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Related Experiment Video

Updated: Apr 18, 2026

Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

792

AI-augmented reliability in CI/CD: a framework for predictive, adaptive, and self-correcting pipelines.

Rohit Dhawan1, Mohit Dhawan1

  • 1Independent Researcher, Edmonds, WA, United States.

Frontiers in Artificial Intelligence
|April 17, 2026
PubMed
Summary
This summary is machine-generated.

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Modern CI/CD pipelines are transformed into intelligent systems using the SAPAL loop, reducing flaky tests and improving delivery velocity. This adaptive framework enhances reliability and efficiency for scalable software development.

Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • DevOps

Background:

  • Static CI/CD pipelines struggle with delivery velocity due to flaky tests and pipeline noise, causing significant delays.
  • Scaling systems exacerbate these issues, threatening the core principles of continuous delivery.

Purpose of the Study:

  • Introduce a framework to convert deterministic CI/CD pipelines into intelligent, adaptive systems.
  • Address challenges of flakiness, noise, and scalability in modern software delivery.

Main Methods:

  • Develop the Sense-Analyze-Predict-Act-Learn (SAPAL) loop, integrating CI/CD specific capabilities.
  • Implement a five-layer architecture for data collection, reliability intelligence, predictive modeling, adaptive execution, and human-AI collaboration.
  • Introduce metrics: Pipeline Health Index, Test Stability Score, and Failure Prediction Confidence.
Keywords:
CI/CDDevOpsadaptive systemsartificial intelligencemachine learningpipeline automationpredictive analyticssoftware reliability

Related Experiment Videos

Last Updated: Apr 18, 2026

Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

792

Main Results:

  • Projected 60% reduction in flaky test-induced build failures through intelligent retry strategies.
  • Potential 50-80% reduction in feedback time using ML-based test selection.
  • Stability-aware deployment orchestration adapts rollouts to regional reliability.

Conclusions:

  • The SAPAL framework offers a practical path to reliable, scalable CI/CD delivery.
  • Enables pipelines to learn, predict, and adapt, making intelligence essential for modern development.