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公开(公告)号：US08538897B2
公开(公告)日：2013-09-17
申请号：US12960015
申请日：2010-12-03
CPC分类号： G06F11/079 , G06F11/3409 , G06F11/3447 , H04L41/0636 , H04L41/16
摘要： Techniques and systems for cross-trace scalable issue detection and clustering that scale-up trace analysis for issue detection and root-cause clustering using a machine learning based approach are described herein. These techniques enable a scalable performance analysis framework for computing devices addressing issue detection, which is designed as a multiple scale feature for learning based on issue detection, and root cause clustering. In various embodiments the techniques employ a cross-trace similarity model, which is defined to hierarchically cluster problems detected in the learning based issue detection via butterflies of trigram stacks. The performance analysis framework is scalable to manage millions of traces, which include high problem complexity.
摘要翻译： 本文描述了用于交叉跟踪可扩展问题检测和聚类的技术和系统，其中使用基于机器学习的方法对用于问题检测和根本原因聚类进行放大跟踪分析。 这些技术使得可扩展的性能分析框架用于处理问题检测的计算设备，其被设计为基于问题检测和根本原因聚类的用于学习的多尺度特征。 在各种实施例中，该技术采用交叉跟踪相似性模型，其被定义为通过三元组栈的蝴蝶在基于学习的问题检测中分层检测集群问题。 性能分析框架是可扩展的，以管理数百万条跟踪，其中包括高问题复杂性。
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公开(公告)号：US20120143795A1
公开(公告)日：2012-06-07
申请号：US12960015
申请日：2010-12-03
CPC分类号： G06F11/079 , G06F11/3409 , G06F11/3447 , H04L41/0636 , H04L41/16
摘要： Techniques and systems for cross-trace scalable issue detection and clustering that scale-up trace analysis for issue detection and root-cause clustering using a machine learning based approach are described herein. These techniques enable a scalable performance analysis framework for computing devices addressing issue detection, which is designed as a multiple scale feature for learning based issue detection, and root cause clustering. In various embodiments the techniques employ a cross-trace similarity model, which is defined to hierarchically cluster problems detected in the learning based issue detection via butterflies of trigram stacks. The performance analysis framework is scalable to manage millions of traces, which include high problem complexity.
摘要翻译： 本文描述了用于交叉跟踪可扩展问题检测和聚类的技术和系统，其中使用基于机器学习的方法对用于问题检测和根本原因聚类进行放大跟踪分析。 这些技术使得可扩展的性能分析框架用于处理问题检测的计算设备，其被设计为用于基于学习的问题检测的多尺度特征以及根本原因聚类。 在各种实施例中，该技术采用交叉跟踪相似性模型，其被定义为通过三元组栈的蝴蝶在基于学习的问题检测中分层检测集群问题。 性能分析框架是可扩展的，以管理数百万条跟踪，其中包括高问题复杂性。
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公开(公告)号：US20120278659A1
公开(公告)日：2012-11-01
申请号：US13095336
申请日：2011-04-27
申请人： Shi Han , Yingnong Dang , Song Ge , Dongmei Zhang
发明人： Shi Han , Yingnong Dang , Song Ge , Dongmei Zhang
IPC分类号： G06F11/36
CPC分类号： G06F11/3072 , G06F11/302 , G06F11/323 , G06F11/3466 , G06F11/3612 , G06F2201/865
摘要： A call pattern database is mined to identify frequently occurring call patterns related to program execution instances. An SVM classifier is iteratively trained based at least in part on classifications provided by human analysts; at each iteration, the SVM classifier identifies boundary cases, and requests human analysis of these cases. The trained SVM classifier is then applied to call pattern pairs to produce similarity measures between respective call patterns of each pair, and the call patterns are clustered based on the similarity measures.
摘要翻译： 调用模式数据库被开采以识别与程序执行实例有关的频繁出现的调用模式。 至少部分基于人类分析师提供的分类，对SVM分类器进行迭代训练; 在每个迭代中，SVM分类器识别边界情况，并请求对这些情况的人类分析。 然后将经过训练的SVM分类器应用于呼叫模式对以在每对的相应呼叫模式之间产生相似性度量，并且基于相似性度量来呼叫模式聚类。
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公开(公告)号：US09348852B2
公开(公告)日：2016-05-24
申请号：US13095415
申请日：2011-04-27
申请人： Shi Han , Yingnong Dang , Song Ge , Dongmei Zhang
发明人： Shi Han , Yingnong Dang , Song Ge , Dongmei Zhang
IPC分类号： G06F17/30
CPC分类号： G06F17/30539 , G06F17/30306 , H04L67/10
摘要： A system for frequent pattern mining uses two layers of processing: a plurality of computing nodes, and a plurality of processors within each computing node. Within each computing node, the data set against which the frequent pattern mining is to be performed is stored in shared memory, accessible concurrently by each of the processors. The search space is partitioned among the computing nodes, and sub-partitioned among the processors of each computing node. If a processor completes its sub-partition, it requests another sub-partition. The partitioning and sub-partitioning may be performed dynamically, and adjusted in real time.
