Publication2019
Effort estimation in agile software development using experimental validation of neural network models
- Industry, Innovation and Infrastructure
- Innovation, Science and Technology
The record
- Type
- Publication
- Date
- 1 September 2019
- Level
- National
- Journal
- International Journal of Information Technology Singapore
- Volume
- 11
- Issue
- 3
- Pages
- 569-573
- Published
- 1 September 2019
- DOI
- 10.1007/s41870-018-0131-2
- Scopus ID
- 85091838553
- ISSN
- 25112104
- Authors
- 3
- Open access
- No
- Citations
- 57
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Neural network models for effort estimation in agile software development improve project planning and resource management methodologies.
- 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. Neural network models for effort estimation in agile software development improve project planning and resource management methodologies.
- 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. “Effort estimation in agile software development using experimental validation of neural network models”. Kortex.