Testsigma is a SaaS, AI-Driven test automation software for Web and Mobile applications to achieve continuous testing with Shift-left approach

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Why Visual Testing Is Essential for Applications With 3D Engines When testing applications with embedded 3D engines or any other related to generating and processing computer graphics, regular functionality is not sufficient. It is also necessary to verify the visual appearance of the application, providing a thorough evaluation of its design and layout. For instance, when working on a game written in Unity 3D, Quality Assurance is expected to assess all game levels’ logic, to ensure the absence of contradictions and unexpected behavior. However, this all does not touch how 3D characters and environments are displayed, whether the lights are reflected as they are expected, or if scaling objects retain these values on restarting the level. Indeed, lots of scenarios that cannot be covered by regular testing. All that has been described above is an example of what Visual testing is capable of handling. But why cannot we simply keep an eye on the visual aspects of such applications instead of spending much more time covering all these scenarios? Well, it’s a really good point. Considering a bunch of factors like high costs of development, maintenance, and additional runs, it is possible to assume it’s not worth the trouble. However, putting it all on humans can also invoke a number of problems: Depending on the set failed threshold, the automation script will compare the actual image with the reference one pixel by pixel – a task hardly feasible for a human. Human error: Since the application is tested by multiple individuals, the visual testing results can vary due to the peculiarities of the tester’s eyesight and visual perception of one or another asset, etc. Inconsistent results: Just like in regular manual testing, automation scenarios allow us to reduce the time for regression checks and focus on other potential scenarios. Repetitive regression scenarios: These costs can often surpass the price of automation and subsequent maintenance. Costs of manual testing: What Visual Testing Ensures Entire Interface page (All UI elements, their position, scale, color, etc.) Visual 2D/3D assets in terms of geometry and polygonal for 3D and vector structure for 2D Lights and Shadows (The effect that gives different sources of light to an object) Materials (Rendering materials on objects in the current scene) Specific shaders used for rendering All these issues are better to be not fully replaced but at least be complemented by exact calculations of different percentages. How to Test Web Applications First, you will need to configure a Cypress project. Cypress is a multi-purpose automation framework that provides all the necessary tools to cover automation scenarios, including visual testing, which is our agenda for today. Undoubtedly, there are many other tools that support visual testing, but covering all of them is beyond the scope of this article. Installing Cypress To install Cypress and configure your first project, I recommend looking through this article: https://learn.cypress.io/testing-your-first-application/installing-cypress-and-writing-your-first-test Now, we are good to go. Installing Cypress Plugin for Comparing Snapshots Install a cypress-image-snapshot plugin according to these instructions: . https://www.npmjs.com/package/cypress-image-snapshot Adding a Custom Command Compared to what the article says, let’s add a custom command to the ‘commands.js’ file, or you can do it the same way I did – create a separate file dedicated only to your commands. commands.js Cypress.Commands.add('compareSnapshotWithBaseImage', (options) => {
  const nameDir = options?.nameDir;
  const element = options?.element;
  const fullName = nameDir.replace('cypress/e2e/', '');
    return cy
      .get(element)
      .matchImageSnapshot(fullName);
}); Here, I’ve added a custom command . When executed for the first time, it creates a reference image with a set name. During the subsequent runs, it compares the actual image with previous captures, marks it as a reference, and gives the difference image as an output if they differ more than the allowed failed threshold. compareSnapshotWithBaseImage This command operates on a particular object passed with the parameter. options Picking Locators It’s important to pass a locator of the object but not the object itself. In my case, returns the following string: . Just to remind you, the locator can be pulled out from the (Firefox) and (Chrome) tab of the Dev console (F12). canvas.getCanvasSelector() ‘canvas#3DCanvas’ Inspector Elements When you click on the mouse cursor in the upper left corner, you are in the picking element mode. Move the cursor over the targeted element (in my case, it is a ), and click on it. 3D canvas Here is an example of the locator found in the dev console: To let Cypress know about this element, there are two options: We need to identify the type of element (canvas). Use the hashtag sign ( ) to concatenate a string with the element’s id ( ). The result string is . # 3DCanvas ‘canvas#3DCanvas’ Alternatively, you can simply use the element’s id preceded by a dot . ‘.3DCanvas’ Calling a Comparison Function Therefore, I can invoke the comparison function from any part of the code using the following syntax: And('I compare 3D scene with the reference image', () => {
  const specName = Cypress.spec.relative + '/';
  cy.wait(2000);
    cy.compareSnapshotWithBaseImage({
      nameDir: specName + 'canvas_img',
      element: canvas.getCanvasSelector(),
    });
}); Testing Results Once this function is called, the first run of this test always passes as Cypress cannot find a file with a specified name and path and creates a new one. Here is what I have after my first run: An actual image of a 3D scene with all its controls and a model of a motorcycle is captured as a reference one. This is a scenario where I am checking how changing directional light intensity affects the appearance of the 3D motorcycle. As you can see, I have multiple sources of light in the scene, so the next run will ensure that changing the directional light of the entire scene does not affect these point lights. A combination of these verifications is also possible and saves builds running time. For example, you can combine changing lights and setting the object’s transformations (rotation, position, scale). Do not combine too many features into one as it makes debugging more difficult and running tests less effective if they fail, let’s say, on changing lights verification, you’ll never know how settings transformations work until lights are fixed. Now, let’s emulate the situation when setting the light intensity does not properly work. To demonstrate this ‘bug’, I changed the value of light intensity from 1.2 to 0.6 to get a darker image of the motorcycle. However, we remember that Cypress will compare with the reference image captured above, a lighter image. Let’s see what we have after this test run. The lighter image is a reference one, the image is the actual image captured in the latest run. The image is a diff image. It shows what exact pixels are different in these two images. left right middle If you manually compare the left and right images, you can certainly see the difference. However, this difference is quite subtle. It is likely that during manual testing such broken lights will be overlooked, whereas automated tests will catch them. How to Test Desktop Applications General Information About Squish Framework For desktop applications, I prefer to use the framework by Squish Froglogic company. According to the official documentation, one fundamental aspect of Squish's approach is that the AUT and the test script that exercises it are always executed in two separate processes. This ensures that even if the