Intent classification for automated software deployment plan generation: a transformer-based approach

Abstract

Manual interpretation of software deployment requests written in natural language remains a time-consuming and error-prone process in DevOps pipelines. Despite the growth of Infrastructure-as-Code (IaC) tools, translating ambiguous or incomplete user intents into executable deployment plans typically demands experienced human intervention. In this paper, we present a transformer-based intent classification framework using DistilBERT fine-tuned with Low-Rank Adaptation (LoRA) for lightweight, accurate interpretation of deployment-related queries. We introduce DeployIntentD, a custom dataset of 3,558 deployment-related queries, composed of real-world CI/CD logs and synthetic examples generated through Retrieval-Augmented Generation (RAG). Our model achieves 88.6% accuracy, 0.83 macro-F1 score, and sub-25ms inference latency on standard GPU hardware. The proposed system represents a step forward in bridging natural language understanding with DevOps automation.