Results show that behavior interpretation and project-aware context retrieval both contribute substantially to generation quality, and demonstrate that LLMs can effectively connect natural-language behavior specifications with project code and support specification-driven software development.
Abstract
Behavior-Driven Development (BDD) helps technical and non-technical stakeholders share a common understanding of software requirements through natural-language scenarios. Glue code makes these scenarios executable by mapping each step to the corresponding project code. However, developing and maintaining glue code requires knowledge of both the intended behavior and the underlying codebase, making it a labor-intensive part of BDD as requirements evolve. Although large language models (LLMs) have shown strong code generation capabilities, their use for automated glue code generation remains unexplored. This task requires reasoning over underspecified behavior, related BDD artifacts, and large project codebases. We present AutoGlue, a hierarchical multi-agent framework for automated Java glue code generation. AutoGlue follows a behavior-first workflow that separates behavior interpretation, context retrieval, and code generation. A Behavior Interpreter derives the intent of a step from its scenario context, while a Developer agent retrieves relevant BDD artifacts and project code before generating the final glue code. We evaluate AutoGlue on 1,307 steps from eight open-source Java projects. Compared with few-shot prompting, AutoGlue improves API F1 by 58.7% and CodeBLEU by 43.7%. It produces directly usable glue code for 46.1% of the evaluated steps, while most partially correct outputs require only minor revisions, such as adding missing actions or refining parameters. Ablation results show that behavior interpretation and project-aware context retrieval both contribute substantially to generation quality. These findings demonstrate that LLMs can effectively connect natural-language behavior specifications with project code and support specification-driven software development.
This work proposes TraceDev, a multi-agent framework for automated software development grounded in use cases that contain multiple functional points and complex semantics, and demonstrates the effectiveness of TraceDev in repository-level code generation from requirements.
Repo0 is presented, a continuous structural evolution framework for zero-to-all code generation that maintains an explicit architectural state instantiated as a Dual-Directed-Acyclic-Graph (Dual-DAG), consisting of a requirement-level DAG, a component-level DAG, and their alignment relation.
Si-Lin Chen, Haoyi Teng, Xiao-Dong Gu et al.· 0 citations
Code generation aims to automatically generate source code from task requirements and has attracted significant attention with the rapid advancement of large language models (LLMs). Despite remarkable progress, LLMs often struggle to generate correct code for complex software engineering tasks because task descriptions are frequently incomplete, ambiguous, or lack critical contextual information. Existing approaches primarily improve the capabilities of coding agents through more sophisticated tools, skills, and workflows, while largely overlooking the quality of the task requirements themselves. To address this limitation, we draw inspiration from software requirements engineering and propose WiseSpec, a novel requirements-driven agent framework for repository-level code generation. WiseSpec automatically constructs structured and information-rich requirements, assesses their quality through execution-based evaluation, and iteratively refines them to better guide code generation. Experimental results show that WiseSpec consistently outperforms all baselines, achieving an average improvement of 13.17% in %Resolved.
: Model-Driven Software Engineering has long excelled at generating code from static structural models, yet the specification and generation of dynamic behavioral models remains a persistent challenge. Meanwhile, Large Language Models (LLMs) offer flexible, natural-language based code generation but suffer from non-determinism and hallucinations. This paper presents a semi-formal approach that bridges these two paradigms for behavioral modeling via UML state machines. We contribute a textual modeling language that captures the essential elements of UML state diagrams—states, transitions, events, guards, and entry/exit actions—alongside a deterministic code generator that transforms state machine models into Java code following the Gang of Four State design pattern. The language supports two complementary action annotation styles: direct code fragments for concise, self-contained actions, and natural language descriptions for semantically richer behavior to be completed by an LLM weaver. LLM involvement is deliberately scoped to small, well-constrained action bodies, reducing token consumption and non-determinism compared to fully LLM-based approaches. Validated through the Gumball Machine case study, correctness is confirmed by automated tests covering state and transition coverage criteria, and repeating the LLM weaving step produced consistent results across all runs. Compared to both classical UML tooling and fully LLM-based generation, the approach offers stronger determinism, better traceability, lower cognitive modeling effort, and reduced computational cost, while retaining the flexibility to express complex action behavior in natural language where formal specification would be unnecessarily burdensome.
Oliver Engling, Felix Schwägerl, Thomas Buchmann· International Conference on...· 0 citations
An empirical study of whether complete software artifacts generated by LLM coding agents can be executed in a clean environment using only the code, dependency specifications, and instructions the agent provides suggests that coding-agent evaluation should treat clean-environment executability as a first-class metric alongside functional correctness.
CCGMAS enables more explicit semantic alignment across platforms by introducing requirement documents as an intermediate semantic layer and incorporating platform residue modeling, and a feedback-driven refinement loop is designed to iteratively correct errors at different stages, improving both functional correctness and platform compatibility.
Xiao Zhang, Bo Yang· IEEE Access· 0 citations
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