AUT crashes, it should not crash Squish. Squish runs a small server, , that handles the communication between the AUT and the test script. squishserver The test script is executed by the tool, which in turn connects to squishserver. squishserver starts the instrumented AUT on the device, which starts the Squish hook. squishrunner With the hook in place, squishserver can query AUT objects regarding their state and can execute commands on behalf of squishrunner. squishrunner directs the AUT to perform whatever actions the test script specifies. All the communication takes place using network sockets which means that everything can be done on a single machine, or the test script can be executed on one machine and the AUT can be tested over the network on another machine. The following diagram illustrates how the individual Squish tools work together. A step-by-step guide on how to create screenshots verifications can be found here: , while I would like to focus more on the interface of as it looks very informative. https://doc.qt.io/squish/how-to-create-and-use-verification-points.html#how-to-do-screenshot-verifications screenshots comparison Working with Verification Point Viewer I ran a test on 3D desktop software where my 3D model was moved along Y-axis. So, my screenshots verifications failed. Let’s open the Verification point and see the difference. To do this, find the line about Verification point failure on the tab. Right-click on it, and select the option. Test Result View Differences The first tab comprises all the available options for analyzing screenshots. Differences actual and reference images are being shown alternately, displaying the difference with a red outline. Flicker: all common pixels are removed, and only the different ones are shown. The way the subtraction works can be influenced by changing the color settings, and by checking or unchecking the Invert checkbox. Subtract: HSV (Hue, Saturation, Value) : works pretty much the same as the Subtract mode, but instead of highlighting all pixel differences in color, it focuses on differences in . This means that it considers variations in shades of gray rather than color differences. Gray Diff grayscale Absolutely identical pixels are green, the areas that differ between actual and reference images are red. Red/Green Diff: The mode features a draggable slider that displays the reference image when dragged to the right and the actual image when dragged to the left (as shown in the image above). Split View: Besides the visual representation of screenshot differences, there is one more tab of the Verification point viewer called . The Comparison mode is crucial for specifying how the actual application behavior should be compared to the expected behavior. Comparison Mode It defines the criteria for determining whether the verification point passes or fails and what is the acceptable failure threshold. Several other options and settings are available for the Comparison mode, but I would like to talk more about color histograms. Here you can see the for my 3D test model. Histogram It is pretty useful for cases when the color profile didn’t change significantly, but the object was rotated, scaled, or transformed, exactly like what happened to my object when I moved it along Y-axis. How it works: Divides the color range (0-255) of each color component (RGB) of every pixel by the number of (or baskets) and calculates the number of pixels in each bin. Bins Divides the total number of pixels in the image by the number of pixels in each bin to get . These respective values are put back into the corresponding bins. normalized values The values of all corresponding bins are subtracted from one another and the resulting values are summed up. This value represents the difference between the reference and actual images. This mode lets you configure the number of and , which represents the maximum difference between two images for which they are still considered to be "equal." The interface of setting the number of Bins and Allowed failures is shown in the image: Bins Allowed failures Conclusion Long story short, I hope I helped you understand that visual testing plays a significant role in testing applications with visual assets, such as design platforms, games, game development engines, 3D modeling, and engineering software. Automating the detection of most visual defects can mitigate the impact of the 'human factor' and facilitate further analysis. Resources: https://testsigma.com/guides/visual-testing/#What_is_Visual_Testing https://www.coderskitchen.com/visual-testing-of-a-unity-based-3d-world/ https://learn.cypress.io/testing-your-first-application/installing-cypress-and-writing-your-first-test https://dev.to/bornfightcompany/cypress-tests-in-bdd-style-52n5 https://doc.qt.io/squish/screenshot-verification-point-dialog.html Header image: Image by pikisuperstar on Freepik

3D Engines: A Comprehensive Guide to Automation

Software deployment pillars the software development life cycle (SDLC). It links users and developers, making it possible to gather and use feedback fast. This feedback loop raises software's quality, usability, and scaling propensities. However, software deployment is challenging and requires careful planning and strategy. As a result, the deployment process's design has to be carefully considered. The effectiveness of the planning phase may determine whether a deployment is successful or unsuccessful. ”Your solution doesn’t work till others can use/tinker with it. Deployment enables that.” In this article, I delve into the specifics of the planning stage of software deployment and provide an accurate walkthrough of everything that must be established. Although there are many different deployment methodologies, the DevOps methodology is the focus of this article. What Is Software Deployment? Software deployment is the method by which applications, modules, updates, and fixes are distributed from developers to target environments. Pushing software into target environments entails actions that increase functionality, such as system management and operations. Deployment is the anti-penultimate stage of the SDLC. Software production is frequently confused with software deployment. Although used synonymously, they have different meanings. Software production is more comprehensive; it involves several activities, including code and build development and deployment. Software Deployment Activities Software deployment is an amalgam of numerous activities that can be summed into five major headings: Planning Deployment: Adequate planning is the primary driver of successful software deployment. This article explains that. Verifying service components Verifying target environments: Depending on your operating system and the size of your project, there are different ways to verify target environments. No matter the operating system, you must configure and create a target connection between your local machine and your target environment before deploying the software package to the target to carry out your planned functions. For instance, on Linux, you can configure and set up a target connection using a real-world NFS target networking environment, an emulated target environment, or a flash development environment. To ensure that the software functions correctly in the target environment, dependencies are also installed during this phase. Executing deployment: For DevOps, this is the phase of implementation of DevOps’ principles like all-around automation, CI/CD, etc. Confirming deployment: More than deployment is required. Confirmation is essential to ensure that the software package has been successfully deployed. Planning for Software Deployment Adequate planning facilitates the convergence of all parties involved in the software deployment process into a single source of truth and improves communication. Good planning has implementation-based advantages as well as corporate or organizational advantages. In this article, I divided the planning process into two main functions: managerial and engineering activities. Engineering Activities The engineering team generated the code, versioned it in the most practical format, and prepared the package for distribution because they are the software's backbone. The technical department must cater to six components: frontend, backend, monitoring, CI/CD, data storage, and security. Prepare your software package for deployment by doing the following: Frontend Minify CSS and JavaScript files, and compress images to reduce the number and size of files your software has to load when deployed. Configure a content delivery network (CDN) to reduce network latency. When a user initially requests access to your software, a CDN server guarantees that the files are transferred from the origin server, a copy of the assets is cached on the edge server, and the data is reused for subsequent requests. Users may get your application more quickly in this fashion, and your deployment host won't become overloaded with requests. of websites use . 