摘要翻译： 用于频繁模式挖掘的系统使用两层处理：多个计算节点和每个计算节点内的多个处理器。 在每个计算节点内，将要执行频繁模式挖掘的数据集存储在共享存储器中，由每个处理器并发访问。 搜索空间在计算节点之间划分，并在每个计算节点的处理器之间进行子分区。 如果处理器完成其子分区，则它请求另一个子分区。 可以动态执行分区和子分区，并实时调整。
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公开(公告)号：US20120278346A1
公开(公告)日：2012-11-01
申请号：US13095415
申请日：2011-04-27
申请人： Shi Han , Yingnong Dang , Song Ge , Dongmei Zhang
发明人： Shi Han , Yingnong Dang , Song Ge , Dongmei Zhang
IPC分类号： G06F17/30
CPC分类号： G06F17/30539 , G06F17/30306 , H04L67/10
摘要： A system for frequent pattern mining uses two layers of processing: a plurality of computing nodes, and a plurality of processors within each computing node. Within each computing node, the data set against which the frequent pattern mining is to be performed is stored in shared memory, accessible concurrently by each of the processors. The search space is partitioned among the computing nodes, and sub-partitioned among the processors of each computing node. If a processor completes its sub-partition, it requests another sub-partition. The partitioning and sub-partitioning may be performed dynamically, and adjusted in real time.
摘要翻译： 用于频繁模式挖掘的系统使用两层处理：多个计算节点和每个计算节点内的多个处理器。 在每个计算节点内，将要执行频繁模式挖掘的数据集存储在共享存储器中，由每个处理器并发访问。 搜索空间在计算节点之间划分，并在每个计算节点的处理器之间进行子分区。 如果处理器完成其子分区，则它请求另一个子分区。 可以动态执行分区和子分区，并实时调整。
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公开(公告)号：US08578213B2
公开(公告)日：2013-11-05
申请号：US13095269
申请日：2011-04-27
申请人： Shi Han , Yingnong Dang , Song Ge , Dongmei Zhang , Bin Zhao , Feng Liang , Chao Bian , Xiangpeng Zhao , Cong Chen , Hang Li , Prashant Ratanchandani
发明人： Shi Han , Yingnong Dang , Song Ge , Dongmei Zhang , Bin Zhao , Feng Liang , Chao Bian , Xiangpeng Zhao , Cong Chen , Hang Li , Prashant Ratanchandani
IPC分类号： G06F11/00
CPC分类号： G06F11/3636
摘要： Execution traces are collected from multiple execution instances that exhibit performance issues such as slow execution. Call stacks are extracted from the execution traces, and the call stacks are mined to identify frequently occurring function call patterns. The call patterns are then clustered, and used to identify groups of execution instances whose performance issues may be caused by common problematic program execution patterns.
摘要翻译： 从执行缓慢执行的性能问题的多个执行实例中收集执行跟踪。 从执行跟踪中提取调用堆栈，并且调用堆栈被挖掘以识别经常出现的函数调用模式。 呼叫模式然后被聚集，并用于识别其性能问题可能由常见的有问题的程序执行模式引起的执行实例组。
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公开(公告)号：US20120278658A1
公开(公告)日：2012-11-01
申请号：US13095269
申请日：2011-04-27
申请人： Shi Han , Yingnong Dang , Song Ge , Dongmei Zhang , Bin Zhao , Feng Liang , Chao Bian , Xiangpeng Zhao , Cong Chen , Hang Li , Prashant Ratanchandani
发明人： Shi Han , Yingnong Dang , Song Ge , Dongmei Zhang , Bin Zhao , Feng Liang , Chao Bian , Xiangpeng Zhao , Cong Chen , Hang Li , Prashant Ratanchandani
IPC分类号： G06F11/36
CPC分类号： G06F11/3636
摘要： Execution traces are collected from multiple execution instances that exhibit performance issues such as slow execution. Call stacks are extracted from the execution traces, and the call stacks are mined to identify frequently occurring function call patterns. The call patterns are then clustered, and used to identify groups of execution instances whose performance issues may be caused by common problematic program execution patterns.
