# Data stream mining for predicting software build outcomes using source code metrics

arXiv: https://arxiv.org/abs/1604.05503 · HTML: https://mlsanity.com/paper/1604.05503/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2016.

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

- [Systematic Literature Review on Application of Learning-based Approaches in Continuous Integration](https://mlsanity.com/paper/2406.19765/) (2024)
- [SoK: Machine Learning for Continuous Integration](https://mlsanity.com/paper/2304.02829/) (2023)
- [Systematic Literature Review on Application of Machine Learning in Continuous Integration](https://mlsanity.com/paper/2305.12695/) (2023)
- [Practitioners' Challenges and Perceptions of CI Build Failure Predictions at Atlassian](https://mlsanity.com/paper/2402.09651/) (2024)

## Abstract (arXiv)

Software development projects involve the use of a wide range of tools to produce a software artifact. Software repositories such as source control systems have become a focus for emergent research because they are a source of rich information regarding software development projects. The mining of such repositories is becoming increasingly common with a view to gaining a deeper understanding of the development process.