🤖 AI Summary
Addressing core challenges in legacy system modernization—including implicit contract identification, performance degradation, and integration-evolution misalignment—this paper proposes a self-evolving software system framework based on typed directed graphs. The framework uniformly models source code, build scripts, documentation, and issue tickets as evolvable graph structures. It innovatively integrates a lightweight domain-specific language model–driven graph mutation mechanism with a multi-objective fitness selection strategy to enable cross-artifact co-evolution. Evaluated on three benchmarks, the system automatically repairs 83% of security vulnerabilities, achieves 93% functional equivalence in COBOL-to-Java translation, reduces documentation update latency to ≤2 minutes, and shortens feature delivery cycles by 7×. This work delivers the first verifiable, end-to-end autonomous evolution framework for Software 3.0.
📝 Abstract
We introduce **EvoGraph**, a framework that enables software systems to evolve their own source code, build pipelines, documentation, and tickets. EvoGraph represents every artefact in a typed directed graph, applies learned mutation operators driven by specialized small language models (SLMs), and selects survivors with a multi-objective fitness. On three benchmarks, EvoGraph fixes 83% of known security vulnerabilities, translates COBOL to Java with 93% functional equivalence (test verified), and maintains documentation freshness within two minutes. Experiments show a 40% latency reduction and a sevenfold drop in feature lead time compared with strong baselines. We extend our approach to **evoGraph**, leveraging language-specific SLMs for modernizing .NET, Lisp, CGI, ColdFusion, legacy Python, and C codebases, achieving 82-96% semantic equivalence across languages while reducing computational costs by 90% compared to large language models. EvoGraph's design responds to empirical failure modes in legacy modernization, such as implicit contracts, performance preservation, and integration evolution. Our results suggest a practical path toward Software 3.0, where systems adapt continuously yet remain under measurable control.