天工不止造物，也能修bug：Skywork-SWE给代码智能体补上软件工程课

Core Viewpoint - The article discusses the emergence of Skywork-SWE, an autonomous code intelligence model developed by Kunlun Wanwei, aimed at addressing the complexities of software engineering and bug fixing in modern code systems, drawing parallels to the craftsmanship spirit of ancient Chinese artisans [2][7][40]. Group 1: Background and Challenges - The need for Skywork-SWE arises from the increasing complexity of software systems, which are integral to modern civilization, yet prone to bugs due to various factors such as logical errors and environmental changes [3][4]. - Bug fixing is identified as a fundamental yet complex task in software engineering, often requiring deep understanding and multi-round reasoning, similar to human developers [4][6]. Group 2: Development of Skywork-SWE - Kunlun Wanwei has developed Skywork-SWE as a high-performance model with 32 billion parameters, representing a complete system that integrates data collection, validation, reasoning, and bug fixing [7][18]. - The model was trained on a large-scale, verifiable software engineering dataset, which was constructed through a structured and automated process involving three main phases and nine steps [12][18]. Group 3: Dataset Characteristics - The dataset for Skywork-SWE includes 10,169 real code issues and 8,209 multi-round interaction trajectories, making it one of the largest and highest quality software engineering datasets available [18][20]. - Compared to existing datasets, Skywork-SWE features significantly higher task complexity, with an average of over 2 function modifications and 74 lines of code changes per patch, reflecting real-world software development challenges [20][21]. Group 4: Performance and Scaling Law - Skywork-SWE-32B achieved a 47% accuracy rate on the SWE-bench Verified benchmark, outperforming other models with fewer parameters and even some larger models [25][33]. - The experiments revealed a scaling law in LLM software engineering capabilities, indicating that performance improves with the expansion of training data, with no signs of saturation in the current dataset scale [27][29]. Group 5: Future Implications - The success of Skywork-SWE signifies a shift towards high-quality, task-oriented data as a foundation for training intelligent agents in software engineering, potentially setting a new standard in the industry [40][42]. - Kunlun Wanwei plans to expand the Skywork-SWE dataset to include more programming languages and enhance its capabilities through online reinforcement learning methods [41][42].