79.9% Cloudflare Configure cache busting by versioning your files when they’re updated. Cache busting guarantees that the most recent updates to your software are generated and immediately made publicly available. Three techniques exist for performing cache busting: File name versioning (e.g., style.v2.css) File path versioning (e.g., /v2/style.css) Query strings (e.g., style.css?ver=2) While the first two techniques are hitch-free, cache busting using a query string may present a number of issues, especially when used with a CDN. Backend On the backend, ensure the following: To ensure that your software has sufficient resources, configure your virtual machines, HDD, CPU, and RAM. If your software is running on EC2, ensure to build an Amazon machine image (AMI). An AMI enables you to create virtual servers — e.g., EC2 instances — inside the AWS infra. You can easily create an AMI from an EC2 instance, register the machine image, and search for it when you need to launch new instances. If containerization is employed, prepare and push your images to the container registry. You can simply do this using the `$ docker push` command. The docker documentation portal contains comprehensive details on how to push a new docker image to a docker registry. If your software is packaged as microservices, configure a container orchestrator. is one of the best known, with comprehensive community support. Kubernetes Data Storage Set up relational databases. has of the relative market share in database management tools, and you can learn how to set up a MySQL database schema . MySQL 44.04% here Set up queue systems to reduce the server and data volume. Run schema migration. Schema migration auto-updates your database scheme per every change in your codebase. Set up file storage: Where will files generated during app usage be stored? Configure tools, like , and , to store these files. Amazon S3 Cloudinary Security For the security of your software, To encrypt your server connection, obtain and configure SSL certificates. Configure static code analyzers to uncover security risks in your codebase or dependencies. CI/CD CI/CD is the cornerstone of the DevOps methodology. Ensure the following: Create a deployment pipeline using a version control system (VCS) like on your remote system. Git Write test scripts and run automated tests. The cornerstone of DevOps is automated testing, and technologies like Lambdatest, TestSigma, and others that are available right out of the box let you execute automated tests in parallel across several environments. There are three ways to construct a test script: using data scripting, using a programming language, or using the record/playback approach. Scripts often include a set of instructions that detail the software functionalities that must be tested as well as the direction in which you want the automated testing to go. Set up static code analyzers. Based on pre-established principles, static code analysis tools analyze your code, format, and lint. This makes it possible to guarantee that the code adheres to current standards. Set up deployment environments. Depending on the purpose of your deployment, you could either set up an ephemeral or permanent environment. Aptible’s hosting platform automates the work of provisioning, managing, and scaling infrastructure, so I recommend Aptible here. Use a load balancer to create a zero-downtime deployment strategy. A load balancer serves as the "traffic cop" in front of your servers, distributing client requests across all servers equipped to handle them in a way that maximizes speed and capacity utilization. The load balancer routes traffic to the active servers in case one server goes offline. The load balancer initiates requests to a new server when it is added to the server group. By distributing requests only to servers that are active, a load balancer assures high availability and dependability and offers the flexibility to add or remove servers as needed. Your future scaling problems are made easier by this. Monitoring Set up log management systems to gain first-hand access to information useful in improving the deployment process. Gathering and utilizing the reports generated by the log management systems is a core component of observability, which makes your pipeline more intelligent and fool-proof. Using tools like or InfluxDB, you can monitor your processes and get error reports in real-time. The P portal has robust information on how to set up its alert management and log monitoring solutions. Prometheus rometheus documentation Managerial Functions Along with the engineering operations, a number of other essential activities include: : Team members should be familiar with both successful and unsuccessful deployment processes before deployment. The modern implementation of the DevOps deployment methodology requires that team members track the performance of the deployed software using different KPIs. Setting key performance indicators (KPIs) These KPIs enable the team to assess the progress or failure of the deployment. However, despite their importance, only a few teams monitor their deployment through KPIs. These KPIs include deployment frequency, failed deployment rate, change volume, memory use, CPU utilization, downtime during deployment, page load time, mean time to recovery (MTTR), error frequency, and many others. : Release notes serve as documentation for prospective future use and deployment explanations. Isolating unexpected problems helps to better identify them. Each deployment should be versioned to keep errors transparent and manageable. Preparing a release versioning and release notes process : Even with meticulous planning and precise automation, unforeseen events might still cripple the deployment operation. If there is a problem with the deployment process, the rollback system enables retrieval of an earlier version. Imagine it as an airbag; the ability to revert to a previous release will be crucial if the current deployment fails. Preparing a rollback system : The deployment must be carried out step-by-step and under control, allowing you to decide whether to move forward at each checkpoint. Therefore, activities set to our current checkpoint can be worked on using resources, but tasks that come after the checkpoint cannot. Hence, the next set of functions is released when the checkpoint has been passed OK. Below is an example of a task list: Preparing a task list Conclusion The success of software deployment depends on how well the planning stage, which comes before actual deployment, is executed. Planning, therefore, requires a lot of thought and attention to detail. The intricate details of the software deployment planning phase have been covered in full in this article. You are prepared to deploy if you have it in your arsenal!

