System for systematic literature review using multiple AI agents: Concept and an empirical evaluation
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916959352782848 |
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| author | Sami, Abdul Malik Rasheed, Zeeshan Kemell, Kai-Kristian Waseem, Muhammad Kilamo, Terhi Saari, Mika Duc, Anh Nguyen Systä, Kari Abrahamsson, Pekka |
| author_facet | Sami, Abdul Malik Rasheed, Zeeshan Kemell, Kai-Kristian Waseem, Muhammad Kilamo, Terhi Saari, Mika Duc, Anh Nguyen Systä, Kari Abrahamsson, Pekka |
| contents | Systematic literature review (SLR) is foundational to evidence-based research, enabling scholars to identify, classify, and synthesize existing studies to address specific research questions. Conducting an SLR is, however, largely a manual process. In recent years, researchers have made significant progress in automating portions of the SLR pipeline to reduce the effort and time required for high-quality reviews; nevertheless, there remains a lack of AI-agent-based systems that automate the entire SLR workflow. To this end, we introduce a novel multi-AI-agent system designed to fully automate SLRs. Leveraging large language models (LLMs), our system streamlines the review process to enhance efficiency and accuracy. Through a user-friendly interface, researchers specify a topic; the system then generates a search string to retrieve relevant academic papers. Next, an inclusion/exclusion filtering step is applied to titles relevant to the research area. The system subsequently summarizes paper abstracts and retains only those directly related to the field of study. In the final phase, it conducts a thorough analysis of the selected papers with respect to predefined research questions. This paper presents the system, describes its operational framework, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision. The code for this project is available at: https://github.com/GPT-Laboratory/SLR-automation . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08399 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | System for systematic literature review using multiple AI agents: Concept and an empirical evaluation Sami, Abdul Malik Rasheed, Zeeshan Kemell, Kai-Kristian Waseem, Muhammad Kilamo, Terhi Saari, Mika Duc, Anh Nguyen Systä, Kari Abrahamsson, Pekka Software Engineering Systematic literature review (SLR) is foundational to evidence-based research, enabling scholars to identify, classify, and synthesize existing studies to address specific research questions. Conducting an SLR is, however, largely a manual process. In recent years, researchers have made significant progress in automating portions of the SLR pipeline to reduce the effort and time required for high-quality reviews; nevertheless, there remains a lack of AI-agent-based systems that automate the entire SLR workflow. To this end, we introduce a novel multi-AI-agent system designed to fully automate SLRs. Leveraging large language models (LLMs), our system streamlines the review process to enhance efficiency and accuracy. Through a user-friendly interface, researchers specify a topic; the system then generates a search string to retrieve relevant academic papers. Next, an inclusion/exclusion filtering step is applied to titles relevant to the research area. The system subsequently summarizes paper abstracts and retains only those directly related to the field of study. In the final phase, it conducts a thorough analysis of the selected papers with respect to predefined research questions. This paper presents the system, describes its operational framework, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision. The code for this project is available at: https://github.com/GPT-Laboratory/SLR-automation . |
| title | System for systematic literature review using multiple AI agents: Concept and an empirical evaluation |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2403.08399 |