Cut Through the Static Analysis Noise! Find & Fix Violations Faster With AI

A common barrier to successfully adopting and implementing static analysis practices is figuring out how to sort through static analysis noise to determine which violations are important to fix and which can be suppressed.

Development teams that struggle with noisy static analysis findings often waste development time triaging static analysis violations to determine what’s important to their program and what violations to prioritize for remediation versus what can be ignored.

When teams do not have a way to quickly sort through the noise, it impacts productivity and leaves developers frustrated. Did you know that you can cut through static analysis noise with AI?

Watch our webinar to learn how applying AI/ML to static analysis can:

• Help development teams reduce static analysis noise and prioritize static analysis findings for remediation.
• Streamline compliance to secure coding standards for Java and .NET applications, such as OWASP, CWE, PCI-DSS, or UL2900.
• Accelerate the remediation of static analysis violations and expand developer knowledge on best practices for developing safe and secure code.

Why Static Analysis Matters

Static analysis is key to shifting left in software development, meaning you catch issues earlier in the process. This helps reduce project costs because fixing bugs later on is much more expensive. The later a defect is found, the more time and resources are needed to understand, locate, and fix it. Static analysis tools, like Parasoft’s Jtest, C/C++test, and dotTEST, are designed to catch these issues early, harden the codebase, and help teams comply with industry standards like OWASP and CWE.

The Problem with Static Analysis Noise

When teams run static analysis on large or complex codebases, they can be hit with thousands of findings. This “noise” makes it difficult to distinguish between critical violations that need fixing and those that are less important or irrelevant to the application. This can lead to:

• Overwhelm and Frustration: Developers get bogged down by too many findings, leading to frustration and less trust in the tools.
• Wasted Development Time: Investigating and dismissing irrelevant findings takes time away from developing new code or fixing critical issues.
• Slower Development Cycles: Reviewing and dismissing alerts can cause delays in project timelines.
• Unnecessary Code Changes: Developers might make changes that aren’t needed, increasing maintenance overhead.
• Distraction from Real Issues: Legitimate problems can be overlooked if developers become used to dismissing alerts.
• Security Vulnerabilities: In security-focused analysis, excessive noise can cause critical vulnerabilities to be missed.

How AI Helps with Static Analysis

AI and machine learning can significantly optimize the static analysis workflow. Parasoft uses four key AI/ML techniques to tackle these challenges:

1. AI Noise Classification

AI analyzes past triage actions (what developers chose to fix, suppress, or ignore) to predict the likelihood of new violations being fixed or ignored. This helps filter out noise, allowing teams to focus on the most important findings.

2. Root Cause Clustering (Violation Hotspots)

AI identifies common problems in the code that lead to multiple violations. These “hotspots” are grouped together, allowing a developer to fix one line of code and resolve many violations at once. This reduces rework and prevents duplication of effort across the team.

3. Skill-Based Assignment

AI builds profiles of developers based on the types of violations they have successfully fixed in the past. When new violations arise, the system can recommend or assign them to the developer best suited to handle them based on their skills and experience. This improves developer productivity and satisfaction.

4. Generative AI Code Fixes

Generative AI technology can create code fix recommendations for static analysis violations. Developers can get suggestions for fixes directly within their IDE, review them, and apply them, significantly speeding up the remediation process.

Parasoft Solutions in Action

Parasoft’s Development Testing Platform (DTP) acts as a central quality hub. It offers customizable dashboards to visualize testing results and trends. Key AI-powered features include:

• Fix Action Prediction: DTP analyzes past triage data to predict which violations are likely to be fixed, helping teams prioritize. For example, it can identify violations with a high confidence factor for fixing.
• Violation Hotspots: The platform highlights areas of code with a high concentration of violations, allowing developers to address multiple issues by fixing a single line.
• Recommended Assignees: DTP suggests which developer is best suited to fix a particular violation based on their past remediation history.

In a demonstration using Visual Studio, the dotTEST plugin showcased generative AI code fixes. When a violation like “Do not catch System.Exception” is found, a developer can request an AI-generated fix. The AI provides a recommended code snippet, explaining the context of the violation and offering a solution. This can be easily copied and pasted into the code, saving significant time compared to manual research and implementation.

Benefits of AI in Static Analysis

Integrating AI into static analysis offers several advantages:

• Reduced Noise: AI helps filter out irrelevant findings, making it easier for teams to focus.
• Faster Triage and Remediation: AI assists in understanding what needs fixing and can even suggest code fixes, accelerating the entire process.
• Skill Development: Developers can learn how to fix specific coding standards and violations more quickly.
• Fewer Defects in Production: Faster remediation in early stages means fewer bugs make it to production, reducing costly late-stage fixes.
• Improved Developer Experience: By automating tedious tasks and providing relevant guidance, AI makes static analysis less burdensome and more productive for developers.