The Software Deployment Checklist You've Never Seen

Continuous Performance Testing Is Recognized Worldwide as Reasonable and Essential to DevOps and Your CI/CD Process. With this approach, you will forget the days of the waterfall approach when you had to wait for performance till the conclusion of your release cycle. As more alterations are made, isolating the primary problem becomes more challenging, and every fix may result in further QA cycles. If performance is poor at that moment, it will probably change the entire release schedule. Teams must learn how to speed up software releases while continually ensuring they don't introduce performance concerns into the production cycle to overcome the challenges. What Should You Know About Continuous Testing? is one of the most crucial DevOps strategies. Functional testing should start with unit and integration tests as soon as possible. But performing non-functional testing is also crucial. “Test early, test frequently” Therefore, you need to conduct performance tests. You no longer have the luxury of delaying performance testing until all features are built as markets get more saturated every day. More teams realize the value of continuous testing, which has increased the popularity of continuous performance testing. Continuous testing on every code push eliminates the need for manual performance tests, which are time-consuming and expensive. This post will define continuous performance testing, the benefits of continuous testing, and outline the tools you’ll need to implement it on your team. What Is Continuous Performance Testing? Continuous performance testing refers to continually monitoring an application’s performance while the load on that application is increased. Manually monitoring and performance testing in a testing environment is an option for teams. Still, this strategy is only feasible for modest-sized systems and on a smaller scale. Performance testing is commonly utilized on large releases. Still, only a small percentage of teams incorporate it into their CI/CD pipeline as part of their DevOps process. However, continuous performance testing during the project’s development phase is strongly advised. Doing so requires a new set of tools to execute and scale automatically. When most developers hear the phrase “performance testing,” they envision the following processes taken in the later phases of application development: You start by listing all the crucial components that you wish to test. After that, you write test scripts for performance testing in weeks. You conduct the tests and then examine several pages of performance test results. Performance testing’s presumption above was true in the past when companies used the waterfall development method for most applications. However, times have certainly changed. Nowadays, most people no longer use the waterfall method. Instead, as agile software development has increased, the requirement for testing has changed. In addition, as more teams adopt DevOps techniques to create and distribute their apps, testing is now an integral element of the development process. Early feedback from automated functional tests is crucial for improving code quality. Teams often employ automated testing to ensure that the application meets the functional requirements. However, looking at the non-functional criteria or system indicators like security, scalability, and performance is also crucial. Every firm depends on its loyal clients. They may help a business expand its clientele, boost sales, and generate more revenue. Therefore, the process of developing software must include continuous performance testing. What Distinguishes It From Regular Performance Testing? Regular performance testing could occur as part of a release or at milestones rather than as a part of CI. However, it is possible to frequently incorporate continuous performance testing into each iteration. Regular load testing focuses on how well a software version performs under pressure. Every new application version is tested continuously for the load to verify that it will function adequately during peak hours. The entire build fails if a new application doesn’t satisfy the required performance criteria. After that, you need to check and resolve any performance problems in the most recent code. Continuous performance testing can validate code in pipelines for continuous deployment to ensure there are no application performance regressions. Once more, the goal is to be able to spot problems early and fix them without delay before they affect users or clients. Continuous load testing is a wonderful concept for certain businesses. However, due to a lack of performance settings for different API versions and a lack of knowledge about traffic variations, it might be difficult to implement. Benefits of Continuous Performance Testing There are several advantages to continuous testing. A summary of some continuous performance testing advantages is provided below. 1. Risk-Based Feedback In addition, tangible feedback assists managers and developers in finding and fixing notable flaws. Continuous testing ensures software features are ready for use before they are released. Risk-based insights from automated solutions may cast a significantly broader support system for business risk coverage than traditional (and time-consuming) human testing. Developers may make better design decisions right away with immediate feedback, which also provides managers with all the information they want to evaluate a release quickly. 2. Smarter Release Decisions Agile, DevOps, and Continuous Delivery have reduced the time needed to plan, develop, and deploy software changes. Therefore, releases can occur daily or as rarely as once every two weeks. It is becoming increasingly essential to employ automated testing to keep up with rapid release cycles to remain competitive and offer things customers want. However, a poorly considered release can cause more harm than gain if you don’t completely understand business risk. In addition, more and more companies are using automated solutions to balance the code’s complexity and the necessity for faster application delivery. Using a continuous testing tool and risk-based feedback, developers may select when and how to deploy new modifications . 3. More Efficient Testing In addition, this enables them to decide if their delivery pipeline needs to move left or right. Continuous testing helps managers and developers do the right tests at the right times . Automated testing minimizes false positives and timeouts, which are common in conventional testing settings. In addition, when testing is conducted throughout the software development process, developers can be sure they are creating a framework that is both safe and highly versatile. Redundancy is eliminated, and significant time is saved. Therefore, continuous testing ensures that software companies have the optimal architecture for the future expansion of their products, especially when users want new features. 