Alternatives to Jest

Needs advice on code coverage tool in Node.js/ExpressJS with External API Testing Framework

Hello community,

I have a web application with the backend developed using Node.js and Express.js. The backend server is in one directory, and I have a separate API testing framework, made using SuperTest, Mocha, and Chai, in another directory. The testing framework pings the API, retrieves responses, and performs validations.

I'm currently looking for a code coverage tool that can accurately measure the code coverage of my backend code when triggered by the API testing framework. I've tried using Istanbul and NYC with instrumented code, but the results are not as expected.

Could you please recommend a reliable code coverage tool or suggest an approach to effectively measure the code coverage of my Node.js/Express.js backend code in this setup?

I use both mocha and Jest because:

• I don't care whether teams use Jest or Mocha. But jest is way too overhyped. Most devs are writing integration tests and think that it's so much better but frankly I don't write integration tests as the way to get both design feedback and confidence when I code. I adhere to the test pyramid, not ice cream cone or the dumb "trophy"

• I TDD, so I only ever use the "API" of test frameworks. I don't do a lot of integration tests for TDD and all the bells and whistles Jest provides you from the command-line I just don't need. And I certainly do not care about or touch Jest Snapshots, I despise them

• My tests are fast enough because I write isolated tests with TDD, so I don't run into performance issues. Example: I write my tests in a way that I can run 300 tests in literally 1 second with mocha. So the Jest ability to pinpoint and only run those tests which are affected by code changes. I want to run all of them every time when I TDD. It's a different mindset when you TDD

• I also mainly code in IntelliJ or WebStorm because I feel the tools in that IDE far surpass VSCode and I also love running the test UI runner in it vs. lousy command-line

• I feel both mocha and Jest read just fine in terms of code readability. Jest might have shorter assertion syntax but I don't really care. I just care that I can read the damn test and my tests are written well and my test descriptions, as well as the code itself including constants represent business language, not technical. I care most about BDD, clean code, 4 rules of simple design, and SOLID

• I don't like using mock frameworks so no I don't use Jest's Mocking framework. I don't have to mock a lot in my tests due to the nature of how I strive to code...I keep my design simple and modular using principals such as clean code and 4 rules of simple design. If I must mock, I create very simple custom mocks with JS

• On the contrary to the belief that integration tests and mount are the way to go (this belief drives me absolutely crazy, especially Dodd's promoting that), I TDD with shallow & enzyme. My tests are simple. My design is driven by my tests and my tests give me quick and useful feedback. I have a course I'm working on coming out soon on TDD with React to show you how to truly test the FE and why the ice cream cone and trophy suck (you're being scammed people). Watch for that here: https://twitter.com/DaveSchinkel/status/1062267649235791873

Don't forget to upvote this post!

Mocha Jest JavaScript React @jsdom Enzyme #tdd #bdd #testdrivendevelopment

When you think about test automation, it’s crucial to make it everyone’s responsibility (not just QA Engineers'). We started with Selenium and Java, but with our platform revolving around Ruby, Elixir and JavaScript, QA Engineers were left alone to automate tests. Cypress was the answer, as we could switch to JS and simply involve more people from day one. There's a downside too, as it meant testing on Chrome only, but that was "good enough" for us + if really needed we can always cover some specific cases in a different way.

For our digital QA organization to support a complex hybrid monolith/microservice architecture, our team took on the lofty goal of building out a commonized UI test automation framework. One of the primary requisites included a technical minimalist threshold such that an engineer or analyst with fundamental knowledge of JavaScript could automate their tests with greater ease. Just to list a few: - Nightwatchjs - Selenium - Cucumber - GitHub - Go.CD - Docker - ExpressJS - React - PostgreSQL

With this structure, we're able to combine the automation efforts of each team member into a centralized repository while also providing new relevant metrics to business owners.

Recently I have been working on an open source stack to help people consolidate their personal health data in a single database so that AI and analytics apps can be run against it to find personalized treatments. We chose to go with a #containerized approach leveraging Docker #containers with a local development environment setup with Docker Compose and nginx for container routing. For the production environment we chose to pull code from GitHub and build/push images using Jenkins and using Kubernetes to deploy to Amazon EC2.

We also implemented a dashboard app to handle user authentication/authorization, as well as a custom SSO server that runs on Heroku which allows experts to easily visit more than one instance without having to login repeatedly. The #Backend was implemented using my favorite #Stack which consists of FeathersJS on top of Node.js and ExpressJS with PostgreSQL as the main database. The #Frontend was implemented using React, Redux.js, Semantic UI React and the FeathersJS client. Though testing was light on this project, we chose to use AVA as well as ESLint to keep the codebase clean and consistent.

We use Jest because when we rebooted our "front end" stack earlier last year, we need to have a testing solution (we didn't have any front-end tests before that!). Jest is fast and convenient and it has plenty of community support behind it. It let's us run our unit tests with Enzyme and snapshot tests.

This is an area that we are constantly reviewing to see what can be improved, both in terms of developer needs, accuracy, test maintainability, and coverage.

I'm currently exploring using React Storybook to be the record of snapshot tests and using some online services, such as Happo.io and Percy in our CI pipeline.

