Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Aleksandar Petrovic
ORCID logo ,
Luka Jovanovic
,
Nebojsa Bacanin
* ORCID logo ,
Milos Antonijevic
ORCID logo ,
Nikola Savanovic
ORCID logo ,
Miodrag Zivkovic
ORCID logo ,
Marina Milovanovic
,
Vuk Gajic
ORCID logo
Version 1 : Received: 31 August 2024 / Approved: 1 September 2024 / Online: 2 September 2024 (13:33:25 CEST)

How to cite: Petrovic, A.; Jovanovic, L.; Bacanin, N.; Antonijevic, M.; Savanovic, N.; Zivkovic, M.; Milovanovic, M.; Gajic, V. Exploring Metaheuristic Optimized Machine Learning for Software Defect Detection on Natural Language and Classical Datasets. Preprints 2024, 2024090021. https://doi.org/10.20944/preprints202409.0021.v1 Petrovic, A.; Jovanovic, L.; Bacanin, N.; Antonijevic, M.; Savanovic, N.; Zivkovic, M.; Milovanovic, M.; Gajic, V. Exploring Metaheuristic Optimized Machine Learning for Software Defect Detection on Natural Language and Classical Datasets. Preprints 2024, 2024090021. https://doi.org/10.20944/preprints202409.0021.v1

Abstract

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 a modified algorithm is 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 CNN achieved an accuracy of 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.

Keywords

Natural language processing; Software error detection; Metaheuristic; Optimization; XGBoost; AdaBoost; Convolutional neural networks; explainable artificial intelligence

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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