An average AI engineer salary reaches $135K in the USA, $89K in Europe, and $66K in Asia. It’s one of the highest among engineer salaries globally.
What do they get paid for? AI engineers are valuable because they develop revenue-increasing solutions. One of such solutions is an AI debugger, a product wanted by every development team.
In traditional debugging, developers spend up to 50% of their coding time finding and fixing bugs. This process frustrates development teams, consumes productivity and extends project timelines. Fortunately, Generative AI-powered debugging can lighten the load. In this article, we’ll explain how generative AI is automating bug fixes and review some top debugging AI tools.
How Generative AI Works in Debugging
Generative AI relies on large language models (LLMs) and machine learning algorithms that analyze patterns within the code. By training on extensive datasets of code, including errors and solutions, generative AI can predict and correct errors based on context. The AI examines the code syntax, variable usage, function flow, and known patterns of common bugs.
For example, if an AI code debugger detects that a loop condition could lead to an infinite loop, it may recommend adding a break condition. Over time, these tools become more adept at identifying complex bugs as they are exposed to diverse coding structures and logic flows.
4 Benefits of Using an AI Debugger
- Speed and Efficiency
An AI code debugger can reduce the time needed to find and fix bugs, which speeds up release cycles up to 40% and shortens time-to-market for software products. - Enhanced Code Quality
With tools that can spot potential issues, teams maintain higher code quality, reducing the likelihood of bugs reaching production. - Cost Savings
Automated bug-fixing minimizes the costs associated with delayed releases and post-launch fixes. Some companies report savings of up to 30% on debugging-related expenses after integrating AI tools into their workflows. - Reduction in Burnout
By eliminating repetitive debugging tasks, generative AI allows developers to engage in more rewarding and creative coding activities (like creating new features), decreasing burnout and enhancing team morale.
Taking into account those benefits, it becomes clear why code debugging AI is widely adopted in healthcare, finance, e-commerce, automotive, manufacturing, entertainment, and education — industries with immense data sets, constant innovations and a huge price for a mistake.
Surely, the tool that worked for developing a trading platform, may not work that well for a patient care portal. To get the most of an AI code debugger, it is worth considering a tool based on the project’s tech stack and workflows.
Top Generative AI Tools for Debugging
Meet the high-rated AI tools that automate debugging with high accuracy and speed.
GitHub Copilot
💼 Project Types. Suitable for a wide range of projects including web development, mobile applications, and backend services.
⚙️ Tech Stack. Works seamlessly with popular languages such as JavaScript, Python, TypeScript, Ruby, Go, and more. It integrates well with frameworks like React, Node.js, and Django.
🤹 Workflows. Is powered by OpenAI’s Codex and integrates directly with code editors like Visual Studio Code. Ideal for developers using Visual Studio Code, JetBrains IDEs, and GitHub itself. Provides inline suggestions and complete code snippets based on comments or existing code context.
DeepCode (now Snyk Code)
💼 Project Types. Primarily focused on code quality analysis and security vulnerability detection. DeepCode’s specialized models are especially helpful for developers looking to improve code quality in large projects.
⚙️ Tech Stack. Supports multiple languages including Java, JavaScript, Python, and TypeScript. It integrates well with CI/CD pipelines.
🤹 Workflows. Best used in conjunction with existing code repositories in IDEs like IntelliJ IDEA and Visual Studio Code. Evaluates each line for potential issues, compares it with similar code structures, and provides real-time solutions.
Tabnine
💼 Project Types. Versatile enough for various project types including web apps, mobile apps, and enterprise software.
⚙️ Tech Stack. Supports many programming languages such as JavaScript, Python, Java, C++, and more. It integrates with popular IDEs like VS Code and IntelliJ IDEA.
🤹 Workflows. While it primarily serves as a productivity tool, Tabnine uses machine learning models to autocomplete code snippets and identify syntax errors. It is particularly effective in environments where predictive coding is essential. By adapting to each developer’s coding style, Tabnine offers personalized debugging support.
CodeGenie
💼 Project Types. Generally used for generating boilerplate code and snippets across various applications.
⚙️ Tech Stack. Compatible with multiple programming languages but may have limited support compared to others.
🤹 Workflows. Useful in rapid application development scenarios where quick code generation is needed. Identifies, prioritizes, and suggests fixes for bugs based on the likelihood of impact. This prioritization enables teams to focus on critical issues first. By analyzing patterns in code, CodeGenie can even offer proactive suggestions for preventing potential bugs.
Combining AI Debuggers for Maximum Effectiveness
- Integrated Usage. Use GitHub Copilot for inline suggestions while coding to enhance productivity. Pair it with Tabnine for pop-up completions to cover more ground in terms of suggestions.
👉 Example. Enable GitHub Copilot for real-time suggestions as you type and use Tabnine for additional context-aware completions when you invoke it manually.
- Sequential Workflow. Start your coding session with GitHub Copilot to generate initial code structures or functions based on comments. Afterward, run DeepCode to analyze the generated code for vulnerabilities or quality issues.
👉 Example. Use Copilot to draft a function and then utilize DeepCode to ensure that the function adheres to best practices and is free from security flaws.
- Enhanced Debugging. If you encounter bugs or issues during development, leverage the capabilities of DeepCode to identify potential problems in your codebase after using GitHub Copilot or Tabnine for initial coding.
👉 Example. After writing a module with Copilot or Tabnine’s assistance, run DeepCode to check for any overlooked errors or compliance issues.
- Documentation Generation. Use GitHub Copilot not only for coding but also for generating documentation based on your code comments. This helps maintain clear documentation alongside development.
👉 Example. As you develop features using Copilot, prompt it to create relevant documentation snippets automatically.
- Customization and Configuration. Adjust settings in each tool to minimize conflicts (e.g., disabling inline completions in one tool if using another). This ensures that you can see suggestions from both tools without them overriding each other.
The Hidden Danger of Debugging AI: How to Mitigate Risks
Generative AI for debugging isn’t without challenges. False positives or suggestions that might not fit the project context can slow down workflows if developers must manually verify each fix. Additionally, an AI debugger may have privacy and security concerns, as it requires access to code repositories.
Nevertheless, automated debugging, most likely, will become a standard development practice soon and the need to implement this practice properly will only grow year by year. That’s why you need to know how to mitigate the risks: to hire developers familiar with AI tools and set clear AI usage policies to align with changing code standards and security protocols.
