Publication2024
A Comparative Analysis for Software Fault Detection Using Machine Learning Techniques
- Industry, Innovation and Infrastructure
- Innovation, Science and Technology
The record
- Type
- Publication
- Date
- 1 January 2024
- Level
- National
- Journal
- 5th International Conference on Electronics and Sustainable Communication Systems Icesc 2024 Proceedings
- Pages
- 1252-1256
- Published
- 1 January 2024
- DOI
- 10.1109/icesc60852.2024.10689843
- Scopus ID
- 85206628137
- Authors
- 6
- Open access
- No
- Citations
- 1
- International co-authorship
- No
- Co-author countries
- IN
Classification
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UN Sustainable Development Goals
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Machine learning classification models for software defect prediction achieve 98-100% accuracy, improving software quality assurance and development engineering practices.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-3 Innovation, Science and Technology
Derived through the seeded SDG-to-Viksit-Bharat crosswalk from the primary SDG 9 label. Machine learning classification models for software defect prediction achieve 98-100% accuracy, improving software quality assurance and development engineering practices.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- 1
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Citing this record
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KIIT Deemed to be University. “A Comparative Analysis for Software Fault Detection Using Machine Learning Techniques”. Kortex.