Inspiration
Software changes can look safe while still introducing hidden regressions in dependencies, authorization logic, APIs, migrations, or runtime behavior.
CHRONOS explores a different approach: treat software changes as experiments that should be analyzed, isolated, tested, and supported by evidence before they are recommended.
What it does
CHRONOS is an Autonomous Software Change Laboratory.
The goal is to analyze a software repository, understand its dependency structure, estimate the blast radius of a requested change, generate competing implementation strategies, execute candidates in isolated change laboratories, run deterministic verification, and compare the resulting evidence.
The core principle is:
AI may propose. Evidence must decide.
Rather than trusting an AI-generated patch simply because it looks correct, CHRONOS is designed to connect recommendations to concrete verification results such as tests, type checking, builds, dependency impact, contracts, migrations, and other repository-specific checks.
How we built it
CHRONOS is currently under active development for this hackathon.
The implementation is being developed as a software engineering system rather than a static prototype. The planned architecture includes repository analysis, a software knowledge graph, blast-radius analysis, candidate change laboratories, deterministic verification gates, evidence lineage, replay, observability, and a developer-focused web interface.
AI tools are being used extensively as development assistants. Their use will be disclosed transparently in the project documentation and final submission.
This section will be updated with the exact final architecture, technologies, implementation decisions, and development process after the working system is completed and verified.
Challenges we ran into
The main engineering challenges being explored include:
- understanding relationships inside an unfamiliar codebase;
- distinguishing known dependency information from inference;
- isolating competing software changes;
- preventing AI reasoning from becoming the authority for correctness;
- connecting every important conclusion to reproducible evidence;
- designing meaningful verification rather than relying on a single test-suite result;
- presenting complex software impact in an understandable interface.
The final submission will document the actual challenges encountered during implementation.
Accomplishments that we're proud of
CHRONOS is still under active development.
Final accomplishments and measured results will be added only after they have been demonstrated by the completed system.
What we learned
This section will be completed from the actual development log rather than written retrospectively before implementation.
What's next for CHRONOS — Autonomous Software Change Laboratory
The immediate objective is to complete and independently verify the first end-to-end CHRONOS experiment, then refine the product based on the evidence produced by that implementation.
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