4. More Stable User Experience Therefore, software developers must balance giving users the additional features they want and maintaining the smooth user experience they’ve enjoyed over time. Continuous testing prevents software bugs from reaching consumers and disrupting their experience. As software plays a major role in linking businesses with customers, one bad user experience might result in a financial disaster. Thanks to intensive testing, every element of the user experience is kept in mind. This helps maintain a company’s image and brand after its software is prepared for showtime. 5. Integrated Teams The days of transferring development code to segregated QA testers are long gone. Continuous testing enables teams to collaborate effectively and efficiently across the development lifecycle. Teams are more connected and more aware of each pipeline stage since quality is evaluated throughout the whole software development cycle, not just at the end. Software organizations today rely on continuous testing to guarantee that high-quality code is created from the moment development teams begin to code. Continuous Testing Tools Continuous testing is one of the latest software delivery concepts brought in by the third wave of test automation. However, learning continuous testing is challenging, and selecting the best technology may be daunting. Choosing the correct tool is crucial to your software testing and delivery success. The top eight continuous testing tools on the market right now are listed below. The automation tools like integrate with these CI/CD tools. Selenium, Playwright, Cypress, Katalon, JMeter, and K6 We have evaluated each tool’s benefits and potential downsides according to criteria, such as the types of supported tests, learning curves, the programming language used, support for continuous testing, support for the CI/CD ecosystem, breakthrough features, and so on. Jenkins The central build and continuous integration occur on , an It is a standalone Java software providing packages for Windows, macOS, and other operating systems that resemble Unix. Jenkins open-source automation server . Jenkins facilitates software development projects’ construction, delivery, and automation and has hundreds of plugins. Coming towards key features provided by Jenkins, it has simple OS installation and upgrading criteria with a seamless user-friendly interface. Also, It has a large community-contributed plugin resource which makes it more extensible. As mentioned, it has a simple environment set up through the user interface. It supports master-slave architecture and distributed builds working with pre-build stages that use shells and Windows command execution. It creates schedules using expressions and offers support for notifications about the development status. CircleCI A CI/CD technology called promotes quick software development and publication. In addition, the user’s workflow may be automated using CircleCI, from code development through testing and deployment. CircleCI You may integrate CircleCI with GitHub, GitHub Enterprise, and Bitbucket to make builds when new code lines are committed. CircleCI also offers cloud-managed continuous integration hosting or uses local infrastructure that is protected by a firewall. The notable traits of CircleCI include its . It uses a virtual machine or container to run the build and simply remove bugs. CircleCI supports automatic parallelizing. integration power with GitHub Enterprise, Bitbucket, and GitHub It supports rapid testing and allows branch-specific deployment which makes it very adaptable to any environment. It allows custom commands and automated merging for package uploading making it a great tool for customized testing. TeamCity The building management and continuous integration server JetBrains is called . TeamCity A continuous integration tool called TeamCity aids in developing and deploying various project kinds. . TeamCity interacts with Visual Studio and IDEs and operates in a Java context. The program supports .NET and open-stack applications and may be deployed on Windows and Linux systems New UI and direct connection with GitLab are features of TeamCity. Additionally, GitLab and Bitbucket server pull requests are supported. Finally, the release contains requests for AWS Spot Fleet, detection of Go tests, and token-based authentication. TeamCity provides several options for reusing the settings and configurations of the parent project in the child project. The tool concurrently does parallel builds in various contexts. It allows you to run previous builds, see test history reports, pin, tag, and favorite builds. TeamCity makes it very simple to interact with, alter and expand the server. The tool ensures the CI server is operational and reliable. The main feature of TeamCity is it gives flexible user administration, user role assignments, a grouping of users, several user authentication methods, and a log of all user activity for full transparency of all server operations. Testsigma is a new player in the test automation market. . In addition, it uses plain English for writing tests, which makes it easier for manual testers to pick up automation skills quickly. Testsigma The software’s cloud-based solution supports web, mobile, and API test automation For organizations with limited resources or complicated testing requirements, Testsigma also offers test infrastructure with the devices, operating systems, and browsers that work best. Testsigma uses AIs to help this practice as the testing industry moves to the left. The application uses AI to find possible threats and issues resulting from ongoing adjustments and alterations so that you can take quick action. To increase Testsigma’s functionality, you may incorporate Java or Selenium using the custom function. Popular CI/CD technologies include Jira and Slack, to mention a couple. Test Sigma supports and connects with both of these. Bamboo A continuous delivery pipeline is created by , a continuous integration server that automates the administration of software application releases. Bamboo Assigning versions, categorizing releases, building, and functional testing, as well as deploying and activating new versions on live systems, are all covered by Bamboo. The notable attribute of Bamboo is its builds are triggered depending on changes seen in the repository. Notifications are pushed from BitBucket, according to a predetermined schedule when a build is finished, or any combination of these. SVN, Git, and Mercurial repositories can immediately apply the CI scheme from the main line to newly discovered branches. It gives pre-environment rights that let developers and testers deploy to individual environments as needed while the production environment is kept secure. It allows up to 100 remote build agents and carries out many tests, runs concurrently, and receives timely responses. GitLab is a c for controlling various phases of the software development lifecycle. The main component is a web-based Git repository manager with problem tracking, statistics, and a Wiki. GitLab ollection of instruments With each change or push on GitLab, you have the option to start builds, launch tests, and deploy code. Jobs can be created on another server, on a virtual machine, or using Docker containers. The main feature of GitLab is that it . delivers safe applications and licensing compliance with container scanning, static application security testing (SAST), dynamic application security testing (DAST), and dependency scanning It provides a set of