arXiv:2606.18976v1 Announce Type: cross Abstract: Automated assessment in software engineering education has advanced significantly for code grading and essay scoring. However, reviewing software architecture deliverables, which requires analyzing structural completeness and requirements traceability, has not yet been fully automated. Applying Large Language Models (LLMs) to this task requires robust architectures to ensure technical feedback is accurate and reliable for students. This paper presents CAPRA (Configurable Architecture Proficiency Report Assessment), a multi-agent LLM system that
Advances in LLM capabilities and multi-agent system design are enabling more complex automation, and the need for scalable, consistent feedback in technical fields like software engineering is growing.
This development indicates a significant step towards automating sophisticated white-collar tasks, specifically complex assessment and feedback, which has broad implications for productivity and education.
The ability to accurately and reliably automate the review of complex deliverables like software architecture means higher quality feedback at lower cost, potentially accelerating learning and development cycles.
- · Software engineering education
- · AI agent developers
- · Organizations requiring scalable code review
- · Human technical reviewers (repetitive tasks)
- · Traditional manual assessment methods
Automated technical feedback becomes more widespread, improving efficiency in education and industry.
The demand for human experts shifts from basic review to designing and overseeing advanced AI assessment systems.
This could lead to a 'race to the bottom' in terms of human review rates, or conversely, free up human capacity for more creative work and complex problem-solving.
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Read at arXiv cs.AI