You agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.

Automated Code Audits: Tools, Types, and Benefits

Table of content:
Join our newsletter

Your privacy is important to us, privacy policy.

Code reviewing is a fundamental process in modern software development used to audit source code for potential issues, bugs, vulnerabilities, and adherence to coding standards. It provides developers and security engineers with valuable insights into the quality and security of their codebase. From minor things like variable naming conventions to severe vulnerabilities hiding in the code to verifying the correctness of the code.

In this article, we take a closer look at the benefits and challenges of automated code audits and the tools developers can use to implement automated code reviews into their product lifecycle. 

What Is an Automated Code Audit?

Automated code audit is a process in software development where tools are used to automatically audit and analyze source code for any potential issues. These tools usually analyze code statically with minimal or not an all need for human intervention, allowing developers to focus on mitigating issues rather than going through the labor-intensive process of manually reviewing everything in the code themselves.

Automated vs Manual Code Reviews

A traditional manual code review process involves human reviewers, developers, inspecting code line by line to identify issues. While manual reviews can be thorough, they are time-consuming and prone to human oversight. On the other hand, automated code audits utilize specialized software tools to analyze code automatically. While they may lack the nuanced understanding of complicated code and intentions, automated tools excel in identifying common issues quickly and consistently. Additionally, they free up developers' time to focus on more complex tasks like writing new code and testing it.

Aspect Automated Code Audits Manual Code Reviews
Process Efficiency Rapidly analyze large codebases in a fraction of the time Time-consuming, especially for large codebases
Consistency Apply predefined rules consistently across all code submissions Susceptible to variations in interpretation and judgment
Scalability Easily scale to accommodate growing codebases and teams, review doesn’t require a team of experts and can maintain the same pace as developer output Limited scalability becomes increasingly impractical over time and few personnel are qualified to review code, leaving limited experts to handle large development teams' output
Human Intervention Minimal human intervention required, developers can progress at their own pace Reliant on human reviewers for inspection and assessment, leading to potential bottlenecks as developers are forced to wait for reviews before progressing
Error and Bugs Detection Detects common issues and vulnerabilities automatically Relies on human experts being available to identify errors and vulnerabilities
Real-time Feedback Provides immediate feedback on code quality and security Feedback may be delayed due to manual inspection processes
Integration with Tools Integrates seamlessly with CI/CD pipelines and development tools Less integration with automated workflows and tools, leading to fewer reports and less of a paper trail
Learning Curve Requires familiarity with tool setup and configuration Relies on reviewers' expertise and experience with coding standards
Cost-effectiveness Initial investment in tools and setup, cost-effective in the long run Potential higher costs associated with manual labor and time spent
Coverage Comprehensive analysis of the entire codebase Coverage may vary depending on reviewer expertise and time constraints, but skilled reviewers can identify issues software might miss

When Should You Use Automated Code Audits?

Automated code audits are invaluable throughout the software development lifecycle. They can be particularly beneficial in the following scenarios:

  • Continuous Integration/Continuous Deployment (CI/CD): 

Integrating automated audits into CI/CD pipelines ensures that each code change undergoes rigorous scrutiny before deployment. Automated scanning for bugs and vulnerabilities and adherence to coding standards ensures the integrity of the application and minimizes the risk of introducing defects into production environments. 

  • Alignment Between Teams on Standards: 

In projects involving multiple development teams or contributors, automated code audits promote consistency and alignment on coding standards. By enforcing uniformity in code formatting, style, and structure, these audits streamline collaboration and reduce the likelihood of conflicts arising from divergent coding practices.

  • Early Bug Detection: 

Automated code audits excel at detecting bugs and vulnerabilities early in the development process. Flagging potential issues as soon as they are introduced enables developers to address them promptly, minimizing the time and effort required for debugging later stages of development and preventing further damage from taking place throughout the development process.

