CodeClash: Benchmarking Goal-Oriented Software Engineering

2025-11-02

AI Summary

Existing code-generation benchmarks primarily target well-defined tasks (e.g., bug fixing), failing to assess models’ ability to iteratively optimize code over multiple rounds—without explicit instructions—toward high-level objectives such as improving user retention or resource efficiency. To address this gap, we introduce Multi-Round Code Arena, the first benchmark for goal-directed software engineering. It employs an adversarial tournament mechanism integrating automated execution feedback, multi-stage editing decisions, and dynamic performance comparison to evaluate strategic planning and long-term code maintenance capabilities. Our evaluation comprises 1,680 tournaments (25,200 rounds) across six arena configurations, testing eight large language models. Results reveal pervasive weaknesses in strategic reasoning and severe code redundancy; all models significantly underperform human experts across all metrics.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Current benchmarks for coding evaluate language models (LMs) on concrete, well-specified tasks such as fixing specific bugs or writing targeted tests. However, human programmers do not spend all day incessantly addressing isolated tasks. Instead, real-world software development is grounded in the pursuit of high-level goals, like improving user retention or reducing costs. Evaluating whether LMs can also iteratively develop code to better accomplish open-ended objectives without any explicit guidance remains an open challenge. To address this, we introduce CodeClash, a benchmark where LMs compete in multi-round tournaments to build the best codebase for achieving a competitive objective. Each round proceeds in two phases: agents edit their code, then their codebases compete head-to-head in a code arena that determines winners based on objectives like score maximization, resource acquisition, or survival. Whether it's writing notes, scrutinizing documentation, analyzing competition logs, or creating test suites, models must decide for themselves how to improve their codebases both absolutely and against their opponents. We run 1680 tournaments (25,200 rounds total) to evaluate 8 LMs across 6 arenas. Our results reveal that while models exhibit diverse development styles, they share fundamental limitations in strategic reasoning. Models also struggle with long-term codebase maintenance, as repositories become progressively messy and redundant. These limitations are stark: top models lose every round against expert human programmers. We open-source CodeClash to advance the study of autonomous, goal-oriented code development.

Problem

• Evaluating LMs on open-ended goal-oriented software development
• Benchmarking strategic reasoning in competitive code improvement
• Assessing long-term codebase maintenance in autonomous programming

Innovation

• Multi-round tournament benchmark for goal-oriented coding
• Code arena evaluates competitive objectives like score maximization
• Autonomous agents iteratively improve codebases without explicit guidance