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

机译:D-Cube:使用PIN从多线程应用程序中进行动态设计发现的工具

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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.
机译:程序理解是系统维护的主要挑战。逆向工程已用于应用程序的控制流分析,但是对于理解多线程应用程序的并发非确定性行为并没有做太多的工作。我们介绍了使用动态工具API构建的D-CUBE,该API在执行期间插入并在运行时为多线程应用程序推断各种线程模型,例如并发性,安全性,数据访问,线程池状态,异常模型等。我们提取根据预先指定的逻辑跟踪的运行时事件,并将其馈送到决策树以进行推断。我们使用3个基准套件(LOC:50-3200)-CDAC Pthreads基准[1](18例),开放式POSIX测试套件[2](21例)和PARSEC 3.0基准[3](3例)以确保准确性批量测试,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%以上的准确性。 D-CUBE生成带有每个推论的图形事件跟踪,以快速有效地理解大型代码。

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