  • Better Code Quality: 

By enforcing coding standards and best practices consistently across the codebase, automated audits contribute to overall code quality. By identifying and addressing bugs, performance issues, and formatting standards, these audits elevate the quality of the codebase and enhance its maintainability over time.

Automated Code Analyzers Generate False Positives and Noise

While automated code analyzers play a crucial role in modern software development by identifying potential issues and vulnerabilities in codebases with high coverage, they are not immune to generating false positives and noise. 

False positives occur when a tool incorrectly flags specific code as problematic when it is, in fact, functioning correctly or just bugfree. Noise refers to irrelevant or trivial issues flagged by the tool, distracting developers from critical issues. This poor signal-to-noise ratio can lead to frustration and inefficiency within development teams, as it diverts valuable time and resources from more pressing concerns and may lead to reduced trust in such tools.

The absence of linters (tools that analyze code for potential errors) for code complexity exacerbates this problem. Developers may receive warnings for formatting and compiler warnings, but tools often lack prioritization, making it challenging for developers to identify which issues are critical and require immediate attention, and which can be addressed later. This lack of context or prioritization can hinder developers' ability to effectively address code quality issues. 

Additionally, the perception that addressing code issues is not worth the investment can be shortsighted. While prioritizing delivering the product within budget constraints may seem expedient, neglecting code quality can lead to long-term risks. Undetected bugs and poorly structured code can emerge during critical stages of product deployment or when scaling the application to meet growing demands. Although code analyzers assist in maintaining quality, they must be complemented with rigorous code testing and are not a complete replacement for other forms of testing.

Ultimately, prioritizing completion over quality may result in technical debt, hindering future development efforts. By taking a proactive approach to automated code audit and prioritizing the resolution of genuine issues over false positives and noise, development teams can ensure the long-term success and maintainability of their software projects.

What Issues Do Automated Code Analyzers Identify?

Automated code audit tools analyze various aspects of code to identify potential issues and improvements. These include:


Security vulnerabilities pose a significant threat to software applications, making security analysis a top priority for automated code audit tools. Static Application Security Testing (SAST) tools, like Checkmarx, specialize in detecting vulnerabilities for the code itself. These tools meticulously scan for common security weaknesses such as SQL injection, cross-site scripting (XSS), and insecure authentication mechanisms, enabling developers to proactively mitigate potential risks early in the development lifecycle. Of course, there’s a difference between the security tools needed for a server-side codebase and a client-side application, as well as across various programming languages. 

Additionally, Software Composition Analysis (SCA) platforms, such as Snyk, or are instrumental in identifying vulnerabilities present in external open-source dependencies. Given the widespread reliance on third-party libraries and frameworks in modern applications, ensuring the security of these components is paramount. SCA tools search for known vulnerabilities within open-source dependencies, allowing developers to identify and address potential security threats before they can be exploited by malicious actors.

Scanning dependencies is distinct from scanning new code lines, with the latter being far more complex. It involves more than just referencing a dictionary of all published and documented vulnerabilities in libraries used in an application. 

In conjunction with security software, adhering to industry guidelines and best practices, such as those outlined by the Open Web Application Security Project (OWASP), is essential for enhancing web security practices. 


Protecting sensitive data, particularly sensitive data such as Personally Identifiable Information (PII), has become paramount in software development in recent years due to the GDPR and similar privacy regulations. Automated code audit tools help identify and mitigate risks related to privacy and data protection. These tools scan codebases for instances of sensitive data being directly embedded within the code, such as credit card or social security numbers or private keys.

In more advanced situations, these tools assist in identifying potential data leakage risks by scanning for inadvertent leaks of sensitive information, ensuring compliance with data protection regulations and safeguarding user privacy. For instance, when developers accidentally leave  debug logs running during  production leading to the spill off of customer PII.

