# Targeted Test Selection Approach in Continuous Integration

arXiv: https://arxiv.org/abs/2509.10279 · HTML: https://mlsanity.com/paper/2509.10279/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 57 references that are in the map.
- First posted on arXiv 12 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [StarCoder 2 and The Stack v2: The Next Generation](https://mlsanity.com/paper/2402.19173/) (2024)
- [Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration](https://mlsanity.com/paper/1811.04122/) (2018)
- [Reinforcement Learning for Test Case Prioritization](https://mlsanity.com/paper/2011.01834/) (2020)
- [Predictive Test Selection](https://mlsanity.com/paper/1810.05286/) (2018)
- [DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing](https://mlsanity.com/paper/2110.07443/) (2021)
- [Systematic Literature Review on Application of Learning-based Approaches in Continuous Integration](https://mlsanity.com/paper/2406.19765/) (2024)
- [Test2Vec: An Execution Trace Embedding for Test Case Prioritization](https://mlsanity.com/paper/2206.15428/) (2022)
- [Efficient Incremental Code Coverage Analysis for Regression Test Suites](https://mlsanity.com/paper/2410.21798/) (2024)
- [Neural Network Embeddings for Test Case Prioritization](https://mlsanity.com/paper/2012.10154/) (2020)

## Built on by (most cited first, 1 of 1)

- [FastCI: Efficient GPU-Intensive CI for LLM Training Frameworks](https://mlsanity.com/paper/2610.01967/) (2026)

## Abstract (arXiv)

In modern software development change-based testing plays a crucial role. However, as codebases expand and test suites grow, efficiently managing the testing process becomes increasingly challenging, especially given the high frequency of daily code commits. We propose Targeted Test Selection (T-TS), a machine learning approach for industrial test selection. Our key innovation is a data representation that represent commits as Bags-of-Words of changed files, incorporates cross-file and additional predictive features, and notably avoids the use of coverage maps. Deployed in production, T-TS was comprehensively evaluated against industry standards and recent methods using both internal and public datasets, measuring time efficiency and fault detection. On live industrial data, T-TS selects only 15% of tests, reduces execution time by 5.9×, accelerates the pipeline by 5.6×, and detects over 95% of test failures. The implementation is publicly available to support further research and practical adoption.