mlsanityData to 7 Oct 2026

SoK: Machine Learning for Continuous Integration

arXiv 2304.02829 ↗First posted 6 Apr 2023cs.SE cs.LGcited by 4

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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.

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

Builds on

7 of 45 references.

References · 7

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2017Continuous Integration, Delivery and Deployment: A Systematic Review on Approaches, Tools, Challenges and Practicescited by 22
  3. 2021Test Case Selection and Prioritization Using Machine Learning: A Systematic Literature Reviewcited by 12
  4. 2021DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testingcited by 8
  5. 2021Scalable and Accurate Test Case Prioritization in Continuous Integration Contextscited by 6
  6. 2020Predictive Models in Software Engineering: Challenges and Opportunitiescited by 5
  7. 2016Data stream mining for predicting software build outcomes using source code metricscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 0; 2026: 1.

4 papers.