# AI Techniques for Software Requirements Prioritization

arXiv: https://arxiv.org/abs/2108.00832 · HTML: https://mlsanity.com/paper/2108.00832/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [An Efficient Diagnosis Algorithm for Inconsistent Constraint Sets](https://mlsanity.com/paper/2102.09005/) (2021)
- [Developer Prioritization in Bug Repositories](https://mlsanity.com/paper/1704.04764/) (2017)
- [Asymmetric Release Planning-Compromising Satisfaction against Dissatisfaction](https://mlsanity.com/paper/1901.05130/) (2019)
- [Towards Utility-based Prioritization of Requirements in Open Source Environments](https://mlsanity.com/paper/2102.08638/) (2021)

## Built on by (most cited first, 3 of 3)

- [Configuring Multiple Instances with Multi-Configuration](https://mlsanity.com/paper/2109.09696/) (2021)
- [Reconsidering Requirements Engineering: Human-AI Collaboration in AI-Native Software Development](https://mlsanity.com/paper/2510.04380/) (2025)
- [Towards Human-AI Synergy in Requirements Engineering: A Framework and Preliminary Study](https://mlsanity.com/paper/2510.25016/) (2025)

## Abstract (arXiv)

Aspects such as limited resources, frequently changing market demands, and different technical restrictions regarding the implementation of software requirements (features) often demand for the prioritization of requirements. The task of prioritization is the ranking and selection of requirements that should be included in future software releases. In this context, an intelligent prioritization decision support is extremely important. The prioritization approaches discussed in this paper are based on different Artificial Intelligence (AI) techniques that can help to improve the overall quality of requirements prioritization processes