参与者系统的目标测试生成

Sihan Li, Farah Hariri, G. Agha
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引用次数: 6

摘要

本文研究了参与者系统的目标测试生成问题。具体地说,我们提出了一种方法来支持系统级测试的生成,以覆盖参与者系统中给定的代码位置。测试生成方法包括两个阶段。首先,静态分析用于根据消息流图(MFG)构建整个参与者系统的抽象。MFG捕获在程序中定义的潜在参与者交互。其次,执行从目标位置到参与者系统的“入口点”的向后符号执行(BSE)。BSE使用在目标测试生成方法的第一阶段中构造的MFG来指导参与者之间的执行。由于并发性导致了巨大的搜索空间,可以通过BSE进行探索,因此我们使用两种启发式方法结合反馈导向技术来减少搜索空间。我们在Java Akka程序的工具Tap中实现了我们的方法,并在Savina基准测试和四个开源项目上对Tap进行了评估。我们的评估表明,Tap实现了相对较高的目标覆盖率(在1,000个目标上达到78%),并检测到受试者中六个以前未报告的错误。2012 ACM学科分类软件及其工程→软件测试与调试
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Targeted Test Generation for Actor Systems
This paper addresses the problem of targeted test generation for actor systems. Specifically, we propose a method to support generation of system-level tests to cover a given code location in an actor system. The test generation method consists of two phases. First, static analysis is used to construct an abstraction of an entire actor system in terms of a message flow graph (MFG). An MFG captures potential actor interactions that are defined in a program. Second, a backwards symbolic execution (BSE) from a target location to an “entry point” of the actor system is performed. BSE uses the MFG constructed in the first phase of our targeted test generation method to guide execution across actors. Because concurrency leads to a huge search space which can potentially be explored through BSE, we prune the search space by using two heuristics combined with a feedback-directed technique. We implement our method in Tap, a tool for Java Akka programs, and evaluate Tap on the Savina benchmarks as well as four open source projects. Our evaluation shows that the Tap achieves a relatively high target coverage (78% on 1,000 targets) and detects six previously unreported bugs in the subjects. 2012 ACM Subject Classification Software and its engineering → Software testing and debugging
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