Bugs and Errors

Automated code audit tools excel at identifying common programming errors and bugs, including null pointer dereferences, memory leaks, and other errors. Static analysis of the code allows these issues to be identified and addressed early in the development process, enabling developers to prevent bugs from proliferating and ensure the stability and reliability of their software.

Some errors are logical at the code level and cannot be flagged by a compiler as it lacks the ability to recognize them. This depends on the programming languages used, but some bugs are trickier to catch, even if the code seems to work in certain environments. For example, a programming error in C++ could involve undefined behavior when the destructors of global scoped objects are called in uncertain order, and a compiler will not issue any warning. 

Code Format - Linters

Linters play a crucial role in enforcing coding standards and best practices, ensuring that code is well-formatted, readable, and maintainable. This is required for all programming languages, but especially for dynamic languages, which don't require a strict style, affecting code readability. If code is hard to read, it will be harder to maintain and extend over time. Automated code audit tools leverage linters to detect deviations from predefined style guidelines, such as indentation errors, inconsistent naming conventions, parameters ordering, levels of expressions chained, or unused variables, enhancing code readability and facilitating collaborative development efforts.


Automated code analysis tools assess code performance by identifying potential bottlenecks, inefficient algorithms, and resource-intensive operations. By analyzing code execution paths and profiling resource utilization, these tools pinpoint areas where optimizations can be made to improve overall performance and responsiveness. Normally, it’s about the fast path code, critical main-loop code like in video games, or just the business logic for finding the shortest path for a delivery address. Compression algorithms, searching through datasets, video codecs, AI, and other similar tasks require significant CPU and memory resources. Optimizing code for speed and efficiency improves the user experience and ensures optimal application performance under varying workloads. 


Measuring code complexity is essential for ensuring code maintainability and comprehensibility. While assessing complexity can be subjective, automated code analysis tools offer objective metrics to evaluate code complexity. Through analyzing factors such as code nesting depth, cyclomatic complexity, and code churn, these tools provide insights into areas of code that may be overly complex or difficult to understand. While subjective to some extent, these metrics provide valuable insights into the overall complexity of the codebase, enabling developers to identify and refactor overly complex code segments. These insights enable developers to identify and rewrite overly complex code segments, enhancing code maintainability and facilitating future modifications and enhancements.

There’s a big debate about using complexity calculator tools, and keeping code within a certain complexity score to make it simpler. But is it really worth fixing and rewriting large portions of code? It’s often  far easier to educate developers to keep functions short and follow strict principles. Besides, badly designed code at the module level, wrong splitting of methods and classes won't be caught by any tool today. Only a skilled and experienced developer performing a manual code review can flag these issues.

Dead Code

Automated code analysis tools also identify dead code, which refers to code segments that are no longer executed or have become redundant in runtime. And sometimes it becomes complicated when perfectly written code remains unused by customers. Dead code often accumulates over time due to changes in project requirements or refactoring efforts. While dead code may not directly impact functionality, it clutters the codebase, hampers readability, and might certainly impact performance (because of code cache issues, for instance). Automated tools flag dead code segments, allowing developers to remove them, streamline the codebase, and improve maintainability. By eliminating dead code, developers can declutter the codebase, enhance code readability, and minimize potential confusion for future maintainers.

Benefits of Automated Code Analysis

Automated code audit offers a wide variety of benefits for development teams, including:

Improves Code Quality

One of the primary advantages of automated code audit is its ability to enhance code quality. By automatically analyzing code for bugs, problematic code patterns, and adherence to coding standards, automated tools identify potential issues early in the development process. This proactive approach enables developers to address issues promptly, resulting in cleaner, more maintainable codebases.


As projects grow in size and complexity, the manual code review process can become increasingly cumbersome and impractical. Automated code audit tools, on the other hand, effortlessly scale to accommodate large codebases and distributed development teams. Whether reviewing a few lines of code or an entire application, automated tools maintain consistent review standards, ensuring thorough scrutiny without compromising efficiency.

