Signal Communication on GitHub: User Perspectives on Repository Evaluation in Open-Source Development

Open-source platforms like GitHub expose extensive data about project activities, contributors, and quality metrics. However, not all data is equally relevant for decision-making, and signal relevance varies across user contexts. This paper investigates how different user types interpret signals when evaluating repositories. Through tool development and empirical studies with GitHub developers, we identify five user personas with distinct signal preferences: (1) Contributors seeking alignment and complexity signals, (2) Learners prioritizing documentation and maturity, (3) Casual Users focused on functionality and maintenance, (4) Employees evaluating stability and security, and (5) Decision-Makers assessing adoption and governance. Our core contributions are: (1) a signal taxonomy for open-source repository evaluation grounded in signal theory, (2) a five-persona classification framework demonstrating that signal importance varies by user type, (3) development and validation of a GitHub signal aggregation tool, (4) empirical findings from think-aloud protocol studies with developers, and (5) practical design recommendations for improving information architecture in open-source collaboration platforms. The research demonstrates that effective repository evaluation requires context-sensitive information presentation. We argue that transparent platforms must complement data availability with intelligent information architecture tailored to user perspectives and decision contexts. Research conducted: Fall 2015 (Carnegie Mellon independent study). Archival publication: January 2026. This version includes retrospective analysis examining which findings have persisted and which have aged given GitHub platform evolution since 2015.