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All Journal Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Radityo Adi Nugroho
Department of Computer Science, Faculty of Mathematics and Natural Science, Lambung Mangkurat University, Banjarbaru, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology

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Metaheuristic-Based Hyperparameter Optimization Analysis of Deep Neural Network for Cross-Project Defect Prediction in Mobile Applications Maulana Abdul Rahman; Rudy Herteno; Radityo Adi Nugroho; Friska Abadi; Setyo Wahyu Saputro
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 2 (2026): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i2.340

Abstract

Software Defect Prediction (SDP) plays a strategic role in identifying software defects during the early stages of development, thereby enabling more efficient allocation of testing resources, particularly in the rapidly evolving mobile application domain characterized by fast release cycles. The commonly used Within-Project Defect Prediction (WPDP) approach is often constrained by the limited availability of historical data, especially in projects at early stages of development. As an alternative, Cross-Project Defect Prediction (CPDP) leverages historical data from other projects as training sources. Moreover, the performance of the Deep Neural Network (DNN) used in SDP is highly dependent on accurate hyperparameter configurations, where manual tuning requires substantial time and computational resources without guaranteeing optimal results. To address this issue, this study analyzes and compares the effectiveness of three metaheuristic algorithms, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO), in optimizing DNN hyperparameters within a CPDP framework. This study utilizes 14 open-source Android mobile application projects and employs the Leave-One-Out Cross-Validation technique. The performance of each combination is evaluated using ROC-AUC as the primary metric. The Wilcoxon Signed-Rank Test with a Bonferroni correction is used to assess the statistical significance of the observed performance differences. The experimental results demonstrate that GWO-DNN achieves the best performance, with an average ROC-AUC of 0.721, and is the only combination that remains statistically significant after Bonferroni correction, with a small effect size based on Cliff’s delta. Overall, the findings of this study indicate that metaheuristic-based hyperparameter tuning is a sufficiently effective approach for improving the capability of DNN in cross-project software defect prediction within the mobile application domain, although the observed improvements remain moderate.
Comparative Evaluation of TabKANet with Oversampling and Feature Selection Ablation for Software Defect Prediction Muhammad Faza Azhiman Saputra; Setyo Wahyu Saputro; Mohammad Reza Faisal; Radityo Adi Nugroho; Andi Farmadi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 3 (2026): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i3.351

Abstract

Software defect prediction (SDP) focuses limited testing resources on the modules most likely to fail, but real-world software metric data are tabular, noisy, and severely class-imbalanced, which degrades conventional learners. The Kolmogorov-Arnold Network (KAN) and Transformer architectures recently achieved strong results on tabular data, yet their combined form, TabKANet, has not been evaluated for SDP, nor has the contribution of common preprocessing techniques been quantified. This study adapts and comparatively evaluates TabKANet against established baselines and measures the contribution of oversampling and feature selection through a structured ablation. Twelve all-numerical NASA Metrics Data Program datasets were used. The pipeline applied duplicate removal, MinMax normalization, effective class weighting, and stratified five-fold cross-validation, with oversampling (SMOTE) and Recursive Feature Elimination (RFE) inserted inside the training folds. Four TabKANet variants (A: base, B: +SMOTE, C: +RFE, D: +SMOTE+RFE) were compared with Multi-Layer Perceptron (MLP), standalone KAN, and TabNet, and differences were tested with the Wilcoxon signed-rank test at a 0.05 significance level. The base TabKANet (variant A) achieved the highest mean AUC of 0.7603, slightly ahead of MLP (0.7594) and KAN (0.7583) and well above TabNet (0.7092). Its advantage over TabNet was significant (p = 0.002), whereas it was statistically equivalent to MLP and KAN (p = 0.733). TabNet attained the highest recall (0.739) but the lowest precision (0.228), indicating over-prediction of defects, while TabKANet kept precision and recall balanced. In the ablation, SMOTE significantly reduced AUC (p = 0.042), RFE caused no significant change (p = 0.733), and their combination stayed neutral (p = 0.266). TabKANet therefore performed best without additional resampling. TabKANet is thus a competitive architecture for all-numerical, highly imbalanced SDP, matching strong neural baselines and surpassing TabNet, where effective class weighting alone suffices and SMOTE is counter-productive.
Co-Authors Andi Farmadi Friska Abadi Maulana Abdul Rahman Mohammad Reza Faisal Muhammad Faza Azhiman Saputra Rudy Herteno Setyo Wahyu Saputro