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    • 32. 发明授权
    • Variational path profiling
    • 变化路径分析
    • US07607119B2
    • 2009-10-20
    • US11115924
    • 2005-04-26
    • Trishul ChilimbiErez Perelman
    • Trishul ChilimbiErez Perelman
    • G06F9/44
    • G06F11/3612G06F11/3466
    • A run time software test tool instruments a program to perform a low overhead profiling of the program's execution, which records the execution time of frequent acyclic control flow paths using hardware performance counters. An analysis of the profiling data is performed to identify those program paths that have significant variation in execution time across different dynamic traversals in the same program run. This variation (measured as the difference between the fastest execution of that path and slower executions) represents the potential speedup that potentially could be achieved if the paths were optimized (such as by the addition of simple pre-fetch optimizations) to do away with these variations. The variational paths are identified to the programmer to guide optimization.
    • 运行时软件测试工具将程序设置为执行程序执行的低开销性能分析,该程序通过硬件性能计数器来记录频繁的非循环控制流程的执行时间。 执行分析数据的分析,以识别在同一程序运行中在不同动态遍历之间执行时间有显着差异的程序路径。 这种变化(测量为该路径的最快执行与较慢的执行之间的差异)表示如果路径被优化(例如通过添加简单的预取优化)可能可以实现的潜在加速,以消除这些 变化。 变化路径被标识给程序员来指导优化。
    • 33. 发明申请
    • Heap-based bug identification using anomaly detection
    • 基于堆的错误识别使用异常检测
    • US20060265694A1
    • 2006-11-23
    • US11134812
    • 2005-05-20
    • Trishul ChilimbiVinod Ganapathy
    • Trishul ChilimbiVinod Ganapathy
    • G06F9/44
    • G06F11/3612G06F11/3616
    • A dynamic analysis tool uses anomaly detection to find heap-based bugs. In spite of the evolving nature of the heap, programs generally exhibit several of properties of their heap usage that remain stable. Periodically, during the execution of the program, the analysis tool computes a suite of metrics which are sensitive to the state of the heap. These metrics track heap behavior, and the stability of the heap reflects quantitatively in the values of these metrics. The ranges of stable metrics, obtained by running a program on a multiple input training set, are then treated as indicators of correct behavior, and are used in conjunction with an anomaly detector to find heap-based bugs.
    • 动态分析工具使用异常检测来查找基于堆的错误。 尽管堆的性质不断发展,但程序通常表现出其堆使用的几个属性保持稳定。 定期地,在执行程序期间,分析工具计算一组对堆的状态敏感的度量。 这些度量跟踪堆行为,并且堆的稳定性在这些度量的值中定量地反映。 然后将通过运行多输入训练集上的程序获得的稳定度量的范围视为正确行为的指标,并与异常检测器结合使用以查找基于堆的错误。