branching tools that are used to see, produce, and manage codes and project data. It has a single distributed version control system that is used to design, develop, and manage codes and project data, allowing for quick iteration and the delivery of business values. It offers scalability and a single source of truth for working together on projects and code. GitLab assists delivery teams in embracing CI and automating and accelerating application delivery and releases by automating builds, integration, and source code verification. Buddy is a . Buddy CI/CD tool that uses code from GitHub, Bitbucket, and GitLab to create, test, and deploy websites and apps It uses DevOps, monitoring, and notification processes together with Docker containers that come pre-installed with the languages and frameworks you may build upon. This CI/CD tool creates, alters, and utilizes test and build environments by providing superior Git integrations and support to the community. It has many different attachable services which include Elastic, MariaDB, Memcached, Mongo, PostgreSQL, RabbitMQ, Redis, Selenium Chrome, and Firefox. It makes sure that the workspace, project, pipeline, and action scopes are fixed, programmable, plain, and encrypted. Buddy supports intelligent change detection, cutting-edge caching, parallelism, and general optimizations. It manages workflows using templates for pipeline cloning, exporting, and importing. Travis CI is called . New contributions are automatically found by Travis CI and published to a GitHub repository. Additionally, Travis CI will build the project and run tests after each new code commit. A CI service used to create and test projects Travis CI Travis CI is a service provider. It has a lot of important features or traits as it has simple OS installation and upgrading criteria with a simple user-friendly interface. It allows viewing live builds for GitHub projects and multiple cloud service deployments. It has built-in database services and provides pristine VMs for each build compiling with iOS, Linux, and macOS. It supports different programming languages including R, C, Python, Ruby, Java, C, C#, C++, Perl, PHP, and JavaScript (with Node.js). It has multiple cloud services deployments and auto-deploys when builds pass. Concluding Thoughts Continuous Performance Testing Is the Way to Go if You Want to Stay Ahead of Performance Issues and Outperform Your Competitors. The major point is that monitoring performance throughout the development process of new features or products before they go live when bugs are present. can save time later during maintenance cycles Hence, . companies should always improve the procedures used by development teams Moreover, thanks to continuous performance testing, it won’t interfere with your consumers’ experience. This involves considering how much load each feature will need once used. If you want to know more about Continuous Performance Testing or have concerns about how to incorporate it into your CI/CD pipeline as part of your DevOps process, are here to help. QAlified Testing Services

Everything You Need to Know About Continuous Performance Testing

Todo lo que necesita saber sobre las pruebas de rendimiento continuas

Tout ce que vous devez savoir sur les tests de performance continus

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継続的パフォーマンス テストについて知っておくべきことすべて

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关于持续性能测试您需要了解的一切

Let’s think for a minute about a very small task in the current project you are working on. The developers have pushed their code for testing. Testers now start the action by testing the smaller parts of the code. This is tough because to execute this successfully, you might have to read the code and understand the smaller parts. This is a developer’s job. Next, you move on to test performance and load capabilities by hit and trial. It takes time, but you find the threshold values eventually. More specific testing paradigms follow in the upcoming days. Finally, you conclude and convey the bug to the manager, who then assigns it to a specific team member. The progress of this bug goes back and forth with frequent updates and more follow-up bugs, creating miscommunications within the development and testing team. As a result, it wastes a lot of time. This is the story of just one project or sprint. What happens when we are floating around lots of sprint data, features, and customer-related bugs? It is just a matter of time before we realize that time is getting wasted and project costs are building up much more than anticipated. Hence, we look back to a few tools that help us in various testing departments and eliminate human errors by ensuring the performance is checked at every spot during the execution. This post explores these software testing tools, focusing on the available software tools and scenarios where they fit perfectly for execution. Unit Testing Tools The first type of software testing tool we mention is the unit testing tool because it is the first type of inspection our software goes through. What are they? Unit testing tools help perform unit testing, which is testing smaller pieces (units) of code. These pieces are often aimed at a specific feature or a special condition that may or may not resolve a bug. For instance, unit testing can test for validations when the “Name” field on the form contains numbers as input. Unit testing is important because it works on a micro level of the software. When this is done, there is less chance that an important part of the code is overlooked. Every component undergoes tests which ultimately result in quality software. How do they work? Due to their nature, unit testing is often considered work done by the developers rather than the tester. This is achieved when the developer writes the feature or resolves the bug. They run the tests to verify that the tests validate the code. In the next run, when another developer goes through the same cycle, they run the previous developer’s tests along with their own to ensure that the software is intact and ready to be sent to the testers. It may look similar to this screen when a developer runs the unit tests on their code: Source This complete process is taken care of by unit testing tools that provide features such as , helping developers to test the software smoothly. mocking in PyUnit Examples of unit testing tools The following tools may help you get started in your unit testing journey: JUnit - Java TestNG - Java unittest - Python PHPUnit - PHP Integration Testing Tools Integration testing tools focus on integrated pieces of code and testing a complete connected system. What are they? The previous tool's expertise stops on a micro level i.e. on a fixed and small portion of the software. For example, if I talk about an eCommerce website, a cart is a feature of the complete web application. We can either perform unit testing on the working of the cart or a specific feature of carts, such as using wallets during checkouts, etc. But is there a guarantee in unit testing that our checkout system will work for the end-user if it passes unit tests? Well, that’s generally not the case. A small feature may work when dummy data (which is exactly the same as the real data) is fed through unit tests. But when an actual