Not to mention that doing security code reviews is significantly more challenging and requires specialized expertise, which most developers in many organizations still lack. It is definitely not scalable when app-sec engineers are typically outnumbered by a ratio of 1 to 100 developers.

Ensures Consistency

Consistency is key to maintaining code quality, fostering collaboration within development teams, and achieving success with a growing R&D organization as well as codebases. Automated code audit tools apply the same set of rules consistently across all code submissions, eliminating biases and ensuring adherence to coding standards. This consistency streamlines the review process, minimizes the risk of oversights, and fosters a culture of accountability and quality within the organization.

Enhances App Quality

The quality of an application is a direct reflection of the quality of its underlying codebase. By identifying and addressing potential bugs, vulnerabilities, and performance bottlenecks, automated code audit contributes to the overall quality and reliability of applications. This, in turn, enhances user experience, reduces the risk of downtime, strengthens the organization's reputation in the market, and speeds up delivery time.

Time Savings

Time is a precious commodity in software development, and every minute spent on manual tasks is a minute that could be invested in innovation and problem-solving. Automated code audit tools significantly reduce the time and effort required for code inspection, allowing developers to focus on more critical tasks such as feature development and bug fixing. Automating repetitive and time-consuming tasks through automated code audit streamlines the development process, speeding up time-to-market.


In today's regulatory landscape, compliance with industry standards and regulations is non-negotiable. Automated code audit plays a crucial role in ensuring compliance with frameworks such as SOC2 (Service Organization Control 2) and PCI DSS (Payment Card Industry Data Security Standard). automated code audit tools help organizations mitigate compliance-related risks and demonstrate a commitment to security and integrity through the identification and remediation of security vulnerabilities, maintenance of data privacy, and compliance with coding regulations.

Types of Automated Code Audit Tools

Automated code audit tools can be broadly categorized into two main types:

Dynamic Analysis Tools

Dynamic analysis tools, also known as runtime analysis tools, operate by examining code behavior during execution. These tools also try to provide insights into how the code behaves in real-world scenarios, identifying runtime errors, memory leaks, performance bottlenecks, error handling, etc. By analyzing code behavior dynamically, these tools can uncover issues that may not be apparent through static analysis alone and sometimes even impossible to find statically. Only when you execute code will you know its true nature, thus being able to uncover deep issues like memory leaks that can easily be overlooked by most tools and humans in complex software.

Running code dynamically can be a bit of a hassle and, therefore, is less popular in general than static analysis. Some applications or code blocks might need a specific state and input, and creating this situation might require a whole setup. For instance, an e-commerce server processing credit card information to charge the user.

The fascinating thing about dynamic code analysis is the fact that the tools examine the compiled code and not the source code and can infer many new things about it that even manual code reviews may overlook. In live environments, these tools can analyze code with real data, a scenario that may be unattainable in test settings.

Static Analysis Tools

On the other hand, static analysis tools analyze source code without executing it. These tools perform a comprehensive examination of the codebase, scrutinizing semantics, logic, flows, code patterns, and much more to identify potential issues and vulnerabilities. Therefore, they are a bit restricted compared to dynamic analysis. Combining them together may allow them to complement each other, though never perfectly. While automated code analysis tools reduce the need for manual labor, fixing the code errors the tool identifies requires human intervention Static analysis tools reduce the need for human effort further by automating checks for bugs or standard violations in the code. 

Feature Dynamic Analysis Tools Static Analysis Tools
Operating Principle Examines code behavior during execution Analyzes source code without execution
Focus Areas Runtime errors, memory leaks, performance Security vulnerabilities, coding issues, adherence to standards
Detection Mechanism Observes code behavior in real-world scenarios Scrutinizes syntax, semantics, code patterns
Advantages Detects issues not apparent through static analysis Identifies security vulnerabilities early in the development process
Limitations Limited scope of analysis, may miss certain issues Requires access to complete codebase for analysis

Popular Automated Code Analysis Tools

Several automated code analysis tools are widely used in the software development community:

Coverity is renowned for its accuracy and comprehensive defect detection capabilities. It excels in identifying critical issues such as security vulnerabilities, memory leaks, and concurrency defects. While Coverity offers robust features, its main downside is the price, which may be prohibitively expensive for small organizations. It’s important to note that larger companies may have the leverage to negotiate pricing based on enterprise licenses.

