  Systematic Literature Reviews (SLRs) have become the foundation of
evidence-based studies, enabling researchers to identify, classify, and combine
existing studies based on specific research questions. Conducting an SLR is
largely a manual process. Over the previous years, researchers have made
significant progress in automating certain phases of the SLR process, aiming to
reduce the effort and time needed to carry out high-quality SLRs. However,
there is still a lack of AI agent-based models that automate the entire SLR
process. To this end, we introduce a novel multi-AI agent model designed to
fully automate the process of conducting an SLR. By utilizing the capabilities
of Large Language Models (LLMs), our proposed model streamlines the review
process, enhancing efficiency and accuracy. The model operates through a
user-friendly interface where researchers input their topic, and in response,
the model generates a search string used to retrieve relevant academic papers.
Subsequently, an inclusive and exclusive filtering process is applied, focusing
on titles relevant to the specific research area. The model then autonomously
summarizes the abstracts of these papers, retaining only those directly related
to the field of study. In the final phase, the model conducts a thorough
analysis of the selected papers concerning predefined research questions. We
also evaluated the proposed model by sharing it with ten competent software
engineering researchers for testing and analysis. The researchers expressed
strong satisfaction with the proposed model and provided feedback for further
improvement. The code for this project can be found on the GitHub repository at
https://github.com/GPT-Laboratory/SLR-automation.
