MirrorCode is a benchmark designed to evaluate AI models on long-horizon coding tasks by requiring them to reimplement entire programs end-to-end without access to the original source code. Solutions generated by AI must match the output of the original program to be considered successful.

A study investigates the impact of code cleanliness on the performance of autonomous coding agents. The research focuses on how the structural and stylistic quality of code influences an agent's ability to navigate and complete coding tasks.

LLM-based Multi-Agent (LLM-MA) systems automate complex software engineering tasks, including requirements engineering, code generation, and testing. Understanding their operational efficiency and resource consumption is crucial for practical adoption.

DeepSWE is a long-horizon software engineering benchmark designed to evaluate coding agents on original engineering tasks. It features contamination-free tasks, high diversity across 91 repositories in five programming languages, and real-world applicability.

Cursor AI enhances short-term development speed in open-source projects by leveraging large language models (LLMs). However, this acceleration may lead to increased long-term complexity in software maintenance and quality.

Approximately 50% of test-passing SWE-bench Verified pull requests created by AI agents between mid-2024 and late-2025 would not be merged into the main branch by repository maintainers. The findings suggest that the lack of iterative feedback for AI agents does not indicate a fundamental capability limitation.

Large language model-powered agents can automate software engineering tasks, including static bug fixing, as shown by benchmarks like SWE-bench. Real-world software development requires navigating complex requirements beyond these capabilities.