CODOR - Realtime Test Companion

A unified web-based development workspace designed to bring coding, testing, debugging, version control, and software maintenance into one continuous workflow. The application opens with a simple “Start a Workspace” experience that leads directly into a full browser-based text editor. Developers can create and edit virtually any text-based file type, from `.txt` and `.md` to `.json`, `.yaml`, `.html`, `.css`, `.js`, `.ts`, `.py`, `.java`, `.cpp`, `.sql`, configuration files, and custom extensions. Multiple files can remain open in connected tabs and operate as a single project through a shared virtual filesystem, allowing imports, dependencies, tests, and relationships between files to be understood automatically. The editor includes an AutoRun mode that continuously verifies supported code as it is developed. When enabled, meaningful changes trigger analysis and execution automatically, providing immediate output, diagnostics, and test results without requiring developers to repeatedly switch between the editor and external tools. AutoRun can also be disabled when manual execution is preferred. The workspace contains an integrated testing system covering unit, integration, API, UI, end-to-end, regression, edge-case, error-handling, performance, database, and security testing. The system analyzes code changes to determine which components and tests may be affected, allowing developers to run relevant tests instead of manually searching for what needs to be verified. Failed tests provide structured information such as expected versus actual behavior, affected files, stack traces, and likely causes. Debugging is directly connected to testing. A failed test can lead directly to the relevant source location, execution state, variables, call stack, and debugging tools. Once a developer modifies the code, affected tests can be rerun automatically to verify the change and detect regressions. AI assistance is integrated into the development workflow rather than existing as a separate chatbot. It can explain errors, analyze failures, generate test cases, identify potential edge cases, suggest fixes, explain Git changes, and assist with maintenance. Proposed code changes are previewed before being applied and are followed by automated verification through the normal testing system. The application also provides integrated Git and development-maintenance capabilities. Developers can inspect diffs, create and switch branches, review changes, resolve merge conflicts, prepare commits, and verify affected tests before committing. Maintenance analysis can detect issues such as unused code, duplicated logic, dependency problems, missing tests, excessive complexity, stale TODOs, deprecated APIs, and potentially flaky tests. A project intelligence layer continuously builds a model of the workspace, connecting files, symbols, dependencies, tests, changes, failures, Git history, and maintenance issues. This allows the system to understand how a code change affects the wider project and provide context-aware development assistance. The workspace also includes an efficiency score that tracks development signals such as successful executions, error recovery, and verification efficiency. This provides a lightweight view of how effectively the developer is moving through the write-test-debug-fix cycle without replacing actual test and verification results. The result is a single development environment where developers can write, test, debug, review, commit, and maintain software without constantly moving between disconnected tools.