AgentModernize: Preserving Business Logic in Legacy Modernization with Multi-Agent LLMs and Behavioral Specification Graphs
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911692153159680 |
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| author | Ahmed, Sheikh Nazib Galib, Marnim |
| author_facet | Ahmed, Sheikh Nazib Galib, Marnim |
| contents | Legacy modernization breaks business logic. Most tools and LLM-based approaches treat modernization as syntax translation, losing implicit rules, edge-case handling, and cross-module constraints. We present AgentModernize, a multi-agent framework that treats modernization as a behavioral preservation problem. Four specialized agents handle extraction, specification, code generation, and validation. The key intermediate artifact -- a Behavioral Specification Graph (BSG) -- forces extracted business logic to be explicit and inspectable before any code is generated. We evaluated on LegacyModernize-8, eight scenarios spanning telecom and banking, using three models (GPT-4o-mini, GPT-4o, GPT-5.3-codex) under a fair protocol: same gold-standard tests, 3 trials, temperature 0.0. Full AgentModernize with feedback was the only configuration with non-zero mean BER under every backbone. SP-LLM and CoT-LLM scored 0.0% on every scenario, on every backbone. AgentModernize without feedback scored 0.0% mean BER with GPT-4o-mini and GPT-5.3-codex; under GPT-4o it achieved non-zero BER only on S1 (44.4%; 5.6% mean over scenarios). Mean BER for full AgentModernize was 9.4% (mini), 8.1% (GPT-4o), and 19.4% (codex). The BSG captures 91.2% of gold-standard rules, confirming that the bottleneck is code generation, not extraction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_17535 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AgentModernize: Preserving Business Logic in Legacy Modernization with Multi-Agent LLMs and Behavioral Specification Graphs Ahmed, Sheikh Nazib Galib, Marnim Software Engineering D.2.7; D.2.5; I.2.2 Legacy modernization breaks business logic. Most tools and LLM-based approaches treat modernization as syntax translation, losing implicit rules, edge-case handling, and cross-module constraints. We present AgentModernize, a multi-agent framework that treats modernization as a behavioral preservation problem. Four specialized agents handle extraction, specification, code generation, and validation. The key intermediate artifact -- a Behavioral Specification Graph (BSG) -- forces extracted business logic to be explicit and inspectable before any code is generated. We evaluated on LegacyModernize-8, eight scenarios spanning telecom and banking, using three models (GPT-4o-mini, GPT-4o, GPT-5.3-codex) under a fair protocol: same gold-standard tests, 3 trials, temperature 0.0. Full AgentModernize with feedback was the only configuration with non-zero mean BER under every backbone. SP-LLM and CoT-LLM scored 0.0% on every scenario, on every backbone. AgentModernize without feedback scored 0.0% mean BER with GPT-4o-mini and GPT-5.3-codex; under GPT-4o it achieved non-zero BER only on S1 (44.4%; 5.6% mean over scenarios). Mean BER for full AgentModernize was 9.4% (mini), 8.1% (GPT-4o), and 19.4% (codex). The BSG captures 91.2% of gold-standard rules, confirming that the bottleneck is code generation, not extraction. |
| title | AgentModernize: Preserving Business Logic in Legacy Modernization with Multi-Agent LLMs and Behavioral Specification Graphs |
| topic | Software Engineering D.2.7; D.2.5; I.2.2 |
| url | https://arxiv.org/abs/2605.17535 |