Automated Code Review for GitHub & BitBucket
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What is automatically and continuously tracks code quality with every GitHub or BitBucket commit and pull request, helping software developers save time in code reviews and efficiently tackle technical debt. is a tool in the Code Review category of a tech stack.

Who uses

10 companies reportedly use in their tech stacks, including STILLWATER SUPERCOMPUTING INC, SOFIT Software, and WineAdvisor.

18 developers on StackShare have stated that they use Integrations

Why developers like

Here鈥檚 a list of reasons why companies and developers use Reviews

Here are some stack decisions, common use cases and reviews by companies and developers who chose in their tech stack.

Kestas Barzdaitis
Kestas Barzdaitis
Entrepreneur & Engineer | 14 upvotes 88K views
Amazon EC2
Amazon EC2
Microsoft Azure
Microsoft Azure
Google Compute Engine
Google Compute Engine
AWS Lambda
AWS Lambda
Azure Functions
Azure Functions
Google Cloud Functions
Google Cloud Functions

CodeFactor being a #SAAS product, our goal was to run on a cloud-native infrastructure since day one. We wanted to stay product focused, rather than having to work on the infrastructure that supports the application. We needed a cloud-hosting provider that would be reliable, economical and most efficient for our product. aims to provide an automated and frictionless code review service for software developers. That requires agility, instant provisioning, autoscaling, security, availability and compliance management features. We looked at the top three #IAAS providers that take up the majority of market share: Amazon's Amazon EC2 , Microsoft's Microsoft Azure, and Google Compute Engine.

AWS has been available since 2006 and has developed the most extensive services ant tools variety at a massive scale. Azure and GCP are about half the AWS age, but also satisfied our technical requirements.

It is worth noting that even though all three providers support Docker containerization services, GCP has the most robust offering due to their investments in Kubernetes. Also, if you are a Microsoft shop, and develop in .NET - Visual Studio Azure shines at integration there and all your existing .NET code works seamlessly on Azure. All three providers have serverless computing offerings (AWS Lambda, Azure Functions, and Google Cloud Functions). Additionally, all three providers have machine learning tools, but GCP appears to be the most developer-friendly, intuitive and complete when it comes to #Machinelearning and #AI.

The prices between providers are competitive across the board. For our requirements, AWS would have been the most expensive, GCP the least expensive and Azure was in the middle. Plus, if you #Autoscale frequently with large deltas, note that Azure and GCP have per minute billing, where AWS bills you per hour. We also applied for the #Startup programs with all three providers, and this is where Azure shined. While AWS and GCP for startups would have covered us for about one year of infrastructure costs, Azure Sponsorship would cover about two years of CodeFactor's hosting costs. Moreover, Azure Team was terrific - I felt that they wanted to work with us where for AWS and GCP we were just another startup.

In summary, we were leaning towards GCP. GCP's advantages in containerization, automation toolset, #Devops mindset, and pricing were the driving factors there. Nevertheless, we could not say no to Azure's financial incentives and a strong sense of partnership and support throughout the process.

Bottom line is, IAAS offerings with AWS, Azure, and GCP are evolving fast. At CodeFactor, we aim to be platform agnostic where it is practical and retain the flexibility to cherry-pick the best products across providers.

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I use because my school team is really intense about being as professional as humanly possible before going into the industry. We find it is a great way to force people to think about the complexity of their functions and remember certain things we don't often think about (explicit single param functions!).

Unfortunately, we had to make it not a required check due to a large amount of false positives. There have been many pull requests where it will report reduced code quality because a function near the code that was changed was already poor quality (from before code factor was added). It's not really feasible or the responsibility of the person doing that specific pull request to fix what is likely someone else's mistakes.

It also has a problem with switch statements that are simply enum conversions. We often have a use for stringifying enums, but it will say there are "18+ code paths", which makes it a complex function. We're more than fine with this and the solution of using an unordered map isn't really elegant.

We've also gotten a fail because "Complex Code" and it just points to the whole file. Like...okay? What do you want from me? How do I help with this? "The file just kinda sucks". Thanks CodeFactor.

I would also really like the ability to ignore something that comes up during a pull request. Doing so doesn't make the code factor check pass. I'm not even sure if I have the option to do that.

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I use because it has helped me create a habbit of conforming to PEP8 when writing Python. It has drastically improved the overall quality of my code base on its way into production. Combined with other CI/CD tools, it is a great way of working in best practices into my development process.

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I use because it really does help my team to find problems in our code. Its a really valuable extra step in our review process that points out things that need to be simplified or code that is repeated from somewhere else in the project. It makes our reviews more efficient and can catches style problems that are tough to catch any other way (until they bite you).

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I use because I needed a code quality checker with easy setup with GitHub and low cost. CodeFactor solved this very nicely and for free :)

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I use because it's a great tool for teams that just starting their journey. provides 1 free private repo which is an amazing deal! I know what I'll be spending my money on as soon as we outgrow a single private repo.

See more's Features

  • Integrates with GitHub or BitBucket in seconds - nothing to install. Solo to Team - unlimited users for every repository. Continuously tracks code quality with every commit and pull request. Recognizes libraries in use and identifies potential code issues within. Checks complexity, duplication, churn, problems for code style, performance, etc
  • Provides real-time actionable feedback about potential quality issues as soon as they occur. Identifies hot-spots the most impactful list for refactoring. Private and Secure - encryption in transit and at rest. Free for Open Source and full time Students. Discount for non-profits. Alternatives & Comparisons

What are some alternatives to
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SonarQube provides an overview of the overall health of your source code and even more importantly, it highlights issues found on new code. With a Quality Gate set on your project, you will simply fix the Leak and start mechanically improving.
Code Climate
After each Git push, Code Climate analyzes your code for complexity, duplication, and common smells to determine changes in quality and surface technical debt hotspots.
Prettier is an opinionated code formatter. It enforces a consistent style by parsing your code and re-printing it with its own rules that take the maximum line length into account, wrapping code when necessary.
Codacy is an automated code review tool for Scala, Java, Ruby, JavaScript, PHP, Python, CoffeeScript and CSS. It's continuous static analysis without the hassle. Save time in Code Reviews. Tackle your technical debt
See all alternatives's Followers
40 developers follow to keep up with related blogs and decisions.
Munish Kapoor
Enis Necipo臒lu
Krishnan Subramanian
Dadi Atar
Romans Pokrovskis
Kestas Barzdaitis
Pedro Aguiar