Spectrum-Based Multiple Fault Localization

2009 ◽  
Author(s):  
Rui Abreu ◽  
Peter Zoeteweij ◽  
Arjan J.C. van Gemund
Keyword(s):  
Fault Localization ◽  
Multiple Fault

scholarly journals Research of Multiple Fault Localization Based on Cluster Analysis of Program Failures

2017 ◽  
Vol 10 (8) ◽  
pp. 63-74
Author(s):  
Xiaoan Bao ◽  
Yusen Wang ◽  
Junyan Qian ◽  
Zijian Xiong ◽  
Na Zhang ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Fault Localization ◽  
Multiple Fault

Fault Localization Analysis for Multiple Fault Diagnosis in Multi-Station Assembly Systems

2007 ◽  
Author(s):  
Posheng Tsai ◽  
Kamal Mannar ◽  
Darek Ceglarek
Keyword(s):  
Manufacturing Systems ◽  
Large Scale ◽  
Fault Localization ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Isolation ◽  
Data Driven ◽  
Multiple Fault ◽  
Critical Measure ◽  
The Impact

Modern large scale multi-station manufacturing systems require effective variation reduction to improve the final assembly dimensional quality. One critical measure is to diagnose the fault in the process using knowledge-based root cause identification, which can be very challenging due to the complexity of the system. The paper investigates the need of data-driven fault localization to enhance the diagnosability within the context of multiple-fault scenario(s) in multi-station assembly processes where multivariate measurements are used. The paper proposes three types of fault-signal transmission in assembly system and illustrates the nature of structured noise. Moreover, the impact of structured noise on the diagnosability is illustrated on two major fault isolation methods, namely, Principal Component Analysis and Independent Component Analysis. We then propose to use data-driven fault localization to reduce the structured noise effect and enhance the diagnosability. A simulation case study based on automotive panel assembly model is provided to illustrate the impact of structured noise and the need for data-driven localization.

scholarly journals Research on Software Multiple Fault Localization Method Based on Machine Learning

2018 ◽  
Vol 232 ◽  
pp. 01060 ◽  
Author(s):  
Meng Gao ◽  
Pengyu Li ◽  
Congcong Chen ◽  
Yunsong Jiang
Keyword(s):  
Machine Learning ◽  
Fault Location ◽  
Fault Localization ◽  
Systematic Research ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Machine Learning Classification ◽  
The Status ◽  
Coupling Fault ◽  
Program Spectrum

Fault localization is one of time-consuming and labor-intensive activity in the debugging process. Consequently, there is a strong demand for techniques that can guide software developers to the locations of faults in a program with high accuracy and minimal human intervention. Despite the research of neural network and decision tree has made some progress in software multiple fault localization, there is still a lack of systematic research on various algorithms of machine learning. Therefore, a novel machine-learning-based multiple faults localization is proposed in this paper. First, several concepts and connotation of software multiple fault localization are introduced, move on to the status and development trends of the research. Next, the principles of machine learning classification algorithm are explained. Then, a software multiple fault localization research framework based on machine learning is proposed. The process is taking the Mid function as an example, compares and analyzes the performance of 22 machine learning models in software multiple fault localization. Finally, the optimal machine learning method is verified in the multiple fault localization of the Siemens suite dataset. The experimental results show that the machine learning based on Random Forest algorithm has more accuracy and significant positioning efficiency. This paper effectively solved the problem of large amount of program spectrum data and multi-coupling fault location, which is very helpful for improving the efficiency of software multiple fault debugging.

Multiple fault localization of software programs: A systematic literature review

2020 ◽  
Vol 124 ◽  
pp. 106312 ◽  
Author(s):  
Abubakar Zakari ◽  
Sai Peck Lee ◽  
Rui Abreu ◽  
Babiker Hussien Ahmed ◽  
Rasheed Abubakar Rasheed
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Fault Localization ◽  
Multiple Fault ◽  
Software Programs

scholarly journals Spectrum-based Fault Localization Techniques Application on Multiple-Fault Programs: A Review

2020 ◽  
pp. 41-48
Author(s):  
Abubakar Zakari ◽  
Shamsu Abdullahi ◽  
Nura Modi Shagari ◽  
Abubakar Bello Tambawal ◽  
Nuruddeen Musa Shanono ◽  
...  
Keyword(s):  
Software Industry ◽  
Fault Localization ◽  
Research Trends ◽  
Fault Isolation ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Program Debugging ◽  
High Utilization ◽  
Software Fault ◽  
Parallel Debugging

Software fault localization is one of the most tedious and costly activities in program debugging in the endeavor to identify faults locations in a software program. In this paper, the studies that used spectrum-based fault localization (SBFL) techniques that makes use of different multiple fault localization debugging methods such as one-bug-at-a-time (OBA) debugging, parallel debugging, and simultaneous debugging in localizing multiple faults are classified and critically analyzed in order to extensively discuss the current research trends, issues, and challenges in this field of study. The outcome strongly shows that there is a high utilization of OBA debugging method, poor fault isolation accuracy, and dominant use of artificial faults that limit the existing techniques applicability in the software industry.

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