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  1. Stackups
  2. DevOps
  3. Code Coverage
  4. Code Coverage
  5. Coveralls vs SonarQube

Coveralls vs SonarQube

OverviewDecisionsComparisonAlternatives

Overview

Coveralls
Coveralls
Stacks1.7K
Followers278
Votes68
SonarQube
SonarQube
Stacks1.9K
Followers2.0K
Votes53
GitHub Stars10.0K
Forks2.1K

Coveralls vs SonarQube: What are the differences?

Introduction

In this document, we will discuss the key differences between Coveralls and SonarQube, two popular tools used for code quality analysis and evaluation. Both tools are designed to analyze code, identify vulnerabilities, and provide insights to improve code quality and maintainability. However, there are significant differences in their features and functionalities that set them apart.

  1. Scalability and Language Support: One of the primary differences between Coveralls and SonarQube is their scalability and language support. SonarQube is a highly scalable tool that supports a wide range of programming languages, including Java, C#, Python, JavaScript, and more. On the other hand, Coveralls has limited language support and primarily focuses on providing coverage analysis for Ruby, Python, and JavaScript.

  2. Static Code Analysis vs. Test Coverage Analysis: Another key difference lies in the primary focus and use case of each tool. SonarQube is primarily a static code analysis tool that examines the source code to identify issues such as code smells, bugs, vulnerabilities, and code duplications. It provides a comprehensive analysis of the codebase, allowing developers to assess and improve the overall quality of the code. In contrast, Coveralls is primarily a test coverage analysis tool that helps developers measure and track the code coverage of their tests to ensure adequate test coverage and identify areas without test coverage.

  3. Integration and Plugin Ecosystem: SonarQube offers a rich ecosystem of plugins and integrations, allowing users to extend its functionality and integrate it with various tools and services. It seamlessly integrates with popular build tools like Maven and Jenkins, issue trackers like JIRA, and various source code management systems. Coveralls, on the other hand, has a more limited integration and plugin ecosystem compared to SonarQube.

  4. Reporting and Visualization: SonarQube provides a comprehensive and customizable dashboard that offers in-depth reports, metrics, and visualizations for code quality, vulnerabilities, and other analysis results. It provides developers and teams with a clear overview of the codebase's health and progress over time. In contrast, Coveralls focuses more on providing coverage reports and may not offer the same level of comprehensive reporting and visualization capabilities as SonarQube.

  5. Commercial vs. Open Source: SonarQube is available in both open-source and commercial versions, offering different levels of features and support. The commercial version provides additional enterprise-level capabilities and support options. Coveralls, on the other hand, is a commercial tool that offers plans at different price points based on the desired features and usage.

  6. Community and Support: SonarQube has a larger and more active community compared to Coveralls. This larger community translates into better support, more frequent updates, and access to a wealth of community-generated plugins, extensions, and resources. Coveralls, being a smaller and more niche tool, may have a relatively smaller community and support network.

In summary, while both Coveralls and SonarQube aim to improve code quality, they differ significantly in terms of their scalability, language support, primary focus, integration ecosystem, reporting capabilities, commercial/open-source availability, and community support. These differences should be considered when choosing the most suitable tool for your code quality analysis requirements.

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Advice on Coveralls, SonarQube

Felipe
Felipe

May 24, 2020

Needs advice

My website is brand new and one of the few requirements of testings I had to implement was code coverage. Never though it was so hard to implement using a #docker container.
Given my lack of experience, every attempt I tried on making a simple code coverage test using the 4 combinations of #TravisCI, #CircleCi with #Coveralls, #Codecov I failed. The main problem was I was generating the .coverage file within the docker container and couldn't access it with #TravisCi or #CircleCi, every attempt to solve this problem seems to be very hacky and this was not the kind of complexity I want to introduce to my newborn website.
This problem was solved using a specific action for #GitHubActions, it was a 3 line solution I had to put in my github workflow file and I was able to access the .coverage file from my docker container and get the coverage report with #Codecov.

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Comments

Detailed Comparison

Coveralls
Coveralls
SonarQube
SonarQube

Coveralls works with your CI server and sifts through your coverage data to find issues you didn't even know you had before they become a problem. Free for open source, pro accounts for private repos, instant sign up with GitHub OAuth.

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.

Repository Coverage Statistics;Individual File Coverage Reports;Line By Line Coverage;Repository Overview
Multi-language;Detect tricky issues;Security analysis;Enhance your workflow
Statistics
GitHub Stars
-
GitHub Stars
10.0K
GitHub Forks
-
GitHub Forks
2.1K
Stacks
1.7K
Stacks
1.9K
Followers
278
Followers
2.0K
Votes
68
Votes
53
Pros & Cons
Pros
  • 45
    Free for public repositories
  • 13
    Code coverage
  • 7
    Ease of integration
  • 2
    More stable than Codecov
  • 1
    Combines coverage from multiple/parallel test runs
Pros
  • 26
    Tracks code complexity and smell trends
  • 16
    IDE Integration
  • 9
    Complete code Review
  • 2
    Difficult to deploy
Cons
  • 7
    Paid support is poor, techs arrogant and unhelpful
  • 7
    Sales process is long and unfriendly
  • 1
    Does not integrate with Snyk
Integrations
Travis CI
Travis CI
CircleCI
CircleCI
Semaphore
Semaphore
Jenkins
Jenkins
Codeship
Codeship
Gradle
Gradle
Apache Maven
Apache Maven
Jenkins
Jenkins
TeamCity
TeamCity
Appveyor
Appveyor
Travis CI
Travis CI
Apache Ant
Apache Ant
Bamboo
Bamboo

What are some alternatives to Coveralls, SonarQube?

Code Climate

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.

Codacy

Codacy

Codacy automates code reviews and monitors code quality on every commit and pull request on more than 40 programming languages reporting back the impact of every commit or PR, issues concerning code style, best practices and security.

Phabricator

Phabricator

Phabricator is a collection of open source web applications that help software companies build better software.

Codecov

Codecov

Our patrons rave about our elegant coverage reports, integrated pull request comments, interactive commit graphs, our Chrome plugin and security.

PullReview

PullReview

PullReview helps Ruby and Rails developers to develop new features cleanly, on-time, and with confidence by automatically reviewing their code.

Gerrit Code Review

Gerrit Code Review

Gerrit is a self-hosted pre-commit code review tool. It serves as a Git hosting server with option to comment incoming changes. It is highly configurable and extensible with default guarding policies, webhooks, project access control and more.

RuboCop

RuboCop

RuboCop is a Ruby static code analyzer. Out of the box it will enforce many of the guidelines outlined in the community Ruby Style Guide.

CodeFactor.io

CodeFactor.io

CodeFactor.io 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.

ESLint

ESLint

A pluggable and configurable linter tool for identifying and reporting on patterns in JavaScript. Maintain your code quality with ease.

Amazon CodeGuru

Amazon CodeGuru

It is a machine learning service for automated code reviews and application performance recommendations. It helps you find the most expensive lines of code that hurt application performance and keep you up all night troubleshooting, then gives you specific recommendations to fix or improve your code.

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