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Dan Li
UCSI University
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Program defect prediction model based on topology aware node evaluation pool graph topology model Dan Li; Poh Soon JosephNg; Peng Yin Choo; Koo Yuen Phan; Wong See Wan
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp583-593

Abstract

Traditional fuzzing struggles with efficiency, as maximizing code coverage does not guarantee the discovery of additional vulnerabilities. To solve this, the study introduces topology-aware node evaluation (TANE-Pool), a deep learning model that proactively predicts defects to guide the fuzzing process. The model analyzes the structural properties of a program’s attributed control flow graph (ACFG) via a diffusion attention mechanism. This process identifies fragile code regions and generates a static vulnerability score (SVS) for each basic block. The fuzzer then uses this score to prioritize test cases, concentrating its efforts on the areas most likely to contain flaws. Evaluated on the Juliet test suite and a set of real-world programs, TANE Pool demonstrates superior prediction accuracy. Its integration into a fuzzer significantly enhances the rate of vulnerability discovery, proving that a defect-prediction-guided approach is a more efficient and effective strategy for software security testing.
Co-Authors Koo Yuen Phan Peng Yin Choo Poh Soon JosephNg Wong See Wan