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Intraprocedural Dataflow Analysis for Software Product Lines

机译:软件产品线的内部数据流分析

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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 a 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(SPLS)通常使用有条件编译等注释方法来开发,具有构建错误产品的固有风险。因此,必须能够分析SPL。但是,随着数据流分析技术无法处理SPLS,开发人员必须单独生成和分析所有有效方法,这对于非微不足道的SPL昂贵。在本文中,我们演示了如何采取任何标准的跨型计算机数据流分析,并以三种不同的方式自动将其转换为特征敏感的数据流分析。所有所有人都能够分析SPL的所有有效方法,而无需明确地生成所有这些方法。我们已经实施了所有分析,作为烟灰的跨型数据流分析框架的扩展,并在四个定性不同的SPL上进行实验评估它们的性能和内存特性。结果表明,特征敏感分析平均比我们的SPL上的蛮力方法快5.6倍,并且它们具有不同的时间和空间权衡。

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