Adaptive Neuro-Fuzzy Inference Expert System for Agile-Inspired Software Development
- 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.
Cite this article
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 -