Toward Reliable Forward Snowballing in Systematic Literature Reviews: A Comparative Study and Framework Proposal

Systematic Literature Reviews (SLRs) play a vital role in the software engineering field by synthesizing existing knowledge, identifying research gaps, and guiding future investigations with methodological rigor. Given the rapid growth of published research, techniques such as snowballing are essential. Forward snowballing, in particular, helps to discover newer studies that cite key seed papers, enhancing the completeness of SLRs. Despite its value, when performed manually, it remains a labor-intensive and error-prone task. To address this challenge, we mapped existing tools that support forward snowballing. Our analysis focuses on the capabilities of these tools to automate core tasks, including the identification of relevant articles and the extraction of bibliographic metadata. We critically examine their reliability by comparing their outputs against a manually curated forward snowballing process. Key evaluation criteria include the completeness and relevance of the metadata retrieved, the quality of the retrieved scientific databases, and the ease of integration into SLR workflows. As future research, based on our findings, we will propose a framework that takes advantage of existing tools to automate forward snowballing.