What is SonarLint and what are its top alternatives?
SonarLint is a powerful code analysis tool that helps developers catch and fix code quality issues as they write code. It integrates with popular IDEs and provides real-time feedback on code quality, security vulnerabilities, and code smells. However, SonarLint has limitations in terms of advanced code analysis and customization options.
- SonarQube: SonarQube is an open-source platform for continuous inspection of code quality. It supports a wide range of programming languages and provides in-depth analysis of code issues. Pros include comprehensive code analysis and customizable rulesets, while cons include a more complex setup compared to SonarLint.
- Checkstyle: Checkstyle is a static code analysis tool that enforces a set of coding standards to ensure code quality and consistency. Key features include configurable rules, support for various programming languages, and integration with build tools. Pros include customizable rule sets, while cons include a steeper learning curve for beginners.
- PMD: PMD is an open-source static code analysis tool that detects and fixes common programming flaws. It supports multiple languages and provides detailed reports on code issues. Pros include a wide range of built-in rules, while cons include the lack of real-time feedback like SonarLint.
- FindBugs: FindBugs is a static analysis tool that detects potential bugs and security vulnerabilities in Java code. Key features include a wide range of bug patterns and integration with popular IDEs. Pros include specialized bug detection, while cons include limited language support compared to SonarLint.
- ESLint: ESLint is a popular linting tool for JavaScript that helps developers write cleaner and error-free code. It supports custom rules, plugins, and integrations with various development tools. Pros include extensive customization options, while cons include a focus on JavaScript only.
- PyLint: PyLint is a static code analysis tool for Python that checks for errors, style issues, and code smells. It offers customizable rules, integration with IDEs, and detailed reports on code quality. Pros include Python-specific checks, while cons include limited language support.
- CodeClimate: CodeClimate is a cloud-based static analysis tool that provides automated code reviews and helps identify code smells, security vulnerabilities, and maintainability issues. Pros include seamless integration with version control systems, while cons include a subscription-based pricing model.
- Coverity: Coverity is a static analysis tool that identifies defects and security vulnerabilities in code. It offers deep code analysis, scalability for large projects, and integration with popular build tools. Pros include advanced defect detection, while cons include a higher price point compared to SonarLint.
- Codacy: Codacy is an automated code review tool that helps developers improve code quality, security, and maintainability. It supports multiple programming languages, customizable code standards, and integration with popular tools. Pros include real-time feedback on code quality, while cons include limited customization options.
- Infer: Infer is a static analysis tool developed by Facebook that helps identify bugs in mobile and web apps. It offers fast and scalable error detection, support for popular languages, and integration with build systems. Pros include high accuracy bug detection, while cons include a more specialized focus compared to SonarLint.
Top Alternatives to SonarLint
- ReSharper
It is a popular developer productivity extension for Microsoft Visual Studio. It automates most of what can be automated in your coding routines. It finds compiler errors, runtime errors, redundancies, and code smells right as you type, suggesting intelligent corrections for them. ...
- SonarQube
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. ...
- FindBugs
It detects possible bugs in Java programs. Potential errors are classified in four ranks: scariest, scary, troubling and of concern. This is a hint to the developer about their possible impact or severity. ...
- PMD
It is a source code analyzer. It finds common programming flaws like unused variables, empty catch blocks, unnecessary object creation, and so forth. It includes CPD, the copy-paste-detector. ...
- JSLint
It is a static code analysis tool used in software development for checking if JavaScript source code complies with coding rules. It is provided primarily as a browser-based web application accessible through their domain, but there are also command-line adaptations. ...
- ESLint
A pluggable and configurable linter tool for identifying and reporting on patterns in JavaScript. Maintain your code quality with ease. ...
- Pylint
It is a Python static code analysis tool which looks for programming errors, helps enforcing a coding standard, sniffs for code smells and offers simple refactoring suggestions. ...
- Checkstyle
It is a development tool to help programmers write Java code that adheres to a coding standard. It automates the process of checking Java code to spare humans of this boring (but important) task. This makes it ideal for projects that want to enforce a coding standard. ...
SonarLint alternatives & related posts
- Refactor also using different code6
- Early discover bugs6
- IDE Integration5
- Highlighted //todo //bug4
- Spell checking3
- Visual studio become slower8
related ReSharper posts
JetBrains Rider massively improves production speed and value. It's hard to find a tool/IDE that is so well designed like Rider (especially for Unity).
The folks at ReSharper do an awesome job with resharper-unity
plugin. They even have a more in depth explanation of common issues in Unity than Unity itself.
I need to compile a comparative analysis of the differences between ReSharper and SonarQube features. Please share your experience/knowledge.
