Large Language Models in Software Project Management: A Two-phase Systematic Literature Review

Background: Large Language Models (LLMs) are increasingly integrated into Software Engineering (SE) processes and practices. However, their application within Software Project Management (SPM) remains fragmented and insufficiently synthesized. This absence of systematic evidence prevents both researchers and practitioners from understanding where LLMs can meaningfully contribute to project management processes in relation to established standards such as the Project Management Body of Knowledge (PMBOK).

Objectives: This study aims to systematically synthesize existing evidence on the application and integration of LLMs in SPM by means of a two-phase method where Phase I systematically searches for existing secondary studies in the topic of interest, and Phase II (whenever applicable) carries out an SLR in the topic of interest.

Method: We conducted a two-phase method: Phase I is a tertiary study that used 14 research questions to assess the existence, coverage, and quality of prior secondary studies in LLMs applied to SPM. As no suitable SLR was identified from Phase I, we also conducted Phase II, which employed the SEGRESS guidelines to analyze primary studies that empirically evaluated the use of LLMs in SPM via five research questions. Phase II also assessed coverage across PMBOK processes and synthesized reported mechanisms, outcomes, research designs and evaluation approaches.

Results: The SLR (Phase II) shows that studies employ encoder-only and decoder-only LLM architectures in equal proportion (17 studies each). Their applications focus on a narrow set of SPM tasks, including effort estimation, project planning, and requirements-related activities. Coverage across PMBOK processes is uneven, with limited attention to areas such as procurement, stakeholder, quality, and cost management. Most studies rely on proof-of-concept evaluations, with scarce industrial validation and heterogeneous evaluation metrics.