Building Intelligent CI Systems: Reducing Build Overhead with Prediction, Dependency Analysis and Automated Repair

Continuous Integration (CI) has become an essential practice in modern software development, enabling rapid feedback through automated building and testing of software systems. However, projects grow in size and complexity. As a consequence, such CI pipelines increasingly suffer from high execution costs, dependency-related failures, and build breakages that require substantial developer effort to diagnose and repair.
In this talk, I will present my research agenda on intelligent build engineering that combines prediction, validation, and automated repair techniques to improve the efficiency and reliability of software builds.
First, I will discuss approaches for anticipating build outcomes and identifying skippable CI commits, leveraging both changes to source code and to build code, along with complexity measures, to enable more efficient CI utilization. Next, I will introduce my approaches for validating dependencies in build configurations and automatically detecting and repairing dependency conflicts, including approaches that leverage large language models to modify source code when configuration-level fixes are insufficient.
Finally, I will explore the emerging role of AI agents in build maintenance. This includes evaluating whether large language models can replace traditional build log analyzers, as well as presenting agent-based approaches for diagnosing dependency-related failures and repairing such build breakages automatically. In summary, my research directions investigate how predictive analytics, dependency management, large language models, and autonomous agents may enable future CI systems that are faster, more resilient, and increasingly self-healing.