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All Journal Data Science: Journal of Computing and Applied Informatics
Astrid Pranadani
Universitas Amikom Yogyakarta
Computer Science & IT

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Size-Controlled Opcode Ablation for Smart Contract Vulnerability Detection Astrid Pranadani; Dhani Ariatmanto
Data Science: Journal of Computing and Applied Informatics Vol. 10 No. 2 (2026): Data Science: Journal of Computing and Applied Informatics (JoCAI) In Press
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v10i2.26398

Abstract

Smart contract vulnerability detection requires evaluation protocols that separate real representation signal from dataset-specific artifacts. DIVE provides lifecycle-based tabular features for Ethereum smart contracts, but benchmark performance alone cannot show whether a dominant feature group is useful or only benefits from having many columns. This study examines Opcode Distribution features using 22,330 contracts, 397 processed features, and eight DASP-aligned vulnerability labels. Five multi-label learning configurations were evaluated under 3 x 5 repeated cross-validation, followed by global feature-group ablation, size-controlled random opcode ablation, per-label degradation analysis, cumulative stability analysis, and opcode-profile group-aware robustness checking. MultiOutput LightGBM achieved the best baseline performance, with Micro-F1 of 0.91396, Macro-F1 of 0.82464, and Macro-PR-AUC of 0.90146. Removing the full Opcode Distribution group reduced Macro-F1 to 0.78745, while removing a same-sized random opcode subset produced Macro-F1 of 0.82404. The findings indicate that Opcode Distribution acts as a collective predictive representation rather than a feature-count artifact, without implying causal vulnerability mechanisms.
Co-Authors Dhani Ariatmanto