Md Asif Iqbal Fahim | Searcharxiv

arXiv2026

ReqEvolve: User-Oriented Software Self-Evolution through Automatic Requirement Interpretation

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.

cs.SE↗

arXiv2026

Software Self-Extension with SelfEvolve: an Agentic Architecture for Runtime Code Generation

Traditional self-adaptive systems automatically reconfigure existing components in response to changing requirements, but provide limited support for the generation of novel functionalities. The software generation capabilities of large language models (LLMs) open the possibility to create entirely new modules at runtime, enabling a form of self-evolution beyond traditional self-adaptation. We present SelfEvolve, an orchestrated agentic pipeline architecture enabling runtime self-extension--the autonomous addition of new capabilities during execution--as a preliminary form of self-evolution. Self-extension focuses on the autonomous generation and integration of new functions, based on user requests, without requiring a system restart or developer intervention. Evaluation of our architecture across 11 self-extension tasks demonstrates an average Pass@1 of 92.7% (51/55), outperforming developer-focused code generation baselines like AutoGen, MetaGPT, and AgentCoder. SelfEvolve achieves 61.8% improvement over the best baseline, i.e. Autogen, with statistical significance. This work demonstrates the feasibility of runtime capability extension through autonomous code generation. This provides preliminary evidence for a paradigm in which systems autonomously evolve to satisfy user needs, paving the way towards individualised, self-improving systems.

cs.SE↗