摘要翻译： 从执行缓慢执行的性能问题的多个执行实例中收集执行跟踪。 从执行跟踪中提取调用堆栈，并且调用堆栈被挖掘以识别经常出现的函数调用模式。 呼叫模式然后被聚集，并用于识别其性能问题可能由常见的有问题的程序执行模式引起的执行实例组。
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公开(公告)号：US09147271B2
公开(公告)日：2015-09-29
申请号：US11820636
申请日：2007-06-20
申请人： Yingnong Dang , Dongmei Zhang , Min Wang , Xiaohui Hou , Jian Wang
发明人： Yingnong Dang , Dongmei Zhang , Min Wang , Xiaohui Hou , Jian Wang
CPC分类号： G06T11/206 , G06F21/577
摘要： A method for enabling graphical representation of aggregated data is provided. The method includes accessing aggregated data retrieved from a plurality of on-line sources and receiving selection of a portion of the data through a graphical user interface. The method further includes identifying attributes associated with the portion of the data and generating a graphical representation of the portion of the data.
摘要翻译： 提供了一种用于实现聚合数据的图形表示的方法。 该方法包括访问从多个在线源检索的聚合数据，并通过图形用户界面接收对部分数据的选择。 所述方法还包括识别与所述数据的所述部分相关联的属性并且生成所述数据的所述部分的图形表示。
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公开(公告)号：US20120159434A1
公开(公告)日：2012-06-21
申请号：US12972535
申请日：2010-12-20
申请人： Yingnong Dang , Sadi Khan , Dongmei Zhang , Weipeng Liu , Song Ge , Gong Cheng
发明人： Yingnong Dang , Sadi Khan , Dongmei Zhang , Weipeng Liu , Song Ge , Gong Cheng
IPC分类号： G06F9/44
摘要： A code verification system is described herein that provides augmented code review with code clone analysis and visualization to help software developers automatically identify similar instances of the same code and to visualize differences in versions of software code over time. The system uses code clone search technology to identify code clones and to present the user with information about similar code as the developer makes changes. The system may provide automated notification to the developer or to other teams as changes are made to code segments with one or more related clones. The code verification system also helps the developer to understand architectural evolution of a body of software code. The code verification system provides an analysis component for determining architectural differences based on the code clone detection result between the two versions of the software code base. The code verification system also provides a user interface component for displaying identified differences to developers and others involved with the software development process in intuitive and useful ways.
摘要翻译： 本文描述了一种代码验证系统，其通过代码克隆分析和可视化来提供增强的代码审查，以帮助软件开发人员自动识别相同代码的相似实例，并可视化软件代码随时间的版本的差异。 该系统使用代码克隆搜索技术来识别代码克隆，并向用户呈现与开发者进行更改相似的代码的信息。 系统可以向开发人员或其他团队提供自动通知，因为对具有一个或多个相关克隆的代码段进行了更改。 代码验证系统还可以帮助开发人员了解一系列软件代码的架构演变。 代码验证系统基于软件代码库的两个版本之间的代码克隆检测结果提供了用于确定架构差异的分析组件。 代码验证系统还提供了一个用户界面组件，用于以直观和有用的方式向开发人员和与软件开发过程相关的其他人显示识别的差异。
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公开(公告)号：US20120137182A1
公开(公告)日：2012-05-31
申请号：US12957090
申请日：2010-11-30
申请人： Dongmei Zhang , Yingnong Dang , Song Ge
发明人： Dongmei Zhang , Yingnong Dang , Song Ge
IPC分类号： G06F11/00
CPC分类号： G06F11/0781 , G06F11/0775 , G06F11/0778 , G06F11/0787
摘要： Techniques for error report processing are described herein. Error reports, received by a developer due to program crashes, may be organized into a plurality of “buckets.” The buckets may be based in part on a name and a version of the application associated with a crash. Additionally, a call stack of the computer on which the crash occurred may be associated with each error report. The error reports may be “re-bucketed” into meta-buckets to provide additional information to programmers working to resolve software errors. The re-bucketing may be based in part on measuring similarity of call stacks of a plurality of error reports. The similarity of two call stacks—a measure of likelihood that two error reports were caused by a same error—may be based in part on functions in common, a distance of those functions from the crash point, and an offset distance between the common functions.
摘要翻译： 这里描述了用于错误报告处理的技术。 开发人员由于程序崩溃而收到的错误报告可以被组织成多个“桶”。桶可以部分地基于与崩溃相关联的应用的名称和版本。 此外，发生崩溃的计算机的调用堆栈可能与每个错误报告相关联。 错误报告可能会重新分级到元数据桶中，以便为解决软件错误的程序员提供更多信息。 可以部分地基于测量多个错误报告的调用堆栈的相似度来重新估计。 两个调用堆栈的相似性 - 两个错误报告由相同错误引起的可能性的度量可能部分地基于共同的功能，这些功能与碰撞点的距离以及公共功能之间的偏移距离 。
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