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

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  • Aleksandar Petrovic

    (Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
    These authors contributed equally to this work.)

  • Luka Jovanovic

    (Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
    These authors contributed equally to this work.)

  • Nebojsa Bacanin

    (Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
    Department of Mathematics, Saveetha School of Engineering, SIMATS, Thandalam, Chennai 602105, Tamilnadu, India
    MEU Research Unit, Middle East University, Amman 11822, Jordan
    These authors contributed equally to this work.)

  • Milos Antonijevic

    (Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
    These authors contributed equally to this work.)

  • Nikola Savanovic

    (Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
    These authors contributed equally to this work.)

  • Miodrag Zivkovic

    (Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
    These authors contributed equally to this work.)

  • Marina Milovanovic

    (Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
    These authors contributed equally to this work.)

  • Vuk Gajic

    (Department of Environment and Sustainable Development, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
    These authors contributed equally to this work.)

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 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.

Suggested Citation

  • Aleksandar Petrovic & Luka Jovanovic & Nebojsa Bacanin & Milos Antonijevic & Nikola Savanovic & Miodrag Zivkovic & Marina Milovanovic & Vuk Gajic, 2024. "Exploring Metaheuristic Optimized Machine Learning for Software Defect Detection on Natural Language and Classical Datasets," Mathematics, MDPI, vol. 12(18), pages 1-45, September.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:18:p:2918-:d:1481402
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    References listed on IDEAS

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    1. Ganesh Dash & Chetan Sharma & Shamneesh Sharma, 2023. "Sustainable Marketing and the Role of Social Media: An Experimental Study Using Natural Language Processing (NLP)," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
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