Enhancing SWE Bench with Context Engineering: A Comparative Study Against Prompt Engineering in LLM-Based Software Tasks

Abstract

This article explores context engineering as a different form of conventional prompt engineering to enhance the performance of large language models on software engineering problems, in particular, the SWE Bench framework. Although the idea of prompt engineering has gained wide acceptance, it has fundamental weaknesses in dealing with complex software development scenarios that demand knowledge in more than one dimension. Context engineering is an approach that solves these shortcomings by providing an ordered enhancement of model input, with pertinent history, architecture, and domain-specific data, based on code repositories. The article employs an elaborate approach that compares baseline, prompt-engineered, and context-engineered strategies on various software activities. Results find that context engineering performs much better than prompt engineering in complex situations that require multiple files or system components and provides a real improvement in such solution quality dimensions as maintainability and project alignment. In addition to the performance improvements, the method has a significant environmental, economic, and social advantage as it allows more efficient resource use, guarantees a higher productivity of the developers, and democratizes access to contextual knowledge. The article forms an empirical basis for next-generation software development tools that attain context-sensitive techniques to develop more intelligent and useful language model applications in software engineering.