Valgrind is a dynamic analysis tool primarily used for memory debugging, memory leak detection, and profiling. It provides deep insights into memory-related issues and helps developers optimize code for performance and reliability. It is a bit outdated, and yet still considered a very great tool. Valgrind is open-source and freely available, making it accessible to developers across different organizations and budgets.

SonarQube is a widely adopted platform for continuous inspection of code quality, security vulnerabilities, and technical debt. It offers a comprehensive set of features, including static code analysis, code duplication detection, and security vulnerability scanning. While SonarQube provides value across the development lifecycle, its scalability and performance may vary depending on the size and complexity of the codebase.

Snyk is the company that brought up the whole software supply chain security and dependency monitoring to the market. Snyk is a security-focused platform that integrates with development workflows to identify and remediate vulnerabilities in open-source dependencies (code libraries and packages). It offers real-time monitoring, vulnerability prioritization, and automated fixes, enabling developers to proactively address security risks. While Snyk provides valuable security insights, its pricing model may pose challenges for smaller organizations with limited budgets.

GitLeaks is a specialized tool for scanning Git repositories to identify secrets and sensitive information inadvertently committed to version control. It helps mitigate the risk of data breaches by identifying and removing exposed credentials, API keys, and other confidential information that get forgotten in the source code. GitLeaks is open-source and freely available, making it a cost-effective solution for enhancing code security.

GitHub Advanced Security encompasses a suite of security features, including code scanning, secret scanning, and dependency analysis, integrated directly into the GitHub platform. It offers seamless integration with existing workflows, enabling developers to identify and remediate security vulnerabilities early in the development process. While GitHub Advanced Security provides valuable insights, its pricing structure may be prohibitive for smaller organizations.

  • ASAN (Address Sanitizer) and USBAN (Undefined Behavior Sanitizer)

ASAN and USBAN are some of the best low level runtime tools for detecting memory errors and undefined behavior in C and C++ code, respectively. They help identify potential security vulnerabilities, such as buffer overflows and use-after-free errors, by instrumenting code during execution. While these tools offer powerful debugging capabilities, they may require additional setup and configuration compared to integrated platforms like SonarQube or Snyk.

Key Features to Look for in an Automated Code Audit Tool

When selecting an automated code audit tool, consider the following key features:

False Positive Ratio

One of the most critical aspects of an automated code audit tool is its ability to minimize false positives. False positives occur when the tool incorrectly flags code as problematic, leading to wasted time and effort for developers to verify the results. A high false positive ratio can be detrimental to productivity and may erode trust in the tool's effectiveness. Look for tools that employ advanced algorithms and heuristics to accurately identify genuine issues while minimizing false positives.


The popularity of an automated code audit tool can provide valuable insights into its reliability, community support, and future development prospects. Tools with a large user base often benefit from extensive community contributions, frequent updates, and a robust ecosystem of plugins and integrations. Additionally, popular tools are more likely to undergo rigorous testing and refinement, ensuring they meet the evolving needs of developers and organizations.

Language and IDE Support

Ensure that the automated code audit tool supports the programming languages and Integrated Development Environments (IDEs) used in your projects. Compatibility with a wide range of programming languages ensures that the tool can be seamlessly integrated into your development workflow, regardless of the technologies employed. Similarly, IDE support enables developers to conduct code reviews directly within their preferred development environment, streamlining the review process and enhancing productivity.


