A5031789677- Software Engineering Research
- Software Testing and Debugging Techniques
- Software System Performance and Reliability
- Anomaly Detection Techniques and Applications
- Software Reliability and Analysis Research
- Fault Detection and Control Systems
- Innovations in Concrete and Construction Materials
- Asphalt Pavement Performance Evaluation
- Adversarial Robustness in Machine Learning
- Power Transformer Diagnostics and Insulation
- Innovative concrete reinforcement materials
- Machine Learning and Data Classification
- Engineering Diagnostics and Reliability
- Advanced Decision-Making Techniques
- Technology and Security Systems
- Power Systems and Technologies
- Spectroscopy and Chemometric Analyses
- Geotechnical Engineering and Underground Structures
- Imbalanced Data Classification Techniques
- Water Quality Monitoring and Analysis
- Infrastructure Maintenance and Monitoring
- Evaluation Methods in Various Fields
- Concrete and Cement Materials Research
- Smart Grid and Power Systems
- Advanced Malware Detection Techniques
Chongqing University
2021-2024
Changsha University of Science and Technology
2021-2024
Changchun Institute of Technology
2019
Shanghai Jiao Tong University
2009
Data is the fuel to models, and it still applicable in fault localization (FL). Many existing elaborate FL techniques take code coverage matrix failure vector as inputs, expecting could find correlation between program entities failures. However, input data high-dimensional extremely unbalanced since real-world programs are large size number of failing test cases much less than that passing cases, which posing severe threats effectiveness techniques.
Fault localization aims at developing an effective methodology identifying suspicious statements potentially responsible for program failures. The spectrum-based fault is the widely used by analyzing statistical coincidences viewed from spectrum to evaluate suspiciousness of each statement being faulty. However, just in coverage information perspective and without combining diverse amount may restrict effectiveness. Thus, this article proposes feature-based ( <monospace...
Fault localization (FL) analyzes the execution information of a test suite to pinpoint root cause failure. The class imbalance suite, i.e., imbalanced proportion between passing cases (i.e., majority class) and failing ones minority class), adversely affects FL effectiveness.To mitigate effect in FL, we propose CGAN4FL: data augmentation approach using Context-aware Generative Adversarial Network for Localization. Specifically, CGAN4FL uses program dependencies construct failure-inducing...
Automated fault localization (FL) techniques collect runtime information as input data and then analyze to identify the relationship between program statements failures. They usually take advantages of statistics develop a suspiciousness evaluation methodology (e.g., spectrum-based formulas deep neural network models) by exploring underlying correlation rooted in data. Thus, quality is critical for FL. In actual process development, developers seek generate adequate test cases testing...
Automated fault localization techniques collect runtime information as input data to identify suspicious statement potentially responsible for program failures. To discover the statistical coincidences between test results (i.e., failing or passing) and executions of different statements a executed not executed), researchers developed suspiciousness methodology (e.g., spectrum-based formulas deep neural network models). However, occurrences coincidental correctness (CC) which means faulty...
Datasets of real-world bugs shipped with human-written patches are intensively used in the evaluation existing automated program repair (APR) techniques, wherein always serve as ground truth, for manual or assessment approaches, to evaluate correctness test-suite adequate patches. An inaccurate patch tangled other code changes will pose threats reliability results. Therefore, construction such datasets requires much effort on isolating real bug fixes from fixing commits. However, work is...
Fault localization seeks to locate the suspicious statements possible for causing a program failure. Experimental evidence shows that fault effectiveness is affected adversely by existence of coincidental correctness (CC) test cases, where CC case denotes which executes but no failure occurs. Even worse, cases are prevailing in realistic testing and debugging, leading severe issue on effectiveness. Thus, it indispensable accurately detect alleviate their harmful effect effectiveness.To...
Context: Fault localization (FL) is essentially a search over the space of program statements to find suspicious entities that might have caused failure. However, real-world programs are high dimensional and imbalanced, which limits effectiveness efficiency existing FL methods. The state-of-the-art method (Aeneas) solves imbalanced high-dimensional problem but in complex time-consuming process.Objective: Due limited original methods low data augmentation Aeneas, this paper proposes Lamont ,...
This paper is a systematic study of artificial neural network applications for the diagnosis power transformer incipient fault. Diagonal recurrent (DRNN) used to realize intelligent fault oil-filled based on dissolved gas-in-oil analysis(DGA). In order obtain better detection results and improve accuracy detection, photoacoustic spectroscopy (PAS) technique applied on-line monitoring system. To overcome disadvantages BP algorithm, new recursive prediction error (RPE) algorithm proposed...
The high quality of input data serves as the foundation for various tasks. Inaccurate may decrease effectiveness elaborate algorithms and significantly impact output. This also applies to fault localization, accurate reliable is crucial effective localization techniques. Many techniques analyze coverage information detecting bug positions. However, source suffers from problems, such imbalanced coincidental correctness. These problems make unreliable localization. To mitigate potential...
A test suite is indispensable for fault localization by providing useful execution information of its cases locating suspicious statements being faulty. There exists a type known as coincidental correctness (CC) cases, which executes the faulty statement whereas produces anticipated output. The existing studies have shown CC harmfully impact effectiveness. Therefore, it crucial to detect mitigate adverse on localization.To address this issue, we propose ContraCC: detection method using...
This paper proposes a transformer operating state evaluation method based on fault tree analysis (FTA), which analyzes and counts various possible faults of the transformer, classifies basic events through establishment sorting, in order to find out necessary conditions that affect reliability transformer. While adopting method, upstream approximate calculation probability are used analyze calculate known factors logically related or unrelated summarized into several minimum cutset elements,...