# AI for Agile development: a Meta-Analysis

arXiv: https://arxiv.org/abs/2305.08093 · HTML: https://mlsanity.com/paper/2305.08093/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 14 May 2023.

## Builds on (most cited first, 8 of 8)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry](https://mlsanity.com/paper/1812.05389/) (2018)
- [Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?](https://mlsanity.com/paper/2103.08942/) (2021)
- [Towards Human-Bot Collaborative Software Architecting with ChatGPT](https://mlsanity.com/paper/2302.14600/) (2023)
- [A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks](https://mlsanity.com/paper/2201.10500/) (2022)
- [MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Cases](https://mlsanity.com/paper/2103.08937/) (2021)
- [Predictive Test Selection](https://mlsanity.com/paper/1810.05286/) (2018)
- [DASP: A Framework for Driving the Adoption of Software Security Practices](https://mlsanity.com/paper/2205.12388/) (2022)

## Built on by (most cited first, 1 of 1)

- [Cognitive Agents Powered by Large Language Models for Agile Software Project Management](https://mlsanity.com/paper/2508.16678/) (2025)

## Abstract (arXiv)

This study explores the benefits and challenges of integrating Artificial Intelligence with Agile software development methodologies, focusing on improving continuous integration and delivery. A systematic literature review and longitudinal meta-analysis of the retrieved studies was conducted to analyse the role of Artificial Intelligence and it's future applications within Agile software development. The review helped identify critical challenges, such as the need for specialised socio-technical expertise. While Artificial Intelligence holds promise for improved software development practices, further research is needed to better understand its impact on processes and practitioners, and to address the indirect challenges associated with its implementation.