- Tracks code complexity and smell trends26
- IDE Integration16
- Complete code Review9
- Difficult to deploy2
- Sales process is long and unfriendly7
- Paid support is poor, techs arrogant and unhelpful7
- Does not integrate with Snyk1
related SonarQube posts
Our whole DevOps stack consists of the following tools:
- GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
- Respectively Git as revision control system
- SourceTree as Git GUI
- Visual Studio Code as IDE
- CircleCI for continuous integration (automatize development process)
- Prettier / TSLint / ESLint as code linter
- SonarQube as quality gate
- Docker as container management (incl. Docker Compose for multi-container application management)
- VirtualBox for operating system simulation tests
- Kubernetes as cluster management for docker containers
- Heroku for deploying in test environments
- nginx as web server (preferably used as facade server in production environment)
- SSLMate (using OpenSSL) for certificate management
- Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
- PostgreSQL as preferred database system
- Redis as preferred in-memory database/store (great for caching)
The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:
- Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
- Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
- Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
- Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
- Scalability: All-in-one framework for distributed systems.
- Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.
I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).
As per my work experience and knowledge, I have chosen the followings stacks to this mission.
UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.
Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.
Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.
Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.
Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.
Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.
Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.
Happy Coding! Suggestions are welcome! :)
Thanks, Ganesa
related FindBugs posts
We use PMD alongside Checkstyle and FindBugs (Spotbugs) for our static code analysis, as a standard stage in all of our pipelines. PMD offers us insight into various optimization possibilities, best-practice alignment, coding convention compliance and general problems with our code.
related PMD posts
We use PMD alongside Checkstyle and FindBugs (Spotbugs) for our static code analysis, as a standard stage in all of our pipelines. PMD offers us insight into various optimization possibilities, best-practice alignment, coding convention compliance and general problems with our code.
related JSLint posts
- Consistent javascript - opinions don't matter anymore8
- Free6
- IDE Integration6
- Customizable4
- Focuses code review on quality not style2
- Broad ecosystem of support & users2
related ESLint posts
Our whole DevOps stack consists of the following tools:
- GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
- Respectively Git as revision control system
- SourceTree as Git GUI
- Visual Studio Code as IDE
- CircleCI for continuous integration (automatize development process)
- Prettier / TSLint / ESLint as code linter
- SonarQube as quality gate
- Docker as container management (incl. Docker Compose for multi-container application management)
- VirtualBox for operating system simulation tests
- Kubernetes as cluster management for docker containers
- Heroku for deploying in test environments
- nginx as web server (preferably used as facade server in production environment)
- SSLMate (using OpenSSL) for certificate management
- Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
- PostgreSQL as preferred database system
- Redis as preferred in-memory database/store (great for caching)
The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:
- Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
- Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
- Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
- Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
- Scalability: All-in-one framework for distributed systems.
- Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
Our whole Vue.js frontend stack (incl. SSR) consists of the following tools:
- Nuxt.js consisting of Vue CLI, Vue Router, vuex, Webpack and Sass (Bundler for HTML5, CSS 3), Babel (Transpiler for JavaScript),
- Vue Styleguidist as our style guide and pool of developed Vue.js components
- Vuetify as Material Component Framework (for fast app development)
- TypeScript as programming language
- Apollo / GraphQL (incl. GraphiQL) for data access layer (https://apollo.vuejs.org/)
- ESLint, TSLint and Prettier for coding style and code analyzes
- Jest as testing framework
- Google Fonts and Font Awesome for typography and icon toolkit
- NativeScript-Vue for mobile development
The main reason we have chosen Vue.js over React and AngularJS is related to the following artifacts:
- Empowered HTML. Vue.js has many similar approaches with Angular. This helps to optimize HTML blocks handling with the use of different components.
- Detailed documentation. Vue.js has very good documentation which can fasten learning curve for developers.
- Adaptability. It provides a rapid switching period from other frameworks. It has similarities with Angular and React in terms of design and architecture.
- Awesome integration. Vue.js can be used for both building single-page applications and more difficult web interfaces of apps. Smaller interactive parts can be easily integrated into the existing infrastructure with no negative effect on the entire system.
- Large scaling. Vue.js can help to develop pretty large reusable templates.
- Tiny size. Vue.js weights around 20KB keeping its speed and flexibility. It allows reaching much better performance in comparison to other frameworks.
- Command Line3
- Spell Check strings & comments2
- Code score & directions2
- Pre-commit checks2
- FOSS2
- Standards2
- IDE Integration2
- Check both committed & Uncommitted code1
- Hints to improve code1
related Pylint posts
related Checkstyle posts
We use PMD alongside Checkstyle and FindBugs (Spotbugs) for our static code analysis, as a standard stage in all of our pipelines. PMD offers us insight into various optimization possibilities, best-practice alignment, coding convention compliance and general problems with our code.