Alternatives to FitNesse

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.

In 2012 we made the very difficult decision to entirely re-engineer our existing monolithic LAMP application from the ground up in order to address some growing concerns about it's long term viability as a platform.

Full application re-write is almost always never the answer, because of the risks involved. However the situation warranted drastic action as it was clear that the existing product was going to face severe scaling issues. We felt it better address these sooner rather than later and also take the opportunity to improve the international architecture and also to refactor the database in. order that it better matched the changes in core functionality.

PostgreSQL was chosen for its reputation as being solid ACID compliant database backend, it was available as an offering AWS RDS service which reduced the management overhead of us having to configure it ourselves. In order to reduce read load on the primary database we implemented an Elasticsearch layer for fast and scalable search operations. Synchronisation of these indexes was to be achieved through the use of Sidekiq's Redis based background workers on Amazon ElastiCache. Again the AWS solution here looked to be an easy way to keep our involvement in managing this part of the platform at a minimum. Allowing us to focus on our core business.

Rails ls was chosen for its ability to quickly get core functionality up and running, its MVC architecture and also its focus on Test Driven Development using RSpec and Selenium with Travis CI providing continual integration. We also liked Ruby for its terse, clean and elegant syntax. Though YMMV on that one!

Unicorn was chosen for its continual deployment and reputation as a reliable application server, nginx for its reputation as a fast and stable reverse-proxy. We also took advantage of the Amazon CloudFront CDN here to further improve performance by caching static assets globally.

We tried to strike a balance between having control over management and configuration of our core application with the convenience of being able to leverage AWS hosted services for ancillary functions (Amazon SES , Amazon SQS Amazon Route 53 all hosted securely inside Amazon VPC of course!).

Whilst there is some compromise here with potential vendor lock in, the tasks being performed by these ancillary services are no particularly specialised which should mitigate this risk. Furthermore we have already containerised the stack in our development using Docker environment, and looking to how best to bring this into production - potentially using Amazon EC2 Container Service

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.

I am a QA heading to a new company where they all generally use Visual Studio Code, my experience is with IntelliJ IDEA and PyCharm. The language they use is JavaScript and so I will be writing my test framework in javaScript so the devs can more easily write tests without context switching.

My 2 questions: Does VS Code have Cucumber Plugins allowing me to write behave tests? And more importantly, does VS Code have the same refactoring tools that IntelliJ IDEA has? I love that I have easy access to a range of tools that allow me to refactor and simplify my code, making code writing really easy.

We just launched the Segment Config API (try it out for yourself here) — a set of public REST APIs that enable you to manage your Segment configuration. A public API is only as good as its #documentation. For the API reference doc we are using Postman.

Postman is an “API development environment”. You download the desktop app, and build API requests by URL and payload. Over time you can build up a set of requests and organize them into a “Postman Collection”. You can generalize a collection with “collection variables”. This allows you to parameterize things like username, password and workspace_name so a user can fill their own values in before making an API call. This makes it possible to use Postman for one-off API tasks instead of writing code.

Then you can add Markdown content to the entire collection, a folder of related methods, and/or every API method to explain how the APIs work. You can publish a collection and easily share it with a URL.

This turns Postman from a personal #API utility to full-blown public interactive API documentation. The result is a great looking web page with all the API calls, docs and sample requests and responses in one place. Check out the results here.

Postman’s powers don’t end here. You can automate Postman with “test scripts” and have it periodically run a collection scripts as “monitors”. We now have #QA around all the APIs in public docs to make sure they are always correct

Along the way we tried other techniques for documenting APIs like ReadMe.io or Swagger UI. These required a lot of effort to customize.

Writing and maintaining a Postman collection takes some work, but the resulting documentation site, interactivity and API testing tools are well worth it.

Our whole Node.js backend stack consists of the following tools:

• Lerna as a tool for multi package and multi repository management
• npm as package manager
• NestJS as Node.js framework
• TypeScript as programming language
• ExpressJS as web server
• Swagger UI for visualizing and interacting with the API’s resources
• Postman as a tool for API development
• TypeORM as object relational mapping layer
• JSON Web Token for access token management

The main reason we have chosen Node.js over PHP is related to the following artifacts:

• Made for the web and widely in use: Node.js is a software platform for developing server-side network services. Well-known projects that rely on Node.js include the blogging software Ghost, the project management tool Trello and the operating system WebOS. Node.js requires the JavaScript runtime environment V8, which was specially developed by Google for the popular Chrome browser. This guarantees a very resource-saving architecture, which qualifies Node.js especially for the operation of a web server. Ryan Dahl, the developer of Node.js, released the first stable version on May 27, 2009. He developed Node.js out of dissatisfaction with the possibilities that JavaScript offered at the time. The basic functionality of Node.js has been mapped with JavaScript since the first version, which can be expanded with a large number of different modules. The current package managers (npm or Yarn) for Node.js know more than 1,000,000 of these modules.
• Fast server-side solutions: Node.js adopts the JavaScript "event-loop" to create non-blocking I/O applications that conveniently serve simultaneous events. With the standard available asynchronous processing within JavaScript/TypeScript, highly scalable, server-side solutions can be realized. The efficient use of the CPU and the RAM is maximized and more simultaneous requests can be processed than with conventional multi-thread servers.
• A language along the entire stack: Widely used frameworks such as React or AngularJS or Vue.js, which we prefer, are written in JavaScript/TypeScript. If Node.js is now used on the server side, you can use all the advantages of a uniform script language throughout the entire application development. The same language in the back- and frontend simplifies the maintenance of the application and also the coordination within the development team.
• Flexibility: Node.js sets very few strict dependencies, rules and guidelines and thus grants a high degree of flexibility in application development. There are no strict conventions so that the appropriate architecture, design structures, modules and features can be freely selected for the development.

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.

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.

I was building a personal project that I needed to store items in a real time database. I am more comfortable with my Frontend skills than my backend so I didn't want to spend time building out anything in Ruby or Go.

I stumbled on Firebase by #Google, and it was really all I needed. It had realtime data, an area for storing file uploads and best of all for the amount of data I needed it was free!

I built out my application using tools I was familiar with, React for the framework, Redux.js to manage my state across components, and styled-components for the styling.

Now as this was a project I was just working on in my free time for fun I didn't really want to pay for hosting. I did some research and I found Netlify. I had actually seen them at #ReactRally the year before and deployed a Gatsby site to Netlify already.

Netlify was very easy to setup and link to my GitHub account you select a repo and pretty much with very little configuration you have a live site that will deploy every time you push to master.

With the selection of these tools I was able to build out my application, connect it to a realtime database, and deploy to a live environment all with $0 spent.

If you're looking to build out a small app I suggest giving these tools a go as you can get your idea out into the real world for absolutely no cost.

Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.

Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!

Check Out My Architecture: CLICK ME

Check out the GitHub repo attached

Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

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.