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Auto Code Review: A Guide to Effective Code Reviews in 2024

Automated code review tools are critical to standardize and scale an organization’s software development efforts. They are complementary to manual code reviews. Since automated code reviews take care of the more typical software mistakes, human reviewers can focus on higher level code issues. Automated code reviews make the source code more efficient while also speeding up the review process.

What is automated code review?

Automated code review is the process of automatically reviewing a source code using a predefined set of rules to identify inefficient or suboptiomal code.

The automated code review tools are helping coders to find bugs  and to identify potential vulnerabilities. These tools usually display warning signs during the code review process to check if the code meets the standards of the company. An automated code review tool can automatically fix bugs or guides users on how to fix them.

Why is automated code review important now?

We explained why code reviews are important in our research on the topic. Automated code reviews automate parts of a code review. This is important since code reviews are completed by developers and their managers who make up one of the highest paid groups in a company by area of specialization.

How does it work?

Automated code review tools often work integrated with services like Github, Gitlab or Codecommit that host secure Git-based repositories. It analyzes the code as if compiling it and checks whether it meets the required standards. These standards vary depending on application of the software. Some issues that automated code review tools analyze are:

  • Code security
  • Error prone
  • Code style
  • Compatibility
  • Unused codes
  • Performance of the code.

Some of these software tools allow programmers to configure their own code standards. Some tools go beyond a rules based analysis of the code. For example, Amazon’s CodeGuru warns coders by detecting frequent mistakes and vulnerabilities using machine learning techniques.

How is automated code review different than code reviews?

Automated reviews save development team time and reduce release time however they are not a comprehensive review. Ideally, teams should combine manual and automated reviews for efficient and effective software development.

Manual code reviews involve a developers’ peers reviewing code manually in order to detect any possible vulnerabilities.For more details on manual code review, please see our article on the topic.

The main benefits of the automated code review over manual code review are time efficiency, lack of human error and lack of bias:

  • An automated code review tool can quickly identify errors, even while the developer is coding
  • Automated code review tools are not prone to manual errors like humans. They perform flawless rules based audits: If they are built to identify a well defined error, they would definitely identify it.
  • Automatic code reviews do not include any personal bias

On the other hand, relying on just automated code reviews do not guarantee finding all bugs or security defects. Some kinds of security vulnerabilities are difficult to find automatically, such as authentication problems, access control issues, insecure use of cryptography.

Can automated code reviews replace manual code reviews?

No. Manual code reviews reduce risky high level decisions such as the use of suboptimal architectures. They also support a collaborative culture and peer feedback.

While automated code reviews are better than having no code reviews, they are not a replacement for manual code reviews. However, they can make manual code reviews more efficient since they save human reviewers from looking for minor errors such as function naming, spacing or style.

What are the things to pay attention to while choosing automated code review solutions?

Automated code review is an integral part of modern software development best practices. The most important points to consider when choosing code review tools are:

  • Code language and IDE support: The most basic criterion is that the language in which the code is written is well served by the review tool. It is vital to learn if the tool to be selected supports
    • programming languages currently used by the team
    • programming languages that the team plans to use
  • Widely used: Popular tools tend to have fewer bugs, more responsive support and better documentation.
  • Cloud-hosted: Cloud-based support is important for different teams planning to collaborate. However, a cloud hosted system can bring security and connectivity problems. Pros and cons of a cloud solution need to be evaluated by the team.
  • Well documented and supported: Better documentation helps onboard new team members faster. Technical support would help developers as they master the automatic code review tools.
  • Static code analysis with an extensive set of rules: Predefined rules guide automated code reviews. It is helpful to have a wide range of rules in the auto code review tool.
  • Machine learning (ML) capabilities: Auto code review tools are moving beyond simple rules based approaches to using machine learning. A tool with ML capabilities is a more future proof solution.

What are automated code review companies?

Some automated code review companies are:

  • Amazon AWS CodeGuru
  • Codacy
  • Codebeat
  • CodeClimate
  • Codecov
  • CodeSonar
  • Coverity
  • Rubocop
  • Scrutinizer
  • SonarQube

If you believe your business can accelerate with auto code review, you can start comparing code review tools by going over our data driven lists.

If you have questions about how automated code reviews can support your tech function, we can help:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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