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International Journal of Science, Strategic Management and Technology

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HARDWARE-AWARE BUG PREDICTION:LEVERAGING PERFORMANCE COUNTERS FOR EARLY SOFTWARE DEFECT DETECTION

AUTHORS:
TC. Vivek
Mentor
Vanitha
Affiliation
Department of Computer Technology, Dr.N.G.P.Arts and Science College,Coimbatore
DOI: 10.55041/ijsmt.v2i3.302
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

Software bugs present major challenges in software engineering, resulting in increased costs, security vulnerabilities, and system failures. Traditional bug prediction methods focus on source code metrics and historical defect data, often missing low-level hardware characteristics that reveal performance anomalies indicating latent defects. This paper introduces a Hardware-Aware Bug Prediction System (HABPS) using hardware performance counters (HPCs) for software defect detection. Our approach extracts dynamic hardware-level features during execution, including cache miss rates, branch prediction accuracy, pipeline stalls, and memory


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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 03
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Mar 28 2026
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Vivek, T. (2026). Hardware-Aware Bug Prediction:Leveraging Performance Counters for Early Software Defect Detection. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.302

Vivek, TC.. "Hardware-Aware Bug Prediction:Leveraging Performance Counters for Early Software Defect Detection." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.302.

Vivek, TC.. "Hardware-Aware Bug Prediction:Leveraging Performance Counters for Early Software Defect Detection." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.302.

References
1.Menzies, T., et al. ”Defect prediction from static code features.” IEEE TSE, 2010.

2.Yan, J., et al. ”PerfXRay: Diagnosing performance problems via HPCs.” ACM SIGOPS, 2017.

3.Zhang, X., et al. ”Machine Learning in SE: A Systematic Mapping Study.” JSS, 2020.

4.Breiman, L. ”Random Forests.” ML, 2001.

5.Friedman, J. H. ”Greedy function approximation: A gradient boosting machine.” AoS, 2001.
Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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