A5051664828- Software Testing and Debugging Techniques
- Software Reliability and Analysis Research
- Software Engineering Research
- Software-Defined Networks and 5G
- Software System Performance and Reliability
Chongqing University
2022-2024
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...
Abstract Deep‐Learning‐based Fault Localisation (DLFL) leverages deep neural networks to learn the relationship between statement behaviour and program failures, showing promising results. However, since DLFL uses failures as labels conduct supervised learning, a labelled dataset is requisite of applying DLFL. A failure detected by comparing output with test oracle which standard answer for given input. The problem is, oracles are often difficult, or even impossible acquire in real life,...