User and developers are likely the target audience. The problem could be related to inefficiencies in beta testing processes. For example, tracking bugs, managing feedback, analyzing performance metrics. The solution is jtbeta, perhaps providing tools to visualize beta testing data, automate reporting, prioritize critical bugs.

Implementation details would require explaining the architecture, tech stack (Java, maybe Spring Boot, React for UI), any novel algorithms implemented. API design might be important if developers can plug into other systems.

The methodology section might detail the approach taken in developing jtbeta. Was it a machine learning model trained on beta test data? A new algorithm for bug detection? Or maybe a tool for managing beta test phases? I need to hypothesize based on possible functionalities.

Make sure the paper's contribution is clear: is it a novel approach, a new tool in the existing landscape, an optimization? Differentiating factors are crucial for the paper's impact.

The paper should compare with existing solutions: existing beta testing tools like TestFlight, Firebase Beta Testing, etc. Highlight what features jtbeta offers that others don't. Maybe it's open-source, integrates with CI/CD pipelines differently, supports specific platforms better.