Beyond Isolated Tasks: A Framework for Evaluating Coding Agents on Sequential Software Evolution
📰 ArXiv cs.AI
A framework for evaluating coding agents on sequential software evolution tasks beyond isolated tasks
Action Steps
- Generate sequential software evolution tasks using the automated coding task generation framework
- Evaluate coding agents on long-horizon tasks that capture the accumulation of code changes and technical debt over time
- Analyze the performance of coding agents on tasks with growing test suites and evolving software requirements
- Use the SWE-STEPS dataset to fine-tune and improve the coding agents' capabilities
Who Needs to Know This
Software engineers and AI researchers on a team benefit from this framework as it helps evaluate coding agents in real-world software development scenarios, allowing them to improve the agents' performance and adaptability
Key Insight
💡 Evaluating coding agents on isolated tasks is not enough; they need to be tested on long-horizon tasks that mimic real-world software development
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🚀 Evaluate coding agents on sequential software evolution tasks with SWE-STEPS dataset 💻
Key Takeaways
A framework for evaluating coding agents on sequential software evolution tasks beyond isolated tasks
Full Article
Title: Beyond Isolated Tasks: A Framework for Evaluating Coding Agents on Sequential Software Evolution
Abstract:
arXiv:2604.03035v1 Announce Type: cross Abstract: Existing datasets for coding agents evaluate performance on isolated, single pull request (PR) tasks in a stateless manner, failing to capture the reality of real-world software development where code changes accumulate, technical debt accrues, and test suites grow over time. To bridge this gap, we introduce an automated coding task generation framework, which helps generate our dataset SWE-STEPS, that evaluates coding agents on long-horizon task
Abstract:
arXiv:2604.03035v1 Announce Type: cross Abstract: Existing datasets for coding agents evaluate performance on isolated, single pull request (PR) tasks in a stateless manner, failing to capture the reality of real-world software development where code changes accumulate, technical debt accrues, and test suites grow over time. To bridge this gap, we introduce an automated coding task generation framework, which helps generate our dataset SWE-STEPS, that evaluates coding agents on long-horizon task
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