Master Thesis: Beyond Git: Source Control Architecture for Agentic Software Development

Technology, Data & Digital · Software & Web Development · Software Engineering · DevOps · Data Science

In short

This Master Thesis project investigates the behavior of conventional Git-based source control with hundreds of concurrent AI coding agents and compares it with alternative architectures. The project involves building an experimental environment to simulate agent-scale development and evaluate scalability, merge behavior, CI impact, and semantic integration failures.

Responsibilities

Investigate how conventional Git-based source control behaves with hundreds of autonomous AI coding agents working concurrently on a shared codebase.
Compare alternative architectures for agent-scale development.
Build an experimental environment simulating up to approximately 500 concurrent agents.
Evaluate scalability, merge behavior, CI impact, resource consumption, and semantic integration failures.
Research similar solutions at other large-scale software companies.
Define areas of exploration within the project.
Design realistic agent workloads and task distributions.
Define a reproducible setup for running and scaling simulations.
Select representative codebases of different sizes and modularity.
Characterize Git behavior under agent-scale load, including repository growth, throughput, contention, conflict rates, rebase loops, and queue delays.
Analyze impact on CI, including build/test load, queueing, and wasted compute.
Analyze resource consumption (compute, memory, storage, network).
Investigate semantic integration failures.
Identify and compare alternative architectures (e.g., trunk-based, merge-queue, fine-grained changes, stacked changes).
Define metrics and a benchmarking methodology.
Propose recommendations for evolving source control and CI to support agentic software development.

Requirements

Knowledge in Computer Science, Software Engineering, or a related field.
Solid understanding of version control (especially Git) and continuous integration.
Experience in at least one programming language (Python is a plus).
Solid understanding of Linux environments and bash.
Interest in distributed systems, performance analysis, and experimental evaluation.
Eagerness to learn and explore new concepts.
Experience with LLM-based coding agents or agent frameworks.
Experience with containerization and orchestration (e.g., Docker, Kubernetes).

Skills

GitPythonLinuxBashDockerKubernetes

#AI#Software Development#Git#Source Control#Agentic Software

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