Parameterized Tests (PTs) are designed to reduce duplication and improve input coverage in unit testing, yet their real-world adoption patterns, design styles, and practical impacts remain poorly understood. While prior work has explored the theoretical foundations of parameterized testing and proposed automated approaches to generate or transform PTs, there is still no large-scale empirical evidence on how PTs are used within modern JUnit-based ecosystems. To address this gap, we propose a comprehensive empirical study of more than 260 Apache Java projects. Using automated static analysis and commit-history mining, we will characterize PT adoption at community, project, and contributor levels (RQ1); analyze the usage and design patterns of parameter sources through annotation-level mining and saturation-based qualitative coding (RQ2); and examine how PTs differ from non-PTs in structural complexity, cognitive complexity, and redundancy, including how complexity varies across different data source patterns (RQ3). The expected results will provide the first ecosystem-scale evidence on how developers employ parameterized testing in practice, revealing its design idioms, trade-offs, and potential maintainability implications. These findings will inform practitioners, framework designers, and researchers working on automated test generation and maintenance tools.