AI tool for programming and debugging
In the last months, numerous AI assistants were tested in project daily life, from autocomplete to autonomous error-fixing agents. The central question is which AI tool for programming and debugging is the most reliable, tailored to the respective IDE, the stack and the data protection requirements. Since September 2025 the Copilot Coding Agent is generally available, which significantly expands the range of features. At the same time, Amazon Q Developer, JetBrains AI Assistant, Sourcegraph Cody, Cursor and Tabnine offer specific strengths, especially in error detection, code explanation and team integration.
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Introduction & overview
An AI tool for programming today comprises three main functions: Autocomplete (editor completions), Chat (questions about the code) and Agents (multi-step tasks including tests and refactorings). Copilot Chat answers coding, testing and debugging questions directly in the editor or on GitHub. In Visual Studio and VS Code Copilot understands call stacks and variables when debugging and suggests targeted fixes. Amazon Q Developer explains, refactors and fixes marked code sections, generates tests and optimizes code directly from the IDE. JetBrains AI Assistant explains code as well as build and SQL errors and suggests concrete corrections. Sourcegraph Cody pulls context from local and remote repositories and thus provides precise answers in large codebases. Cursor integrates agents directly into an AI-first IDE, including task assignment and multi-file changes.
GitHub Copilot started 2021 in the Technical Preview and was generally available in 2022. In 2025 the Agent mode followed in VS Code/Visual Studio, that performs multi-step tasks. Since September 25, 2025, the Copilot Coding Agent is generally available. Amazon Q Developer has also established itself as an IDE assistant focusing on code explanations, tests, upgrades and debugging. JetBrains AI Assistant has expanded explain- and fix-functions for compiler and SQL errors. Sourcegraph introduced 'agentic chat', which actively collects context from code, shell and the web. Copilot Workspace, an experimental agent-dev environment was discontinued.
Detailed analysis of the tools
Providers push for agents to turn sporadic suggestions into 'colleagues' that take over entire task chains – from the fix to tests to the pull request. GitHub positioniert Copilot open to multiple models and integrations. Amazon optimiert Q Developer for AWS workflows up to security-relevant best practices and deliberate permission assignment. Sourcegraph spielt seine Stärke bei großflächigem Kontext aus, which is critical when debugging distributed systems. JetBrains adressiert typische Fehlersituationen in its IDEs with direct explain and fix buttons. The 'best' tool depends heavily on whether you works GitHub-centric (Copilot), is AWS-first (Amazon Q), JetBrains uses (AI Assistant), large/heterogeneous repos (Cody), or an AI-first IDE with agents (Cursor).
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The clip clearly shows how the Copilot Coding Agent plans tasks, changes code, and automates fixes – useful as a side note on the agent principle.
It is established that Copilot beim Debuggen hilft, Call-Stacks und Frames versteht und Fix-Vorschläge in Visual Studio liefert. The chat answers debug questions. Amazon Q Developer explains and fixes marked code, generates tests and supports refactorings directly from the IDE. Cody hat Kontext auf lokaler Repo-Ebene und aus entfernten Repositories; agentic chat sammelt aktiv relevanten Kontext. JetBrains AI Assistant explains build and SQL errors and suggests corrections. The Copilot Coding Agent is generally available.
Quelle: allaboutai.com
The question of code quality is central when choosing AI tools for programming.
The claim that a tool is objectively the best for all teams is unclear, as this depends on the IDE, codebase, compliance and cloud stack. The providers pursue different strengths ( Copilot, Amazon Q, Cody, JetBrains AI Assistant). The claim that with AI you no longer need reviews/tests is false. All providers emphasize human oversight and best practices; Amazon Q warnt explizit vor unbedachten Tool-Berechtigungen. GitHub verweist auf verantwortungsvolle Nutzung und Trust-Center-Schutzmaßnahmen.
Practical implications
Developers report productivity gains, but also erroneous suggestions, especially for complex code paths. GitHubs eigene Studie zeigt signifikante Effekte, betont aber methodische Grenzen. Legally, there is pushback: Die Copilot-Klage rund um Trainingsdaten und Lizenzen läuft seit 2022, where courts have dismissed a large portion of the claims. Strategically, providers expand the agent approach and model choice, etwa GitHub mit einer Multi-Model-Strategie und Partnern.
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ChatGPT as a versatile tool for code generation and development support.
For teams focused on GitHub and VS Code, it is Copilot (inklusive Debug-Guides) der pragmatischste Start. In AWS-heavy environments, it delivers Amazon Q Developer starke Debug- und Upgrade-Flows in IDE und CLI, with attention to permissions, data flow and region settings ( Datenspeicherung, Sicherheit). When using JetBrains IDEs, the AI Assistant nahtlos integriert und hilft bei konkreten Fehlersituationen. Large, fragmented code bases benefit from Cody-Kontext und agentischem Chat. Under strict data privacy requirements, are Tabnines No-Train/No-Retain-Politik und Optionen für private Installationen zu prüfen.
Quelle: YouTube
The short walkthrough shows how Amazon Q Developer is set up in VS Code and used for debugging – a good addition for AWS teams.
Open questions concern realistic measurement of quality (faster fixes, fewer regressions, more stable builds), which require team metrics and A/B comparisons ( GitHub-Studie). Also the development of the legal framework around training data and licenses is relevant ( Copilot-Klage). With cloud agents like Amazon Q Developer, the control of models, regions and retention, including opt-outs and storage paths, needs to be clarified ( Datenspeicherung, Datenschutz). Finally, boundaries for agents in production must be defined, such as permissions, sandboxes and mandatory reviews ( Sicherheit, FAQ).
Conclusion & Outlook
The 'best' AI tool for programming and debugging is the one that fits the respective reality: Copilot für den GitHub-/VS-Code-Standard und agentische Workflows, Amazon Q Developer für AWS-zentrische Entwicklung und sichere Tool-Kontrolle, JetBrains AI Assistant für tief integrierte IDE-Hilfen, Cody für riesige Codebasen mit starkem Kontext, Cursor für AI-first-Agenten and Tabnine bei strikter Datenhoheit. The decisive factors are the IDE, repository size, compliance and the maturity of tests/reviews. Then AI goes from gimmick to a reliable debugging partner.
Quelle: youtube.com
The future of software development: programming with Artificial Intelligence.