Software engineering has undergone this large transformation to automate tasks, particularly through large language models. This may concern generating code or tests checking for bugs, an activity traditionally done by human engineers. Now, AI-driven agents based on LLMs would understand and produce human-like text, carrying out complex operations in software development. However, the full potential of such AI agents was never harnessed because their capabilities were usually narrowed down to just one task, giving a fragmented solution to software engineering challenges.
The challenge in software engineering is debugging an issue in a large codebase, such as the ones on GitHub. Codebases are huge and very complex, which makes it very difficult to understand how the software was designed and how it is functioning. SWE agents were developed to address these issues automatically by automatically generating bug patches. The task is cumbersome because of the need to navigate large code repos and complex interactions between functions. In the end, it gives an accurate fix. Up-to-date, each artificial intelligence for each agent has yet to show mastery of every aspect of these tasks, often yielding suboptimal sometimes and inconsistent results.
Several researchers have developed several AI-based agents that bestow special emphasis on different aspects of software issue resolution. Some are very good at reproducing bugs in a development environment to understand the problem better, while others specialize in patch generation or code review. The problem is that these agents usually operate in isolation and offer limited success. Without a framework for collaboration, it would then enforce fully diverse strengths from these agents, leading to bottlenecks and missed opportunities for problem-solving efficiency.
Researchers from the Salesforce AI Research team and Carnegie Mellon University proposed the Diversity Empowered Intelligence (DEI) framework. DEI is a framework designed to encompass multiple software engineering agents leveraging unique strengths toward a more unified, powerful, cohesive problem-solving entity. This framework functions as a meta-module applicable to existing SWE agents, allowing them to cooperate in a coordinated manner. In guiding and managing these collaborative efforts, DEI greatly improves the ability of all agents to solve complex software problems compared to any agent possibly doing it by itself.
The DEI framework works based on the evaluation that the various agents of software engineering implement in the solutions they provide, and based on that, they choose the solution’s effectiveness in the provided context and for the prevailing problem. This has been made possible through the re-ranking pipeline implemented so that the selection and application of any patch are done to the best of the possibilities. DEI is a scheme that particularly works because its diverse expertise from different agents enables them to solve an area of wider problems with a much higher level of accuracy. First and foremost, the framework is scalable in a way that has been carefully designed to integrate with any existing SWE agent framework, consequently fostering a more collaborative and efficient software engineering environment.
DEI framework performance has been exhaustively tested with a benchmark specially designed within SWE-Bench Lite to evaluate the capability of software engineering agents in finding a solution to real-world GitHub issues. From the results of the performance obtained in these tests, the performance is simply astonishing. With a 27.3% resolution rate maximized across SWE-Bench Lite benchmarks, the best individual agent performance is increased by 25%, with the DEI-guided committee of 34.3% to solve. The best-performing group working on DEI achieved a resolution rate of 55%, the highest value recognized by SWE-Bench Lite. This performance, indeed, surpasses what can be done by single agents and many closed-source systems, exhibiting great potential with these collaborative AI systems.
In conclusion, the Diversity Empowered Intelligence (DEI) framework integrates the diverse capabilities of multiple SWE agents and effectively addresses the challenges of resolving complex software issues in large codebases. The framework’s ability to enhance performance through collaboration and re-ranking has been proven through extensive testing, where it achieved notable results, including a 34.3% resolve rate and a 55% peak performance on SWE-Bench Lite. These findings underscore the importance of diversity in AI systems, as it leads to greater innovation, efficiency, and problem-solving capabilities in software engineering.
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