Technology Category
Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: (Large) Language ModelsPlanning, Routing, and Scheduling: Planning with Language Models
Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead
🤖 AI Summary
There is a lack of systematic, software engineering (SE)-driven analysis of the full lifecycle challenges associated with large language models (LLMs). Method: We conduct a structured systematic literature review grounded in the SE lifecycle model, decomposing LLM development into six phases—requirements, data, development, testing, deployment, and maintenance—and perform stage-wise problem identification. Contribution/Results: We uncover phase-specific SE challenges—including prompt engineering maintainability, insufficient test coverage under data drift, and misalignment between model versioning and code evolution—and propose the first SE-oriented research framework for LLMs. This framework delineates stage-specific research directions and integrated technical pathways. Our work bridges a critical theoretical gap at the intersection of LLMs and SE, delivering an actionable research roadmap and practical guidance for building efficient, reliable, and evolvable LLM-based software systems.
Technology Category
Application Category
📝 Abstract
The rapid advancement of large language models (LLMs) has redefined artificial intelligence (AI), pushing the boundaries of AI research and enabling unbounded possibilities for both academia and the industry. However, LLM development faces increasingly complex challenges throughout its lifecycle, yet no existing research systematically explores these challenges and solutions from the perspective of software engineering (SE) approaches. To fill the gap, we systematically analyze research status throughout the LLM development lifecycle, divided into six phases: requirements engineering, dataset construction, model development and enhancement, testing and evaluation, deployment and operations, and maintenance and evolution. We then conclude by identifying the key challenges for each phase and presenting potential research directions to address these challenges. In general, we provide valuable insights from an SE perspective to facilitate future advances in LLM development.
Problem
Research questions and friction points this paper is trying to address.
Systematically analyze LLM development lifecycle challenges
Identify key challenges in six SE-based LLM phases
Propose research directions for LLM development solutions
Innovation
Methods, ideas, or system contributions that make the work stand out.
Systematic analysis of LLM development lifecycle
Identify key challenges in six phases
Propose SE solutions for LLM advancement
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