A Mixed-Method Empirical Study of LLM Assistance in Software Engineering Workflows

Abstract

Large Language Models (LLMs) are increasingly integrated into software development workflows, yet their effects are often discussed without distinguishing between task types, developer seniority, and verification demands. This paper presents a mixed-method empirical study of LLM-assisted software engineering with first-year and fourth-year undergraduates. Phase 1 is a preliminary survey (n=157) that characterizes LLM exposure, reliance, and trust calibration among the two groups. Phase 2 is a task-based quasi-experiment with a purposive sample from both cohorts (n=20). Here, we compare AI-assisted and non-AI conditions on a structured set of software engineering tasks spanning implementation, constraint-driven algorithm selection, and architectural reasoning. We then analyze performance outcomes alongside behavioral traces captured via screen recording and a qualitative coding process. Survey results indicate widespread LLM adoption and substantial verification effort, alongside cohort differences in perceived LLM capability for constraint-heavy scenarios. The quasi-experiment further shows that AI assistance changes workflow structure. For example, participants frequently adopt AI-first task entry, copy-transfer integration, and AI-mediated debugging, whereas non-AI workflows rely more on documentation, prior templates, and iterative trial-error refinement. Overall, our findings suggest that the benefits of LLM assistance are task-dependent and mediated by expertise and verification practices, rather than by generation speed alone.