Software Reliability Assessment: An Architectural and Component Impact Analysis

Alyahyan, Saleh; Alatawi, Mohammed Naif; Alnfiai, Mrim M.; Alotaibi, Shoayee Dlaim; Alshammari, Abdullah; Alzaid, Zaid; Alwageed, Hathal Salamah

doi:10.26599/tst.2024.9010101

Tsinghua Sci. Technol.

2025

DOI: 10.26599/tst.2024.9010101

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Software Reliability Assessment: An Architectural and Component Impact Analysis

Saleh Alyahyan,

Mohammed Naif Alatawi,

Mrim M. Alnfiai

et al.

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Exploring Metaheuristic Optimized Machine Learning for Software Defect Detection on Natural Language and Classical Datasets

Petrovic,

Jovanovic,

Bacanin

et al. 2024

Mathematics

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Software is increasingly vital, with automated systems regulating critical functions. As development demands grow, manual code review becomes more challenging, often making testing more time-consuming than development. A promising approach to improving defect detection at the source code level is the use of artificial intelligence combined with natural language processing (NLP). Source code analysis, leveraging machine-readable instructions, is an effective method for enhancing defect detection and error prevention. This work explores source code analysis through NLP and machine learning, comparing classical and emerging error detection methods. To optimize classifier performance, metaheuristic optimizers are used, and algorithm modifications are introduced to meet the study’s specific needs. The proposed two-tier framework uses a convolutional neural network (CNN) in the first layer to handle large feature spaces, with AdaBoost and XGBoost classifiers in the second layer to improve error identification. Additional experiments using term frequency–inverse document frequency (TF-IDF) encoding in the second layer demonstrate the framework’s versatility. Across five experiments with public datasets, the accuracy of the CNN was 0.768799. The second layer, using AdaBoost and XGBoost, further improved these results to 0.772166 and 0.771044, respectively. Applying NLP techniques yielded exceptional accuracies of 0.979781 and 0.983893 from the AdaBoost and XGBoost optimizers.

show abstract

Exploring Metaheuristic Optimized Machine Learning for Software Defect Detection on Natural Language and Classical Datasets

Petrovic,

Jovanovic,

Bacanin

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Software Reliability Assessment: An Architectural and Component Impact Analysis

Cited by 1 publication

References 44 publications

Exploring Metaheuristic Optimized Machine Learning for Software Defect Detection on Natural Language and Classical Datasets

Exploring Metaheuristic Optimized Machine Learning for Software Defect Detection on Natural Language and Classical Datasets

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