Best Debugging Tools for GitHub Copilot

Xcode is Apple’s integrated development environment for building, testing, and distributing apps across Apple platforms. It provides powerful tools for coding, debugging, profiling, and simulation in one unified workspace. Predictive code completion and coding intelligence help developers write cleaner, faster code. Xcode supports advanced debugging and performance analysis to identify issues early. Built-in simulators allow developers to prototype apps across Apple devices without physical hardware. Testing frameworks ensure apps meet quality and performance standards. Xcode streamlines the entire app development lifecycle from idea to deployment.

Visualize and resolve thread performance issues, memory leaks, and structural app issues, during development or in production. Koin’s container captures only essential app behavior data, ensuring minimal overhead. Understand and debug the lifecycle of component instances and scopes, their loading times, and execution among different threads. This ensures proper management and resource allocation while identifying and preventing errors such as memory leaks, performance issues, and ANRs. Analyze dependencies to understand relationships between different components. Optimize the dependency injection process, simplifying development and debugging efforts. This proactive analysis ensures reliability and scalability by detecting and addressing potential issues early. Gain granular traceability of components and lifecycles with Kotzilla’s dedicated API. Visualize identified issues and application crashes to get comprehensive insights into your app’s performance and stability.

FlowLens is an AI-native debugging and session-recording tool that captures everything needed for correct, context-aware bug diagnosis and lets AI coding agents fix bugs autonomously. With a simple browser extension and optional MCP server, FlowLens records full user sessions, including video of the UI, network-request data, console logs, user interactions (clicks, inputs, navigation), storage state (cookies, local/session storage), system info, and more, all synchronized on a unified timeline. Once a bug is reproduced, FlowLens bundles that complete context into a single “flow” that can be shared via link. AI coding agents compatible with MCP (such as those from major providers) can then load the flow, inspect network activity, error logs, UI state, and user inputs, and automatically analyze root causes and suggest or even generate code fixes. This removes the need for manual replays, copying and pasting logs, or writing verbose bug descriptions.