Hybrid intelligence effort for software effort estimation in LLM assisted development

Abstract

Software effort estimation remains a cornerstone of project planning and control, yet existing estimation models are grounded in the assumption that software development effort is dominated by human reasoning and manual construction. The rapid integration of large language models (LLMs) into development workflows fundamentally challenges this assumption by automating substantial portions of code synthesis while shifting human effort toward supervision, validation, and integration. As a result, traditional effort estimation proxies such as Story Points and size-based metrics may no longer reliably characterize development effort. This paper presents an empirical study examining how effort manifests in LLM-assisted software development. Rather than using LLMs as predictive estimation tools, we investigate how their adoption reshapes the underlying cost structure of development work. We introduce the notion of Hybrid Intelligence Effort (HIE), conceptualizing effort as the combined burden of model-performed reasoning and human oversight activities. Using a controlled experiment involving 22 developers, 110 real-world tasks, and three LLMs, we compare the explanatory power of traditional estimation metrics against interaction- and oversight-based Hybrid Intelligence dimensions. Our results show that while Story Points retain partial explanatory validity, they fail to capture dominant sources of effort in LLM-assisted workflows. In controlled experiments, HIE dimensions increase explained variance in observed effort from approximately 72–80%, while substantially reducing systematic estimation error. Human validation and corrective intervention emerge as the primary drivers of effort, outweighing artifact-level characteristics. These findings suggest that effort estimation models must move beyond human-centric and size-based assumptions to remain effective in AI-augmented software engineering.