# Software engineering for artificial intelligence and machine learning software: A systematic literature review

arXiv: https://arxiv.org/abs/2011.03751 · HTML: https://mlsanity.com/paper/2011.03751/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2020.

## Built on by (most cited first, 15 of 15)

- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners](https://mlsanity.com/paper/2304.00078/) (2023)
- [The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice](https://mlsanity.com/paper/2301.09001/) (2023)
- [Maintainability Challenges in ML: A Systematic Literature Review](https://mlsanity.com/paper/2408.09196/) (2024)
- [How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions](https://mlsanity.com/paper/2409.07192/) (2024)
- [Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review](https://mlsanity.com/paper/2102.07574/) (2021)
- [Artificial Intelligence Impact On The Labour Force -- Searching For The Analytical Skills Of The Future Software Engineers](https://mlsanity.com/paper/2302.13229/) (2023)
- [Designing Machine Learning Toolboxes: Concepts, Principles and Patterns](https://mlsanity.com/paper/2101.04938/) (2021)
- [Systematic Mapping Study on the Machine Learning Lifecycle](https://mlsanity.com/paper/2103.10248/) (2021)
- ["Project smells" -- Experiences in Analysing the Software Quality of ML Projects with mllint](https://mlsanity.com/paper/2201.08246/) (2022)
- [What is Software Quality for AI Engineers? Towards a Thinning of the Fog](https://mlsanity.com/paper/2203.12697/) (2022)
- [A Framework to Model ML Engineering Processes](https://mlsanity.com/paper/2404.18531/) (2024)
- [Engineering an Intelligent Essay Scoring and Feedback System: An Experience Report](https://mlsanity.com/paper/2103.13590/) (2021)
- [Scalability and Maintainability Challenges and Solutions in Machine Learning: Systematic Literature Review](https://mlsanity.com/paper/2504.11079/) (2025)
- [Automatic Identification of Machine Learning-Specific Code Smells](https://mlsanity.com/paper/2508.02541/) (2025)

## Abstract (arXiv)

Artificial Intelligence (AI) or Machine Learning (ML) systems have been widely adopted as value propositions by companies in all industries in order to create or extend the services and products they offer. However, developing AI/ML systems has presented several engineering problems that are different from those that arise in, non-AI/ML software development. This study aims to investigate how software engineering (SE) has been applied in the development of AI/ML systems and identify challenges and practices that are applicable and determine whether they meet the needs of professionals. Also, we assessed whether these SE practices apply to different contexts, and in which areas they may be applicable. We conducted a systematic review of literature from 1990 to 2019 to (i) understand and summarize the current state of the art in this field and (ii) analyze its limitations and open challenges that will drive future research. Our results show these systems are developed on a lab context or a large company and followed a research-driven development process. The main challenges faced by professionals are in areas of testing, AI software quality, and data management. The contribution types of most of the proposed SE practices are guidelines, lessons learned, and tools.