I use both mocha and Jest because:

• I don't care whether teams use Jest or Mocha. But jest is way too overhyped. Most devs are writing integration tests and think that it's so much better but frankly I don't write integration tests as the way to get both design feedback and confidence when I code. I adhere to the test pyramid, not ice cream cone or the dumb "trophy"

• I TDD, so I only ever use the "API" of test frameworks. I don't do a lot of integration tests for TDD and all the bells and whistles Jest provides you from the command-line I just don't need. And I certainly do not care about or touch Jest Snapshots, I despise them

• My tests are fast enough because I write isolated tests with TDD, so I don't run into performance issues. Example: I write my tests in a way that I can run 300 tests in literally 1 second with mocha. So the Jest ability to pinpoint and only run those tests which are affected by code changes. I want to run all of them every time when I TDD. It's a different mindset when you TDD

• I also mainly code in IntelliJ or WebStorm because I feel the tools in that IDE far surpass VSCode and I also love running the test UI runner in it vs. lousy command-line

• I feel both mocha and Jest read just fine in terms of code readability. Jest might have shorter assertion syntax but I don't really care. I just care that I can read the damn test and my tests are written well and my test descriptions, as well as the code itself including constants represent business language, not technical. I care most about BDD, clean code, 4 rules of simple design, and SOLID

• I don't like using mock frameworks so no I don't use Jest's Mocking framework. I don't have to mock a lot in my tests due to the nature of how I strive to code...I keep my design simple and modular using principals such as clean code and 4 rules of simple design. If I must mock, I create very simple custom mocks with JS

• On the contrary to the belief that integration tests and mount are the way to go (this belief drives me absolutely crazy, especially Dodd's promoting that), I TDD with shallow & enzyme. My tests are simple. My design is driven by my tests and my tests give me quick and useful feedback. I have a course I'm working on coming out soon on TDD with React to show you how to truly test the FE and why the ice cream cone and trophy suck (you're being scammed people). Watch for that here: https://twitter.com/DaveSchinkel/status/1062267649235791873

Don't forget to upvote this post!

Mocha Jest JavaScript React @jsdom Enzyme #tdd #bdd #testdrivendevelopment

For our internal team and collaboration panel we use Nuxt.js (with TypeScript that is transpiled into ES6), Webpack and npm. We enjoy the opinionated nature of Nuxt.js over vanilla Vue.js, as we would end up using all of the components Nuxt.js incorporates anyways and we can adhere to the conventions setup by the Nuxt.js project, which allows us to get better support in case we run into any dead ends. Webpack allows us to create reproducable builds and also debug our application with hot reloads, which greately increased the pace at which we are able to perform and test changes. We also incorporated a lot of testing (ESLint, Chai, Jasmine, Nightwatchjs) into our pipelines and can trigger those jobs through GitLab CI. All packages are fetched through npm, so that we can keep our git repositories slim and are notified of new updates aswell as reported security flaws.

Protractor or Cypress for ionic-angular?

We have a huge ionic-angular app with almost 100 pages and 10+ injectables. There are no tests written yet. Before we start, we need some suggestions about the framework. Would you suggest Cypress or Angular's Protractor with Jasmine / Karma for a heavy ionic app with Angular?

When you think about test automation, it’s crucial to make it everyone’s responsibility (not just QA Engineers'). We started with Selenium and Java, but with our platform revolving around Ruby, Elixir and JavaScript, QA Engineers were left alone to automate tests. Cypress was the answer, as we could switch to JS and simply involve more people from day one. There's a downside too, as it meant testing on Chrome only, but that was "good enough" for us + if really needed we can always cover some specific cases in a different way.

We are in the process of adopting Next.js as our React framework and using Storybook to help build our React components in isolation. This new part of our frontend is written in TypeScript, and we use Emotion for CSS/styling. For delivering data, we use GraphQL and Apollo. Jest, Percy, and Cypress are used for testing.

Needs advice on code coverage tool in Node.js/ExpressJS with External API Testing Framework

Hello community,

I have a web application with the backend developed using Node.js and Express.js. The backend server is in one directory, and I have a separate API testing framework, made using SuperTest, Mocha, and Chai, in another directory. The testing framework pings the API, retrieves responses, and performs validations.

I'm currently looking for a code coverage tool that can accurately measure the code coverage of my backend code when triggered by the API testing framework. I've tried using Istanbul and NYC with instrumented code, but the results are not as expected.

Could you please recommend a reliable code coverage tool or suggest an approach to effectively measure the code coverage of my Node.js/Express.js backend code in this setup?

For our internal team and collaboration panel we use Nuxt.js (with TypeScript that is transpiled into ES6), Webpack and npm. We enjoy the opinionated nature of Nuxt.js over vanilla Vue.js, as we would end up using all of the components Nuxt.js incorporates anyways and we can adhere to the conventions setup by the Nuxt.js project, which allows us to get better support in case we run into any dead ends. Webpack allows us to create reproducable builds and also debug our application with hot reloads, which greately increased the pace at which we are able to perform and test changes. We also incorporated a lot of testing (ESLint, Chai, Jasmine, Nightwatchjs) into our pipelines and can trigger those jobs through GitLab CI. All packages are fetched through npm, so that we can keep our git repositories slim and are notified of new updates aswell as reported security flaws.

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.

Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.