一句话：提出自动合成全面内存模型 litmus 测试套件的方法，以覆盖弱内存模型的隐蔽边界情况。
内存一致性模型是共享内存架构和编程模型的基础，但现代弱内存模型定义与实现复杂，通常依赖黑盒测试。黑盒测试的有效性要求 litmus 测试套件足够全面，能覆盖模型及其实现中所有隐蔽的边界情况。现有测试套件多由人工构建与随机生成组合而来，而现代内存模型的复杂性使人工方法容易出错且覆盖不完整。本文提出自动合成全面的内存模型 litmus 测试套件，旨在系统化生成测试以提升覆盖范围和测试可靠性。摘要未给出具体合成算法与实验评估结果。
The memory consistency model is a fundamental part of any shared memory architecture or programming model. Modern weak memory models are notoriously difficult to define and to implement correctly. Most real-world programming languages, compilers, and (micro)architectures therefore rely heavily on black-box testing methodologies. The success of such techniques requires that the suite of litmus tests used to perform the testing be comprehensive--it should ideally stress all obscure corner cases of the model and of its implementation. Most litmus test suites today are generated from some combination of manual effort and randomization; however, the complex and subtle nature of contemporary memory models means that manual effort is both error-prone and subject to incomplete coverage.
@inproceedings{LustigWPG17,
title = {Automated Synthesis of Comprehensive Memory Model Litmus Test Suites},
author = {Daniel Lustig and Andrew Wright and Alexandros Papakonstantinou and Olivier Giroux},
booktitle = {ASPLOS},
year = {2017},
doi = {10.1145/3037697.3037723}
}