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Learning a graph-based classifier for fault localization

Science China(Information Sciences) 2020年第6期63卷 195-216页

作者： Hao ZHONG Hong MEI Department of Computer Science and Engineering Shanghai Jiao Tong University

Because software emerged, locating software faults has been intensively researched, culminating in various approaches and tools that have been applied in real development. Despite the success of these developments, improved tools are still demanded by programmers. Meanwhile, some programmers are reluctant to use any tools when locating faults in their development. The state-of-the-art situation can be naturally improved by learning how programmers locate faults. The rapid development of open-source software has accumulated many bug fixes. A bug fix is a specific type of comments containing a set of buggy files and their corresponding fixed files, which reveal how programmers repair bugs. Feasibly, an automatic model can learn fault locations from bug fixes, but prior attempts to achieve this vision have been prevented by various technical challenges. For example, most bug fixes are not compilable after checking out, which hinders analyzing bug fixes by most advanced static/dynamic tools. This paper proposes an approach called ClaFa that trains a graph-based fault classifier from bug fixes. ClaFa is built on a recent partial-code tool called Grapa, which enables the analysis of partial programs by the complete code tool called WALA. Once Grapa has built a program dependency graph from a bug fix, ClaFa compares the graph from the buggy code with the graph from the fixed code, locates the buggy nodes, and extracts the various graph features of the buggy and clean nodes. Based on the extraction result, ClaFa trains a classifier that combines Adaboost and decision tree learning. The trained ClaFa can predict whether a node of a program dependency graph is buggy or *** evaluate ClaFa on thousands of buggy files collected from four open-source projects: Aries, Mahout,Derby, and Cassandra. The f-scores of ClaFa achieves are approximately 80% on all projects.

关键词： fault classifier partial code analysis bug fix analysis

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A simple decision tree-based disturbance monitoring system for VSC-based HVDC transmission link integrating a DFIG wind farm

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Protection and Control of Modern Power Systems 2022年第1期7卷 363-381页

作者： Rajesh Babu Damala Rajesh Kumar Patnaik Ashish Ranjan Dash Department of EEE Centurion University of Technology and ManagementParalakhemundiOdishaIndia&GMR Institute of TechnologyRajamAndhrapradeshIndia Department of EEE GMR Institute of TechnologyRajamSrikakulamAndhra PradeshIndia Department of EEE Centurion University of Technology and ManagementParalakhemundiOdishaIndia

fault detection and classification is a key challenge for the protection of High Voltage DC(HVDC)transmission *** this paper,the Teager-Kaiser Energy Operator(TKEO)algorithm associated with a decision tree-based fault classi-fier is proposed to detect and classify various DC *** Change Identification Filter is applied to the average and differential current components,to detect the first instant of fault occurrence(above threshold)and register a Change Identified Point(CIP).Further,if a CIP is registered for a positive or negative line,only three samples of currents(i.e.,CIP and each side of CIP)are sent to the proposed TKEO algorithm,which produces their respective 8 indices through which the,fault can be detected along with its *** new approach enables quicker detection allowing utility grids to be restored as soon as *** novel approach also reduces computing complexity and the time required to identify faults with *** importance and accuracy of the proposed scheme are also thor-oughly tested and compared with other methods for various faults on HVDC transmission lines.

关键词： Change Identification Filter Differential current DC faults Simple Decision Tree fault classifier HVDC transmission link Renewable Energy TKEO algorithm

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fault Diagnosis Based on Knowledge Extracted from Neurofuzzy Networks Using Binary and Real-Valued fault Databases

Fault Diagnosis Based on Knowledge Extracted from Neurofuzzy...

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第二十七届中国控制会议

作者： Mok Hingtung Department of Mechanical Engineering The University of Hong Kong

In this paper,an online fault diagnosis scheme for nonlinear systems is derived from fuzzy rules extracted from the neurofuzzy network that models the residuals of the *** the neurofuzzy network is updated online by the recursive least squares method,the proposed technique is able to diagnose faults *** initiate the fault diagnosis scheme,a binary or real-valued fault database is constructed first from fuzzy rules extracted from each of the neurofuzzy networks that model the possible faults in the *** are diagnosed online by comparing the currently extracted fuzzy rules with those in the fault database using a *** an illustration,a nonlinear DC motor control system is used to illustrate the implementation of the proposed diagnosis schemes,and the performance of the binary and real-valued fault databases is compared.

关键词： fault Diagnosis DC Motor Neurofuzzy Network fault classifier

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