Effective collaboration is essential for a successful code review process. Look for tools that facilitate seamless collaboration among team members, enabling them to share feedback, discuss issues, and coordinate efforts effectively. Features such as code review workflows, comment threads, and integration with collaboration platforms like Slack and Jira enhance transparency, accountability, and communication within development teams.


Visual representations of code quality metrics can provide valuable insights into the overall health and maintainability of your codebase. Look for automated code audit tools that offer visualization capabilities (and scoring too) such as graphs, charts, and heatmaps, to help identify trends, patterns, and areas for improvement. Visualizations make it easier for developers to understand complex code issues and prioritize their efforts accordingly.

Why Automated Code Audits are Not Enough

Automated code audits represent a significant advancement in software development, enhancing efficiency and security measures, but they may not cover all aspects of code analysis, leaving blind spots. To maximize effectiveness, complement automated audits with other methods, tools, and tests.

Dynamic Application Security Testing (DAST) tools, like profilers and vulnerability scanners, probe applications for vulnerabilities and performance issues in in-field scenarios (or as close as possible). Debuggers and profilers assist in identifying root causes of issues and performance bottlenecks that automated static analyzers may miss, providing a comprehensive understanding of application behavior.

Eventually, both static and dynamic code analysis will have blind spots in code, particularly with complex object-oriented programming (OOP) concepts, requiring manual inspections and peer reviews to ensure robust code quality standards.

Without proper code testing and QA, no software is bug-free and ready for production, and even then, there are no guarantees. It’s all about the process of reducing bugs before they reach production and raising code quality as long as shipping new code to production.

How Piiano Assists with Automated Code Analysis for Privacy

Piiano offers a comprehensive suite of solutions tailored to address the critical aspects of data security and privacy throughout the software development lifecycle (SDLC). This includes data protection APIs that allow developers to secure customer data while coding. Our Piiano Flows tool is designed to identify and mitigate risks associated with sensitive data handling by analyzing code repositories and tracking the flow of sensitive information and mapping data exposure.

With Piiano Flows, our privacy code scanner, developers gain unprecedented visibility into the usage and propagation of sensitive data within their codebase. By running as a command-line interface (CLI) tool, Piiano Flows empowers app-sec and DecSecOps teams to proactively identify potential data leaks and privacy policy violations, thereby averting costly war room scenarios once such buggy code reaches production. Unlike traditional automated code analysis tools, Piiano Flows focuses on detecting major sensitive data code-related changes, and ensuring that developers can effectively prioritize and address genuine security and privacy concerns.


Automated code audits play a critical role in modern software development. Manual reviews can be time-consuming, inconsistent, and limited by the availability of security experts. Automated code audits offer a wide range of advantages, including enhanced efficiency, scalability, and consistency. They provide immediate feedback, detect errors and vulnerabilities, and offer real-time integrations with CI/CD pipelines, preventing bottlenecks and allowing developers to maintain a consistent pace of work.

While automated tools may occasionally generate false positives and noise, their advantages far outweigh these drawbacks. To address the complexities of modern codebases and the increasing importance of data protection, solutions like Piiano provide data protection APIs for developers to safeguard customer data during coding. By leveraging automated code audits and integrating data protection measures, development teams can ensure the long-term success, maintainability, and security of their software projects. 

Share article

Powering Data Protection

Skip PCI compliance with our tokenization APIs

Skip PCI compliance with our tokenization APIs




It all begins with the cloud, where applications are accessible to everyone. Therefore, a user or an attacker makes no difference per se. Technically, encrypting all data at rest and in transit might seem like a comprehensive approach, but these methods are not enough anymore. For cloud hosted applications, data-at-rest encryption does not provide the coverage one might expect.

John Marcus

Senior Product Owner

const protectedForm = 
pvault.createProtectedForm(payment Div, 
Thank you! Your submission has been received!

We care about your data in our privacy policy

Oops! Something went wrong while submitting the form.