Akka Tested AI Workflows Across 65 Open Source Projects
Researchers measured model efficiency and performance across software ports, revealing stark variance in outcomes.
Updated on Oct. 5, 2026 in Artificial Intelligence
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Akka has published findings from an experimental study where AI models implemented code across 65 open source projects. The research evaluated the performance, token consumption, and success rates of Claude models during automated software migration.
Why it matters
The study quantifies the viability of using large language models for complex software engineering tasks. It highlights how architectural decisions and model selection dictate whether a codebase sees massive performance gains or significant regressions.
The test consumed 9.41 billion tokens over 99.3 hours, with Claude models implementing 10% of the target surface area. While Opus used 40% fewer tokens than Sonnet, results varied wildly, ranging from a 143,333 times speedup for Dify to a 100 times slowdown for Netflix Metaflow.
The players
Akka
A research entity focused on developing automated AI workflows for software engineering.
Claude
A series of large language models developed by Anthropic, evaluated here for code implementation, testing, and review capabilities.
The details
The Akka workflow utilizes a delivery harness that iterates through discovery, specification, and porting phases, followed by automated verification. Validation protocols rely on unit and integration tests, alongside auditors that check for serialization errors, security vulnerabilities, and architectural boundary violations. This research-stage method attempts to automate the translation of codebases into new environments while maintaining runtime integrity.
Timeline
October 2026: Publication of experimental findings.
The Tech Race
This research follows a growing trend of benchmarking LLMs against real-world software engineering tasks rather than static datasets. It represents a significant step beyond standard coding benchmarks by testing full architectural porting and automated validation.
For developers, these results indicate that current AI-driven code porting is highly experimental and carries significant performance risks. Future adoption will depend on Akka’s planned research into adversarial testing and provenance tracking to ensure code remains reliable after migration.
The takeaway
Automated code migration remains a high-variance task where model choice and project architecture dramatically impact results. Watch for upcoming research on provenance tracking and differential testing to see if these tools can reach production-grade reliability.
Further reading
Explore more analysis of autonomous development tools in the Artificial Intelligence section.
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