在不断发展的软件中管理重复程序变更的工具支持

Vamshi Krishna Epuri, Sushma Sakala, Tae-Hyuk Ahn, Myoungkyu Song
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引用次数: 0

摘要

软件修改通常需要一致的程序更改,一组类似的,相关的更改,在程序的多个位置。开发人员通常不容易(i)检测潜在的变更异常,例如遗漏错误或不正确的编辑,以及(ii)确定应用一致变更的相关位置,这是一个乏味且容易出错的过程。为了解决这个问题,本研究提出了一种管理一致的程序变更、检查和应用重复程序转换(CARP)的技术。给定原始和编辑的程序版本之间的程序差异结果,CARP (i)推断变化模式以检测变化异常,(ii)识别所需的编辑位置,以及(iii)自动应用适当的编辑。它已经在集成开发环境的上下文中作为Eclipse插件实现。作者在三个开源项目中评估了CARP,发现CARP检测种子异常的平均准确率为99.1%。此外,它可以识别更改位置并以98%的准确率进行转换。他们的结果表明,CARP应该帮助开发人员在重复的程序变更中检测潜在的变更异常,并自动执行一致的变更。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tool support for managing repetitive program changes in evolving software
Software modification often requires consistent program changes, a group of similar, related changes, at multiple locations in a program. Developers are typically uneasy to (i) detect potential change anomalies such as omission errors or incorrect edits and (ii) determine related locations to apply consistent changes, which is a tedious and error-prone process. To address this problem, this study presents a technique for managing consistent program changes, checking and applying repetitive program transformation (CARP). Given program differencing results between original and edited program versions, CARP (i) infers change patterns to detect change anomalies, (ii) identifies required edit locations, and (iii) automatically applies adequate edits. It has been implemented in the context of the integrated development environment as an Eclipse plug-in. The authors evaluated CARP on three open-source projects and found that CARP detects seeded anomalies with 99.1% accuracy on average. Furthermore, it identifies change locations and transforms them with 98% accuracy. Their results show that CARP should help developers detect potential change anomalies in repetitive program changes and perform consistent changes automatically.
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