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Program defect prediction model based on topology aware node evaluation pool graph topology model.

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Τίτλος: Program defect prediction model based on topology aware node evaluation pool graph topology model.
Συγγραφείς: Li, Dan, JosephNg, Poh Soon, Choo, Peng Yin, Phan, Koo Yuen, Wan, Wong See
Πηγή: International Journal of Advances in Applied Sciences (IJAAS); Jun2026, Vol. 15 Issue 2, p583-593, 11p
Θεματικοί όροι: Deep learning, Defect tracking (Computer software development), Software measurement
Περίληψη: 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, TANEPool 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 in sustainable infrastructure development. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Advances in Applied Sciences (IJAAS) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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  Data: Program defect prediction model based on topology aware node evaluation pool graph topology model.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Dan%22">Li, Dan</searchLink><br /><searchLink fieldCode="AR" term="%22JosephNg%2C+Poh+Soon%22">JosephNg, Poh Soon</searchLink><br /><searchLink fieldCode="AR" term="%22Choo%2C+Peng+Yin%22">Choo, Peng Yin</searchLink><br /><searchLink fieldCode="AR" term="%22Phan%2C+Koo+Yuen%22">Phan, Koo Yuen</searchLink><br /><searchLink fieldCode="AR" term="%22Wan%2C+Wong+See%22">Wan, Wong See</searchLink>
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  Data: International Journal of Advances in Applied Sciences (IJAAS); Jun2026, Vol. 15 Issue 2, p583-593, 11p
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  Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Defect+tracking+%28Computer+software+development%29%22">Defect tracking (Computer software development)</searchLink><br /><searchLink fieldCode="DE" term="%22Software+measurement%22">Software measurement</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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, TANEPool 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 in sustainable infrastructure development. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Advances in Applied Sciences (IJAAS) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.11591/ijaas.v15.i2.pp583-593
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Defect tracking (Computer software development)
        Type: general
      – SubjectFull: Software measurement
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      – TitleFull: Program defect prediction model based on topology aware node evaluation pool graph topology model.
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            – D: 01
              M: 06
              Text: Jun2026
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              Y: 2026
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