# Detecting Continuous Integration Skip : A Reinforcement Learning-based Approach

arXiv: https://arxiv.org/abs/2405.09657 · HTML: https://mlsanity.com/paper/2405.09657/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods.
- Builds on 13 of its 53 references that are in the map.
- First posted on arXiv 15 May 2024.

## Placed in

- Deep reinforcement learning methods / [RL training of language and agent models](https://mlsanity.com/deeprl/rl-training-of-language-and-agent-models/)

## Builds on (most cited first, 13 of 13)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space](https://mlsanity.com/paper/1810.06394/) (2018)
- [Stakeholders in Explainable AI](https://mlsanity.com/paper/1810.00184/) (2018)
- [Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces](https://mlsanity.com/paper/1905.04388/) (2019)
- [Test Prioritization in Continuous Integration Environments](https://mlsanity.com/paper/1809.00143/) (2018)
- [Automated Inference of Software Library Usage Patterns](https://mlsanity.com/paper/1612.01626/) (2016)
- [Identifying Components from Object-Oriented APIs Based on Dynamic Analysis](https://mlsanity.com/paper/1803.06235/) (2018)
- [Kubernetes as an Availability Manager for Microservice Applications](https://mlsanity.com/paper/1901.04946/) (2019)
- [API Misuse Detection An Immune System inspired Approach](https://mlsanity.com/paper/2012.14078/) (2020)
- [A Kubernetes Controller for Managing the Availability of Elastic Microservice Based Stateful Applications](https://mlsanity.com/paper/2012.14086/) (2020)
- [Towards Using Package Centrality Trend to Identify Packages in Decline](https://mlsanity.com/paper/2107.10168/) (2021)
- [A Hierarchical-DBSCAN Method for Extracting Microservices from Monolithic Applications](https://mlsanity.com/paper/2206.07010/) (2022)

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

- [Reinforcement Learning for Dynamic Workflow Optimization in CI/CD Pipelines](https://mlsanity.com/paper/2601.11647/) (2026)

## Abstract (arXiv)

The software industry is experiencing a surge in the adoption of Continuous Integration (CI) practices, both in commercial and open-source environments. CI practices facilitate the seamless integration of code changes by employing automated building and testing processes. Some frameworks, such as Travis CI and GitHub Actions have significantly contributed to simplifying and enhancing the CI process, rendering it more accessible and efficient for development teams. Despite the availability these CI tools , developers continue to encounter difficulties in accurately flagging commits as either suitable for CI execution or as candidates for skipping especially for large projects with many dependencies. Inaccurate flagging of commits can lead to resource-intensive test and build processes, as even minor commits may inadvertently trigger the Continuous Integration process. The problem of detecting CI-skip commits, can be modeled as binary classification task where we decide to either build a commit or to skip it. This study proposes a novel solution that leverages Deep Reinforcement Learning techniques to construct an optimal Decision Tree classifier that addresses the imbalanced nature of the data. We evaluate our solution by running a within and a cross project validation benchmark on diverse range of Open-Source projects hosted on GitHub which showcased superior results when compared with existing state-of-the-art methods.