# SoK: Machine Learning for Continuous Integration

arXiv: https://arxiv.org/abs/2304.02829 · HTML: https://mlsanity.com/paper/2304.02829/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 45 references that are in the map.
- First posted on arXiv 6 Apr 2023.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Continuous Integration, Delivery and Deployment: A Systematic Review on Approaches, Tools, Challenges and Practices](https://mlsanity.com/paper/1703.07019/) (2017)
- [Test Case Selection and Prioritization Using Machine Learning: A Systematic Literature Review](https://mlsanity.com/paper/2106.13891/) (2021)
- [DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing](https://mlsanity.com/paper/2110.07443/) (2021)
- [Scalable and Accurate Test Case Prioritization in Continuous Integration Contexts](https://mlsanity.com/paper/2109.13168/) (2021)
- [Predictive Models in Software Engineering: Challenges and Opportunities](https://mlsanity.com/paper/2008.03656/) (2020)
- [Data stream mining for predicting software build outcomes using source code metrics](https://mlsanity.com/paper/1604.05503/) (2016)

## Built on by (most cited first, 4 of 4)

- [Are Latent Vulnerabilities Hidden Gems for Software Vulnerability Prediction? An Empirical Study](https://mlsanity.com/paper/2401.11105/) (2024)
- [Software Vulnerability Prediction in Low-Resource Languages: An Empirical Study of CodeBERT and ChatGPT](https://mlsanity.com/paper/2404.17110/) (2024)
- [Systematic Literature Review on Application of Learning-based Approaches in Continuous Integration](https://mlsanity.com/paper/2406.19765/) (2024)
- [Understanding the Impact of AI Code Assistants on Security API Usage: An Empirical Study](https://mlsanity.com/paper/2607.11348/) (2026)

## Abstract (arXiv)

Continuous Integration (CI) has become a well-established software development practice for automatically and continuously integrating code changes during software development. An increasing number of Machine Learning (ML) based approaches for automation of CI phases are being reported in the literature. It is timely and relevant to provide a Systemization of Knowledge (SoK) of ML-based approaches for CI phases. This paper reports an SoK of different aspects of the use of ML for CI. Our systematic analysis also highlights the deficiencies of the existing ML-based solutions that can be improved for advancing the state-of-the-art.