Artificial Intelligence (AI) tools are increasingly becoming essential allies in the software development lifecycle. While most developers are already familiar with mainstream solutions like GitHub Copilot, Tabnine, or DeepCode, there exists a cohort of underrated AI-powered tools quietly revolutionizing debugging and code review workflows.
TLDR: While AI-powered platforms like GitHub Copilot dominate the spotlight, several lesser-known tools are redefining the efficiency of code debugging and review for developers. From intelligent log analyzers to explainable AI for code comprehension, these tools can drastically reduce time spent on error diagnosis and peer review. Below, we explore six underrated AI tools that developers should consider adding to their tech stack. Discover how these niche apps are transforming pain points in debugging and enabling cleaner code releases.
1. CodeWhisperer by AWS
Best for: Integrated AI code suggestions within AWS cloud environments
CodeWhisperer might not steal the limelight like GitHub Copilot, but it’s a powerhouse for developers working heavily in the AWS ecosystem. It uses machine learning to deliver real-time code suggestions and, crucially, it incorporates security scanning while suggesting code snippets—something many mainstream tools overlook.
Its unique advantage lies in being context-aware of AWS SDK usage and integrations. Whether you’re launching EC2 instances or analyzing S3 buckets, CodeWhisperer suggests cloud-smart methods that align with secure and scalable best practices.
- Supports Java, Python, and JavaScript (with more on the way)
- Offers built-in security scans during code generation
- Trains on both internal code and public repositories for better relevance
2. Snyk Code
Best for: AI-assisted security and bug scanning integrated inside your IDE
Snyk is often celebrated for its open-source vulnerability scanning, but Snyk Code—its AI-driven code analysis feature—is lesser-known yet incredibly powerful. Its deep learning models detect potential bugs, vulnerabilities, and even logic flaws while maintaining blazingly fast performance in real-time as you code.
Unlike traditional linters, Snyk Code prioritizes accuracy and gives actionable, easy-to-understand advice with direct context, making it ideal for junior developers or large teams with aggressive deployment cycles.
- Utilizes machine learning models trained on millions of codebases
- Supports popular languages such as JavaScript, Python, Java, and TypeScript
- Works well across major IDEs like VS Code, IntelliJ, and Eclipse
3. Debugger.ai
Best for: Interactive debugging with conversational AI
Debugger.ai offers a fresh perspective on tracking down software bugs. Rather than just inspecting logs and trying to decipher stack traces, it allows you to talk to your debugging environment. By inputting questions like “Why does this function break when input is null?” developers receive intelligent, context-aware responses from the AI based on runtime data and control flow analysis.
This tool is particularly useful when navigating poorly documented legacy systems or complex microservices architectures where reproducing bugs is half the battle.
- Conversational querying using natural language
- Integrates with standard debuggers and telemetry tools
- Flags anomalous behavior in real-time and suggests probable causes
4. ReviewBot
Best for: Lightweight, AI-augmented peer code reviews on pull requests
While large enterprises often use enterprise-grade code review tools, smaller teams or open-source contributors can benefit from the streamlined insights provided by ReviewBot. This lightweight GitHub plugin uses natural language processing and static code analysis to generate initial review comments automatically.
It doesn’t just scan for formatting or style issues—it looks into code design patterns, logic redundancy, and API misuse. ReviewBot gives an objective first-pass review, helping human reviewers focus on higher-level architectural feedback.
- Works within GitHub Pull Requests
- Supports JavaScript, Go, Python, and more
- Supports customized rules and team-level guidelines
5. OpenReplay + Assist AI
Best for: Session replay with AI-assisted insights
OpenReplay is an open-source session replay tool that records what users do in your web app. When paired with Assist AI, it becomes a debug gold mine. The integrated AI scans recorded sessions and logs, interpreting abnormal event flows and surfacing reactive insights such as “user encountered a rendering issue due to data fail from API X.”
This makes it easier not only to see what went wrong but also to understand why it happened—without sifting through thousands of lines of logs.
- Visual playback of frontend sessions
- Assist AI suggests root causes and relevant file paths
- Helps reduce mean time to resolution (MTTR) for frontend bugs
6. ExplainDev
Best for: Code explanation and onboarding junior developers
Ever received a complex function in a team handoff and had no idea what it did? ExplainDev aims to solve that. By using NLP-powered AI, it provides natural-language annotations and breakdowns of complex code snippets.
This is incredibly useful during code reviews or technical onboarding, as it helps newer developers navigate legacy codebases or less-readable scripts quickly and accurately.
- Explains code in human-friendly language
- Works within VS Code as a plugin
- Great for documentation and internal code sharing
When used alongside intelligent autocomplete tools, ExplainDev helps bridge the schism between intent and implementation, ensuring everyone can contribute meaningfully to a shared codebase.
Final Thoughts
While high-profile AI tools are dominating the headlines, it’s these niche, specialized tools that can dramatically improve a developer’s daily workflow. Whether it’s through intelligent session replay, AI-assisted conversations with your codebase, or real-time, IDE-integrated error detection, each of these platforms brings something distinct to the table.
Progressive teams aiming to deliver high-quality software at speed are tapping into these under-the-radar solutions for smarter debugging, deeper code understanding, and faster peer reviews. If you’re still leaning solely on manual reviews or static tools, consider adding one or two from this list to your stack—they could be the secret ingredients that boost your workflow efficiency.
Pro Tip: Most of these tools offer free tiers or open-source versions, so there’s no harm in trying them out in your next sprint. You may just discover your team’s next go-to debugging sidekick.