Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)

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Adaptive Neuro-Fuzzy Inference Expert System for Agile-Inspired Software Development

Authors
Dharmendra Pathak1, *, Mohit Arora1
1Lovely Professional University, Punjab, India
*Corresponding author. Email: dharmendra.32553@lpu.co.in
Corresponding Author
Dharmendra Pathak
Available Online 26 May 2025.
DOI
10.2991/978-94-6463-716-8_36How to use a DOI?
Keywords
Agile; Expert System; Machine Learning; Software Effort Estimation; Story Point
Abstract

Precise effort prediction in Agile settings where needs often shift, is a big hurdle due to a lack of trustworthy data tools and dependence on past project know-how. Due to this, poor guesses and project failures may happen. To address this issue, we have created Agilator, an expert system to bridge the gap between real and guessed user story efforts. We have integrated machine learning models i.e., Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms to provide optimal predictions. We have tested Agilator on datasets containing 162 data points with important attributes like “Number of Story Points” and “Project Speed”. The model achieved a training variance score of 0.99, R2 score of 0.99, and Root Mean Squared Error (RMSE) of 2.00. In testing, we got a variance score of 0.99, R2 score of 0.99, and RMSE of 1.93, which is better than other traditional algorithms. Our model has also used real-time adjustment and visual data profiling, which leads to optimal predictions. These outcomes show its potential as a useful and reliable tool to estimate Agile effort and manage projects.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
26 May 2025
ISBN
978-94-6463-716-8
ISSN
1951-6851
DOI
10.2991/978-94-6463-716-8_36How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Dharmendra Pathak
AU  - Mohit Arora
PY  - 2025
DA  - 2025/05/26
TI  - Adaptive Neuro-Fuzzy Inference Expert System for Agile-Inspired Software Development
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 456
EP  - 468
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_36
DO  - 10.2991/978-94-6463-716-8_36
ID  - Pathak2025
ER  -