of a software change, including the affected module(s) and their data dependencies, re-testing needs, as well as the required resource planning. Historically, many CIA implementations use static analysis and traditional text-based impact reporting. Although useful, static based CIA implementations often cited as time-and effort-intensive (e.g. requiring extensive documentation/design search). Dynamic slicing is an option to address the aforementioned issues. However, the volume of analyzable data potentially impedes understanding. Visualization can be a good leverage for improving analyzability and understanding of impact analysis from dynamic slicing.In line with such a prospect, this paper offers a dynamic approach to visualize the impacts for support selective testing on regression testing.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.