Comparative Analysis of Deep Learning Architectures for Predicting Software Quality Metrics in Behavior-Driven and Test-Driven Development Approaches
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Keywords:
Deep Learning Architectures, Tabular Data, Predictive Modeling, Multi-Layer Perceptron (MLP), Comparative Analysis
AbstractThe impact of software development methodologies on quality metrics is a crucial area of study in empirical software engineering. This research evaluates the performance of three deep learning architectures: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), in predicting key software quality indicators, including maintainability index, test coverage, and code complexity, for projects developed using Behavior-Driven Development (BDD) and Test-Driven Development (TDD) approaches. Using a static tabular dataset containing software quality metrics, the models are evaluated based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the R^2 coefficient. The MLP achieves the best performance, with the lowest RMSE (6.41) and MAE (6.34) and the highest R^2 value (−4.21), demonstrating its suitability for tabular data. The CNN performs moderately, while the LSTM underperforms due to its reliance on temporal dependencies absent from the dataset. These results emphasize the need for careful architectural alignment with dataset characteristics. The findings contribute to understanding the predictive power of deep learning models in software quality analysis and highlight the potential of MLP as a robust tool for such predictions. Future work can explore hybrid models and domain-specific feature engineering to enhance prediction accuracy.Downloads
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ReferencesPargaonkar, S. (2023). Synergizing requirements engineering and quality assurance: A comprehensive exploration in software quality engineering. International Journal of Science and Research (IJSR), 12(8), 2003–2007. Al-Baik, O., Abu Alhija, M., Abdeljaber, H., & Ovais Ahmad, M. (2024). Organizational debt—Roadblock to agility in software engineering: Exploring an emerging concept and future research for software excellence. PLOS ONE, 19(11), e0308183. https://doi.org/10.1371/journal.pone.0308183 Gupta, M. L., Puppala, R., Vadapalli, V. V., Gundu, H., & Karthikeyan, C. V. S. S. (2024). Continuous integration, delivery and deployment: A systematic review of approaches, tools, challenges and practices. In International Conference on Recent Trends in AI Enabled Technologies (pp. 76–89). Springer. https://doi.org/10.1007/978-3-031-59114-3_7 Cui, J. (2024). A comparative study on the impact of test-driven development (TDD) and behavior-driven development (BDD) on enterprise software delivery effectiveness. arXiv preprint arXiv:2411.04141. https://doi.org/10.48550/arXiv.2411.04141 Natarajan, T., & Pichai, S. (2024). Behaviour-driven development and metrics framework for enhanced agile practices in scrum teams. Information and Software Technology, 170, 107435. https://doi.org/10.1016/j.infsof.2024.107435 Rahman, S., & Nadia, F. (2024). Pioneering testing technologies: Advancing software quality through innovative methodologies and frameworks. Journal of Artificial Intelligence and Machine Learning in Management, 8(2), 44–70. Yuan, X., & Tang, X. (2024). Relative effectiveness of morphological analysis training and context clue training on multidimensional vocabulary knowledge. The Journal of Genetic Psychology, 185(2), 77–90. https://doi.org/10.1080/00221325.2024.1234567 Irshad, M., Britto, R., & Petersen, K. (2021). Adapting behavior-driven development (BDD) for large-scale software systems. Journal of Systems and Software, 177, 110944. https://doi.org/10.1016/j.jss.2021.110944 Parsa, S., Zakeri-Nasrabadi, M., & Turhan, B. (2025). Testability-driven development: An improvement to the TDD efficiency. Computer Standards & Interfaces, 91, 103877. https://doi.org/10.1016/j.csi.2025.103877 Razavi, S. (2021). Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling. Environmental Modelling & Software, 144, 105159. https://doi.org/10.1016/j.envsoft.2021.105159 Thesing, T., Feldmann, C., & Burchardt, M. (2021). Agile versus waterfall project management: Decision model for selecting the appropriate approach to a project. Procedia Computer Science, 181, 746–756. https://doi.org/10.1016/j.procs.2021.12.094 Ahmed, S. (2023). A software framework for predicting the maize yield using modified multi-layer perceptron. Sustainability, 15(4), 3017. https://doi.org/10.3390/su15043017 Shu, X., & Ye, Y. (2023). Knowledge discovery: Methods from data mining and machine learning. Social Science Research, 110, 102817. https://doi.org/10.1016/j.ssresearch.2023.102817 Smart, J. F., & Molak, J. (2023). BDD in action: Behavior-driven development for the whole software lifecycle. Simon and Schuster. Krzywanski, J., Sosnowski, M., Grabowska, K., Zylka, A., Lasek, L., & Kijo-Kleczkowska, A. (2024). Advanced computational methods for modeling, prediction and optimization—a review. Materials, 17(14), 3521. https://doi.org/10.3390/ma17143521 Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023). Deep learning modelling techniques: Current progress, applications, advantages, and challenges. Artificial Intelligence Review, 56(11), 13521–13617. https://doi.org/10.1007/s10462-023-10420-9 Yogi. (2024). TDD and BDD comparison dataset. Kaggle. Retrieved November 19, 2024, from https://www.kaggle.com/datasets/yogi2727/tdd-and-bdd-comparison |
Published
2024-12-31
Section
Articles
How to Cite
Airlangga, G. (2024). Comparative Analysis of Deep Learning Architectures for Predicting Software Quality Metrics in Behavior-Driven and Test-Driven Development Approaches. Jurnal Informatika Ekonomi Bisnis, 6(4), 792-798. https://doi.org/10.37034/infeb.v6i4.1045
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