论文标题
TPGEN：一种基于GPU的自动稳定方法，用于生成素数和测试路径
TPGen: A Self-Stabilizing GPU-Based Method for Prime and Test Paths Generation
论文作者
论文摘要
本文介绍了一种基于新型的基于GPU的测试路径（TPS）和质子路径（PPS）生成的方法，称为TPGEN，用于结构测试和测试数据生成。 TPGEN在时空效率和空间效率上以几个数量级的几个数量级来优于现有的PPS和TPS生成方法。通过设计新的非连接和分层内存分配方法，称为三级路径访问方法（TPAM），可以提高时间和空间效率，从而有效地存储最大简单路径。除了时间和空间效率之外，TPGEN的主要意义还包括其自动化设计，其中线程以完全异步和订单的方式执行，而无需使用任何原子说明。 TPGEN可以生成具有极高循环和NPATH复杂性的结构复杂程序的PPS和TPS。
This paper presents a novel scalable GPU-based method for Test Paths (TPs) and Prime Paths (PPs) Generation, called TPGen, used in structural testing and in test data generation. TPGen outperforms existing methods for PPs and TPs generation in several orders of magnitude, both in time and space efficiency. Improving both time and space efficiency is made possible through devising a new non-contiguous and hierarchical memory allocation method, called Three-level Path Access Method (TPAM), that enables efficient storage of maximal simple paths in memory. In addition to its high time and space efficiency, a major significance of TPGen includes its self-stabilizing design where threads execute in a fully asynchronous and order-oblivious way without using any atomic instructions. TPGen can generate PPs and TPs of structurally complex programs that have an extremely high cyclomatic and Npath complexity.
