ReqEvolve：通过自动需求解释实现面向用户的软件自我进化
ReqEvolve: User-Oriented Software Self-Evolution through Automatic Requirement Interpretation
- University College Dublin（都柏林大学学院）
- Consiglio Nazionale delle Ricerche (CNR)（意大利国家研究委员会）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出ReqEvolve系统，通过自动需求工程和测试驱动开发，将用户高层请求转化为可执行功能，实现用户驱动的软件自我进化，在72个案例中Pass@1达89.2%，显著优于基线。
AI中文摘要：
软件自我进化的范式使系统能够在执行过程中根据技术规范自主扩展和重新配置自身能力。然而，新功能的需求往往来自最终用户，且很少以技术术语表达。因此，开发人员必须在系统进化之前将用户需求转化为技术规范，这延迟了用户对请求功能的早期验证，因为用户无法立即观察到由此产生的行为。为了解决这一差距，我们提出了ReqEvolve，一个运行时代码生成系统，通过接受高层用户请求来实现用户驱动的自我进化。该系统集成了自动需求工程（RE）和测试驱动开发（TDD），通过澄清、规范分解、测试生成和运行时集成将这些请求转化为可执行功能。我们在18个项目的72个软件进化案例上评估了ReqEvolve，并与两个基线进行比较：SpecFix（一种以RE为中心的代码生成方法）和我们的系统的消融变体。ReqEvolve达到了89.2%的Pass@1，比SpecFix高出18.8%（p < 0.01，r = 0.79，大效应），比消融基线高出32.6%（p < 0.001，r = 0.88，大效应）。这些结果初步证明，用户驱动的自我进化是一种可行的范式，可以从用户请求中自主扩展软件能力，从而在开发人员验证之前加速需求验证。
英文摘要：
The paradigm of software self-evolution enables systems to autonomously extend and reconfigure their own capabilities during execution in response to technical specifications. Yet requests for new functionality often originate from end users and are rarely expressed in technical terms. As a result, developers must translate user needs into technical specifications before the system can evolve, delaying early validation of the requested functionality by preventing users from immediately observing the resulting behaviour. To address this gap, we present ReqEvolve, a runtime code generation system that enables user-driven self-evolution by accepting high-level user requests. The system integrates automatic requirements engineering (RE) and test-driven development (TDD) to transform these requests into executable functionality through clarification, specification decomposition, test generation, and runtime integration. We evaluate ReqEvolve on 72 software evolution cases across 18 projects against two baselines: SpecFix, an RE-focused code generation approach, and an ablation variant of our system. ReqEvolve achieves 89.2% Pass@1, outperforming SpecFix by 18.8% (p < 0.01, r = 0.79, large effect) and the ablation baseline by 32.6% (p < 0.001, r = 0.88, large effect). These results provide initial evidence that user-driven self-evolution is a viable paradigm for autonomously extending software capabilities from user requests, thereby accelerating requirements validation prior to developer verification.