user works on the system, there are a lot of scenarios that you might not have prepared for or have tested for. Such bugs are pointed out in integration tools. Integration tools, as their name suggests, work on the integrated software. The integrated software is the one that combines each of the units (small features) into the actual big product. This is done after the unit tests and explores the bugs related to communications between codes, data flows, network connections, etc. How do they work? Integration testing starts with a single point in the application. So, if the tester is testing an eCommerce application, it is safe and simple to assume the starting point as the landing page. Next, we move on to different components associated with this stage. Since it is not usability testing, we cannot roam around thinking that somewhere we will hit a bug in our expedition. To understand the connected components, understand the products technically and from a business point of view. Then you will know what other stages are and what they expect from the current stage. Then, you move to the next stage and repeat the same thing here too. This is to be done until you reach the final stage, which in our example would be payment success/failure. Examples of integration testing tools The following tools may help you get started in your integration testing journey: Protractor Citrus Jasmine Load Testing Tools The load testing tool helps identify the load threshold of the system, helping build a scalable system. What are they? Recently a cricket match was watched live on Disney+ Hotstar by 16 million people. This is 16 million requests sent concurrently. Such events are common around the globe when a special event occurs. For example, when a FIFA world cup final is streamed online in football-supporting countries. Or when the Black Friday sale is opened at midnight in the USA. Succeeding with such events as a business is extremely necessary because bad word of mouth spreads like fire. Since you are not the only one out there providing such services, you may lose a lot of business and money. Load testing ensures that whenever such an event occurs, it is well managed and there are no hiccups. How do they work? The best way to conduct this test is to put a load on the system. What kind of load should be put depends on the type of system you have designed. For instance, if you are looking to stream a live telecast, why not start with testing if your system can withstand 10 million subsequent requests for live streaming? Yes? Try for 20 million. No? Try for 15 million and so on. Include this in your report, which is then reviewed to check and verify the expectation of clients or management. If you expect 15 million users and your threshold is 15 million, that will not work. For such a number, the threshold should be slightly higher for backups, keeping a margin for additional users and not putting 100% load on the servers. This testing tool is extremely important if your business can demand such a thing in the future. Examples of load testing tools The following tools may help you get started in your load-testing journey: JMeter - Java Locust - Python LoadNinja - Cloud-based UI Testing Tools UI Testing tools work on the user interface of the application. They ensure that the user interface looks and behaves as per the expectations. What are they? The of an application is the first thing a user interacts with. So, technically, even before they can applaud your backend skills, they need a user interface to reach that point. What if one button goes out of the viewport? Can we expect the user to scroll the page horizontally every time? Or what if the contrast of two elements is such that one element gets overshadowed by the other? Such issues with the user interface can prove costly and require testing. user interface that user interface testing is not optional and should always be kept as a part of the testing phase. Please note How do they work? As the software’s user interface has developed, so have the user interface testing tools to cope with it. Earlier, most UI testing was done manually, but automation tools have now developed unbelievable strength. Apart from conventional testing through code, UI testing can now be done by visualizing the elements automatically and providing a preliminary screening report for it. A few tools nowadays have perfected their surveillance game to the next level. Not only can you detect the UI but also sense the changes associated with it. If any user interfaces element changes, it can detect and change the related UI test cases accordingly. Although, it’s up to the testers and their requirements to choose the tools. Examples of UI testing tools The following tools may help you get started in your UI-testing journey: Testsigma - AI-enabled, self-healing, English-based Cypress - JavaScript TestCafe - JavaScript Bug Tracking Tools The next in our list of software testing tools is the bug tracking tool. As the name suggests, it helps in tracking all the bugs and managing their information in a single place. What are they? As the project scales up, the chances of bugs increase as well. This happens due to more functionalities, code, people involvement, etc. Hence, keeping track of all the bugs becomes difficult as one organization may contain different products. Even if you could log bugs in a directory to organize them according to the project, there are a lot of other pieces of information associated with each bug. For instance, a bug may have a few log files related to steps that raise the bug and are attached by testers. Developers look at these at any point while resolving or referring to the bug. Source Similarly, each bug may have its own information noted down after the tester files the bug. For instance, when it was assigned to what person and what the assignee replied during this process, etc. All this requires a tracking and management tool called a bug-tracking tool in software testing. With this, you get all the bug-related information and project management features on a single page. How do they work? One bug-tracking tool does not work exactly the same way as the other. Since they are tracking tools, other than tracking features, they may present their own set of skills that might or might not be used during a project or by an organization. All bug tracking tools must have three things intact - the bug information, its current status, and the associated files. i. The bug information is all the information related to the bug, such as what systems are seeing the issue, what version of the product, additional details, etc. ii. The status is the current process the bug is going through currently. For instance, is it under test and assigned to tester? Or is it “active” and assigned to the developer? iii. And the last thing - the associated files are the log files, screenshots or any other such files that might help a developer understand the bug or may be a good reference point when we retrospect it later in the future. Types of bug tracking tools The following tools may help you get started in your bug-tracking journey: Bugzilla JIRA Mantis API Testing Tools API testing tools focus on the quality and abilities of an API that carries data from server to server or server to