Applying program analyses to Software Product Lines (SPLs) has been a fundamental research problem at the intersection of Product Line Engineering and software analysis. Different attempts have been made to “lift” particular product-level analyses to run on the entire product line. In this paper, we tackle the class of Datalog-based analyses (e.g., pointer and taint analyses), study the theoretical aspects of lifting Datalog inference, and implement a lifted inference algorithm inside the Soufflé Datalog engine. We evaluate our implementation on a set of Java and C-language benchmark annotative software product lines. We show significant savings in processing time and fact database size (billions of times faster on one of the benchmarks) compared to brute-force analysis of each product individually.

中文翻译：

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使用可变性感知数据记录的注释性软件产品线分析

将程序分析应用于软件产品线 (SPL) 一直是产品线工程和软件分析交叉领域的一个基本研究问题。已经进行了不同的尝试来“提升”特定产品级别的分析以在整个产品线上运行。在本文中，我们解决了基于 Datalog 的分析类（例如，指针和污点分析），研究了提升 Datalog 推理的理论方面，并在 Soufflé Datalog 引擎中实现了提升推理算法。我们在一组 Java 和 C 语言基准注释软件产品线上评估我们的实施。与单独对每个产品进行强力分析相比，我们显示了处理时间和事实数据库大小的显着节省（在其中一个基准上快了数十亿倍）。