# Lifecycle-Aware code generation: Leveraging Software Engineering Phases in LLMs

arXiv: https://arxiv.org/abs/2510.24019 · HTML: https://mlsanity.com/paper/2510.24019/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 57 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## Builds on (most cited first, 30 of 33)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [Measuring Coding Challenge Competence With APPS](https://mlsanity.com/paper/2105.09938/) (2021)
- [CodeBLEU: a Method for Automatic Evaluation of Code Synthesis](https://mlsanity.com/paper/2009.10297/) (2020)
- [RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation](https://mlsanity.com/paper/2303.12570/) (2023)
- [Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code](https://mlsanity.com/paper/2404.00971/) (2024)
- [No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation](https://mlsanity.com/paper/2305.04207/) (2023)
- [CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generation](https://mlsanity.com/paper/2501.07811/) (2025)
- [Uncertainty-Guided Chain-of-Thought for Code Generation with LLMs](https://mlsanity.com/paper/2503.15341/) (2025)
- [Agents4PLC: Automating Closed-loop PLC Code Generation and Verification in Industrial Control Systems using LLM-based Agents](https://mlsanity.com/paper/2410.14209/) (2024)
- [AutoP2C: An LLM-Based Agent Framework for Code Repository Generation from Multimodal Content in Academic Papers](https://mlsanity.com/paper/2504.20115/) (2025)
- [ChatCoder: Chat-based Refine Requirement Improves LLMs' Code Generation](https://mlsanity.com/paper/2311.00272/) (2023)
- [Testing LLMs on Code Generation with Varying Levels of Prompt Specificity](https://mlsanity.com/paper/2311.07599/) (2023)
- [Applying RLAIF for Code Generation with API-usage in Lightweight LLMs](https://mlsanity.com/paper/2406.20060/) (2024)
- [Enhancing LLM Code Generation with Ensembles: A Similarity-Based Selection Approach](https://mlsanity.com/paper/2503.15838/) (2025)
- [Enhancing LLM Code Generation: A Systematic Evaluation of Multi-Agent Collaboration and Runtime Debugging for Improved Accuracy, Reliability, and Latency](https://mlsanity.com/paper/2505.02133/) (2025)
- [Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks](https://mlsanity.com/paper/2501.06625/) (2025)
- [Studying How Configurations Impact Code Generation in LLMs: the Case of ChatGPT](https://mlsanity.com/paper/2502.17450/) (2025)
- [Combining LLM Code Generation with Formal Specifications and Reactive Program Synthesis](https://mlsanity.com/paper/2410.19736/) (2024)
- [Chain-of-Descriptions: Improving Code LLMs for VHDL Code Generation and Summarization](https://mlsanity.com/paper/2507.12308/) (2025)
- [Code Copycat Conundrum: Demystifying Repetition in LLM-based Code Generation](https://mlsanity.com/paper/2504.12608/) (2025)
- [SysVCoder: An LLM-Driven Framework for Systematic Generation of System-Level Design](https://mlsanity.com/paper/2504.20653/) (2025)
- [An evaluation of LLM code generation capabilities through graded exercises](https://mlsanity.com/paper/2410.16292/) (2024)
- [Leveraging Metamemory Agent for Enhanced Data-Free Code Generation in Large Language Models](https://mlsanity.com/paper/2501.07892/) (2025)

## Abstract (arXiv)

Recent progress in large language models (LLMs) has advanced automatic code generation, yet most approaches rely on direct, single-step translation from problem descriptions to code, disregarding structured software engineering practices. We introduce a lifecycle-aware framework that systematically incorporates intermediate artifacts such as requirements analysis, state machine modeling, and pseudocode into both the training and inference stages. This design aligns code generation with standard software development phases and enables more structured reasoning. Experiments show that lifecycle-level fine-tuning improves code correctness by up to 75% over the same model before fine-tuning, with performance gains compounding across intermediate stages. Multi-step inference consistently surpasses single-step generation, demonstrating the effectiveness of intermediate scaffolding. Notably, open-source LLMs, once fine-tuned under our framework, match or slightly outperform models pretrained on code. When applied to DeepSeek-Coder-1.3B, our framework yields relative CodeBLEU improvements of 34.3%, 20.0%, 11.2%, and 22.3% over ChatGPT-3.5, ChatGPT-4o-mini, DeepSeek-R1, and LLaMA-8B, respectively. Our pipeline also proves robust with up to 80% less training data, confirming its resilience. Ablation studies further reveal that each intermediate artifact contributes distinctly to final code quality, with state machine modeling yielding the most substantial impact. Our source code and detailed experimental data are available at https://anonymous.4open.science/r/Lifecycle-Aware-3CCB.