Formal Semantics as the Missing Control Layer for AI-Assisted Software Engineering
Source: Dev.to
Recent Research Overview (2025)
Key Insight:
Across requirements engineering, formal methods, program verification, and AI‑native tooling, a shared pattern emerges: Large language models (LLMs) can significantly assist software construction only when meaning is made explicit, structured, and formally constrained.
Without such constraints, AI‑driven development struggles with reproducibility, explainability, and long‑term maintainability.
This article synthesizes recent peer‑reviewed papers, empirical studies, and formal‑methods research to show how explicit semantics, formal specifications, and deterministic acceptance mechanisms together form the foundation for a more reliable, scalable form of AI‑assisted software engineering. While no single work proposes a complete end‑to‑end paradigm, the individual building blocks are now clearly visible.
Requirements Are Not Documentation — They Are Control Structures
A central shift in the 2025 literature is the reframing of requirements. Rather than being treated as descriptive artifacts, requirements are increasingly understood as structuring and controlling inputs for AI systems.
| Study | Contribution |
|---|---|
| Ferrari & Spoletini – Two‑way roadmap on formal requirements engineering and LLMs | Positions formal requirements as a means to improve controllability, explainability, and verifiability when using LLMs in software development. Argues that formalized requirements provide a structural interface between human intent and machine generation. |
| Caporusso & Perdue – ISCAP 2025 | Empirically demonstrates that a requirements‑first workflow (structured specifications generated before invoking LLMs for code) yields measurably higher quality outputs than direct prompt‑to‑code approaches. |
| Lu et al. – Multi‑agent vision for requirements development and formalization | Proposes a system where requirements are actively refined and formalized before downstream code generation. Highlights “requirements → formalization” as a first‑class quality lever. |
Emerging Pattern: Not AI versus formal methods, but AI embedded within formal control.
Division of Responsibility: AI vs. Formal Mechanisms
Another consistent theme in 2025 research is a clear division of responsibility:
- AI excels at proposing candidate artifacts (contracts, post‑conditions, annotations, documentation).
- Formal mechanisms retain deterministic decision‑making about correctness, validity, or acceptance.
| Study | Role of AI | Role of Formal Methods |
|---|---|---|
| Teuber & Beckert – LLM‑supported Java verification | Generate candidate JML specifications. | Deductive verification acts as an acceptance gate. |
| Zhang, Wang & Zhai – Post‑condition inference | Infer meaningful post‑conditions (even with relatively small models). | Demonstrates feasibility of downstream checking. |
| Recio Abad et al. – Formal JML context for Java documentation | Produce Java documentation. | Formal JML specifications discipline AI outputs, even without full formal proofs. |
Semantic Consistency as a System‑Level Property
Beyond individual artifacts, several 2025 contributions address semantic consistency at the system level.
-
Model‑Based Systems Engineering – Cibrián et al.
Encode semantic constraints at the metamodel level and automatically validate them across complex SysML v2 model landscapes. -
AI‑Native Compiler Discourse – Cogo, Oliva & Hassan
Identify reproducibility and interoperability as core unresolved challenges when translating high‑level intent into executable systems. -
Governance Perspective – Think7 policy work on “Rules as Code” (2025)
Transform intent and regulation into machine‑executable representations to improve transparency, traceability, and consistency.
Limits of Purely Intent‑Driven (Conversational) Approaches
Purely conversational programming paradigms encounter structural limits as systems grow in complexity.
| Study | Findings |
|---|---|
| Bamil – Vibe coding | Highlights challenges related to reproducibility, maintainability, and explainability when code is produced primarily through conversational interaction. |
| Sarkar & Drosos – Empirical work | Developers using conversational programming tools must rely heavily on iterative inspection and manual verification to build trust. |
| Sapkota et al. – Vibe coding vs. agentic approaches | Emphasize that lack of formal acceptance criteria remains a core limitation. |
Consolidated Conclusions
- Explicit, formal semantics is a central enabler for scalable and reliable AI‑assisted software engineering.
- LLMs are well‑suited to propose formal artifacts, while formal validation provides the final grounding.
- Deterministic acceptance mechanisms are technically feasible and already demonstrated in specific domains.
- Semantic consistency can be enforced at the system level, provided that meaning is explicitly modeled.
- No existing work covers the full paradigm end‑to‑end, but the necessary formal components are clearly identifiable.
Take‑away: The emerging picture from 2025 research is not one of automation replacing discipline, but of discipline enabling automation. AI can accelerate software construction — but only when embedded in a framework of explicit semantics, formal specifications, and deterministic validation.
Semantics‑First AI‑Assisted Software Engineering
The shift toward semantic‑first AI‑assisted software engineering emphasizes the need for formal specifications, deterministic acceptance, and contract‑driven development. This perspective aligns with applied efforts such as Secos Rocks, which investigates semantics‑first and contract‑driven approaches to AI‑supported software construction.
The future of AI‑assisted software engineering is therefore not prompt‑driven improvisation, but semantics‑centered construction.
References
- Ferrari, A.; Spoletini, P. (2025). Information and Software Technology, Volume 181, May 2025, 107697.
- Caporusso, N.; Perdue, J. (2025). Proceedings of ISCAP 2025.
- Lu, Y.; et al. (2025). arXiv:2508.18675.
- Teuber, S.; Beckert, B. (2025). arXiv:2502.01573.
- Zhang, G.; Wang, Z.; Zhai, J. (2025). arXiv:2507.10182.
- Recio Abad, J.; et al. (2025). arXiv:2506.09230.
- Cibrián Sánchez, E.; et al. (2025). IEEE Access 2025.3587786.
- Cogo, F. R.; Oliva, G. A.; Hassan, A. E. (2025). arXiv:2510.24799.
- Rapson, J.; et al. (2025). Think7 (G7) Policy Brief.
- Bamil, V. (2025). arXiv:2510.17842.
- Sarkar, A.; Drosos, I. (2025). arXiv:2506.23253.
- Sapkota, S.; et al. (2025). arXiv:2505.19443.