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Intraprocedural Dataflow Analysis for Software Product Lines

机译:软件产品线的过程内数据流分析

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摘要

Software product lines (SPLs) are commonly developed using annotative approaches such as conditional compilation that come with an inherent risk of constructing erroneous products. For this reason, it is essential to be able to analyze SPLs. However, as dataflow analysis techniques are not able to deal with SPLs, developers must generate and analyze all valid methods individually, which is expensive for non-trivial SPLs. In this paper, we demonstrate how to take any standard intraprocedural dataflow analysis and automatically turn it into & feature-sensitive dataflow analysis in three different ways. All are capable of analyzing all valid methods of an SPL without having to generate all of them explicitly. We have implemented all analyses as extensions of SOOT's intraprocedural dataflow analysis framework and experimentally evaluated their performance and memory characteristics on four qualitatively different SPLs. The results indicate that the feature-sensitive analyses are on average 5.6 times faster than the brute force approach on our SPLs, and that they have different time and space tradeoffs.
机译:软件产品线(SPL)通常是使用注释性方法(例如条件编译)开发的,这些方法带有构造错误产品的固有风险。因此,必须能够分析SPL。但是,由于数据流分析技术无法处理SPL,因此开发人员必须单独生成和分析所有有效方法,这对于非平凡的SPL来说是昂贵的。在本文中,我们演示了如何进行任何标准的过程内数据流分析,并以三种不同方式自动将其转换为对特征敏感的数据流分析。它们都能够分析SPL的所有有效方法,而不必显式生成所有方法。我们已将所有分析作为SOOT的过程内数据流分析框架的扩展进行了实施,并在四个定性不同的SPL上通过实验评估了它们的性能和存储特性。结果表明,对特征敏感的分析平均比我们的SPL上的蛮力方法快5.6倍,并且它们在时间和空间上有不同的权衡。

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