Fault Prediction for Large Scale Projects Using Deep Learning Techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Selvi:2022:ICAIS,

• author = "R. Thirumalai Selvi and P Patchaiammal",
• booktitle = "2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS)",
• title = "Fault Prediction for Large Scale Projects Using Deep Learning Techniques",
• year = "2022",
• pages = "482--489",
• abstract = "Fault detection in any industry at an early level is important. However, not that much research can use historical data collection to provide the technical analysis for software fault. Therefore, considerable exertions are needed to demonstrate how software improvement is attained with the maintenance of the historical fault database. Finding new patterns from data forms the structure of algorithms and assists in feature extraction and prediction. Fault data extraction plays an important role in rework-related problems. The success of the fault model is only possible by finding the origin of a fault. Genetic programming makes this process optimal. This classified fault domain knowledge learns the patterns and identifies the effective strategies for rework reduction by hybridization with deep learning. In this current study, we present analytical information known as fault taxonomy obtained from the fault commit history to construct a regression model to predict the fault in software using the labels of the fault and its URL shows considerable correlation with the number of faults. The approach also motivates the investigation and comparison of the software quality before and after the software fault taxonomy, data preprocessing and the level of entropy in the datasets.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ICAIS53314.2022.9743054",
• month = feb,
• notes = "Also known as \cite{9743054}",

}

Genetic Programming entries for R Thirumalai Selvi P Patchaiammal

Citations