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An Experiment Comparing Lifted and Delayed Variability-Aware Program Analysis

机译:比较提升和延迟可变性的实验性的实验

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Today's software systems need to be highly flexible and managing their variability plays an essential role during development. Variability-aware program analysis techniques have been proposed to support developers in understanding code-level variability by analyzing the space of program variants. Such techniques are highly beneficial, e.g., when determining the impact of changes during maintenance and evolution. Two strategies have been proposed in the literature to make existing program analysis techniques variability-aware:(i) program analysis can be lifted by considering variability already in the parsing stage; or(ii) analysis can be delayed by considering and recovering variability only when needed. Both strategies have advantages and disadvantages, however, a systematic comparison is still missing. The contributions of this paper are an in-depth comparison of SPLLIFT and COACH, two existing approaches representing these two strategies, and an analysis and discussion of the trade-offs regarding precision and run-time performance. The results of our experiment show that the delayed strategy is significantly faster but typically less precise. Our findings are intended for researchers and practitioners deciding which strategy to select for their purpose and context.
机译:今天的软件系统需要高度灵活,管理其变化在开发期间起着重要作用。已经提出了可变性感知程序分析技术来支持开发人员通过分析程序变量的空间来了解代码级别可变性。这些技术是非常有益的,例如,当确定在维护和进化期间改变的影响时。在文献中提出了两种策略,以使现有的计划分析技术可变性感知:(i)可以通过考虑在解析阶段的可变性来提升方案分析;或(ii)仅在需要时考虑和恢复可变性来延迟分析。这两种策略都具有优缺点,但是,系统比较仍然缺失。本文的贡献是对斯普拉夫特和教练的深入比较,这两种现有方法代表这两种策略,以及对精确和运行时间绩效的权衡的分析和讨论。我们的实验结果表明,延迟策略明显更快,但通常不太精确。我们的调查结果适用于研究人员和从业者决定选择哪种策略,以选择其目的和背景。

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