D-Cube: Tool for Dynamic Design Discovery from Multi-threaded Applications Using PIN

Srijoni Majumdar, Nachiketa Chatterjee, Shila Rani Sahoo, P. Das
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引用次数: 6

Abstract

Program comprehension is a major challenge for system maintenance. Reverse engineering has been employed for control-flow analysis of applications but not much work has been done for comprehending concurrent non-deterministic behavior of multi-threaded applications. We present D-CUBE, built using dynamic instrumentation APIs, which plugs in during execution and infers various thread models like concurrency, safety, data access, thread-pool state, exception model etc. for multi-threaded applications at runtime. We extract run-time events traced according to pre-specified logic and feed them to decision trees for inference. We use 3 benchmark suites (LOC: 50-3200) -- CDAC Pthreads benchmark [1] (18 Cases), Open POSIX Test-Suites [2] (21 Cases) and PARSEC 3.0 benchmarks [3] (3 Cases) for accuracy and volume testing and validate our approach by comparing the documented behavior of test-suites with D-CUBE's output models. We achieve over 90% accuracy. D-CUBE produces graphical event-traces with every inference for quick and effective comprehension of large code.
D-Cube:使用PIN的多线程应用程序动态设计发现工具
程序理解是系统维护的一个主要挑战。逆向工程已被用于应用程序的控制流分析,但在理解多线程应用程序的并发不确定性行为方面做的工作还不多。我们介绍了D-CUBE,它使用动态instrumentation api构建,在执行过程中插入,并在运行时为多线程应用程序推断各种线程模型,如并发性、安全性、数据访问、线程池状态、异常模型等。我们根据预先指定的逻辑提取跟踪的运行时事件,并将其提供给决策树进行推理。我们使用3个基准测试套件(LOC: 50-3200)——CDAC Pthreads基准测试[1](18例),Open POSIX测试套件[2](21例)和PARSEC 3.0基准测试[3](3例)进行准确性和体积测试,并通过将测试套件的记录行为与D-CUBE的输出模型进行比较来验证我们的方法。我们达到了90%以上的准确率。D-CUBE为每个推理生成图形化事件跟踪,以便快速有效地理解大型代码。
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