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Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Open Access Research Article

Adaptive optimization framework for multimodal software defect prediction using reinforcement learning

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pp. 2351–2362Vol. 46Issue 7October 2025DOI: 10.47974/JIOS-2162XML
Received:
05 Mar 2025
Published Online:
31 Oct 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2162
Pages:
2351–2362

Abstract

Software reliability has continued to be one of the most acute problems in contemporary software engineering, especially as codebase complexity grows, release cycles speed up and the workforce grows more heterogeneous. This paper presents an adaptive optimization framework multimodal software defect prediction design based on reinforcement learning that can dynamically control the combination of heterogeneous data modalities. However, as opposed to standard fusion strategies, the reinforcement learning agent will constantly modify the modality contributions, as it gets feedback on their performance measurements, such as F1-score and false negative rate.  The method can improve accuracy, robustness, as well as establishing a base towards scalable, interpretable, and self-optimizing defect prediction systems in industrial software analytics.

Keywords

Subject Classifications

68-0468T07

References

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