client systems. What are they? The or application programming interfaces have become veins of the system. They are responsible for communication between interfaces and a lot of data flow. For instance, all organizations have internal portals to manage support ticket systems for assistance. To save time and increase security, generally, these organizations remove the page where credentials are entered and take the “employee id” from the VPN or system login. Then, as soon as the employee hits the web address of the intranet, s/he is presented with all information even if s/he changes the system and there are no cookies, etc. This is achieved through APIs. APIs What happens here is as follows: i. The employee enters the URL. ii. The network fetches the system id, and the employee logs into it. iii. An API is hit with this employee id. iv. The API reaches the server and processes or filters information based on employee ID. v. The data of the employee is sent back to the client machine. vi. The data is shown in the UI. Apart from this, APIs also help in the reusability of the codes. If a navigation bar has five options and you hard code it, you might have to change that in every place on every page when a sixth option is added or one is deleted. It’s better to fetch these options from the server (or any other such dynamic information) with the help of APIs. So, it is easier to understand now how sensitive APIs are and how great a need it is to protect, inspect and make them reliable for the benefit of our complete system. This is achieved through API testing tools. How do they work? An API connects a lot of components. A client sends the API with some information (including authentication data), a server verifies it, a database provides the data, and then this data is reflected in other places. This simple process means if an API is not working, it might be due to any of the components, so careful scrutiny of API becomes important. i. Make sure all other components associated with the APIs are working correctly before starting with the APIs. ii. Next, understand the cases where APIs proceed and where it fails. We need to test both of them. iii. Test for API scalability - how many requests does this API handle simultaneously? iv. Understand the API responses, and if a “write” action is taken, it reflects in the database. v. Repeat the same for all the APIs using API testing tools. Since APIs are not seen on the GUI side, they need tools to test everything mentioned above. A good API testing tool requires minimum input from the tester and provides maximum information to them. This may include response code, response time, formatted response and raw response, etc. Validations should also be handled by the tool. If a request is a GET request and the tester sends it via the “POST” method, the tool should be able to decode this instead of just returning a blank response and leaving the guesswork to the tester. Similarly, the text (or JSON / XML) sent with the body should be able to validate if the request is incomplete, etc. Examples of API testing tools The following tools may help you get started in your API testing journey: Testsigma Postman Katalon Studio Apigee Security Testing Tools Organizations adopt security testing tools to ensure that the security of the application is always up to the standards and sensitive information is safe in the databases. What are they? In December 2016, it was reported that . Yahoo confirmed this in October 2017, announcing that all 3 billion accounts were impacted in this breach. This was a serious case of security issues as sensitive information such as name, email, and even telephone numbers were subject to this breach. It was termed the biggest breach on record and has been a major concern among Yahoo users since then. Yahoo had been subjected to malicious activities Such types of breaches have happened worldwide, with stolen data varying from bank accounts and personal information. And it is not so surprising if the next attack is on your web application. If it has the data, always consider it on the radar. What we can do from our side is to be sure that all the open doors are sealed and that we have a secure web application hosted over the internet. This is verified and inspected by security testing tools. How do they work? Security testing tools work in various departments to ensure that when someone is looking to enter the infrastructure illegally, they are stopped at the first check. These tools can monitor the infrastructure regularly and provide tools that can help you point out vulnerabilities and take action on them. The type of tool you use may have its own specialty and style of working. Since security issues can arise in any project domain, choosing a mix of tools or a perfect tool according to the project is important. A few areas to choose from are a network analysis tool, web application analysis, database analysis, etc. Types of security testing tools The following tools may help you get started in your security testing journey: Wireshark Intruder Zed Attack Proxy (ZAP) Wrapping it up The above sections conclude that software testing tools are mandatory no matter what product you build. If it is released to the end-users, it better be tested with all these tools. Apart from the ones discussed above, there may arise a need to use software testing tools specific to a project. For instance, if you are building a web application, cross-browser testing with is essential in such a case. If you are building a mobile application, then a software testing tool specific to mobile testing is important. cross-browser testing tools However, if we start counting such scenarios, we may end up writing a short novel. Keeping this blog post short and generic in nature, it's better to wrap it up here. I hope this post helps you execute tests efficiently in your next project. Thank you for giving this post your valuable time.

Software Testing Tools: A Complete Guide to Smarter Testing

Software testing is a labor-intensive task that has associated labor costs. So, automating the testing process seems like an instant go-to solution. But to leverage the benefits of automation like accuracy & speed, one should make informed decisions considering the circumstances & ability of the organization to allocate a budget for the process. Post-COVID, the software industry has been seeing a substantial increase in demand for upgrading existing software & introducing new software in the market. But the availability of human resources isn’t aligned with demand, and deadlines for software delivery have reduced. This leads to project failures. This article aims to deliver an overview of automation, its types, applications, techniques & full-fledged automated testing process. Automation testing Guide: Let’s begin with a few fundamentals and then dive into the details of the process. i. Consider the unpredictability of user interaction; no process should be fully automated. Testing of predictable user behavior should be automated, and manual testing should be done for unpredictable user behaviors. ii. Well-defined test process & test cases for automation. The process should start with a detailed overview of functionality and documented automation library. iii. Defining certain fundamental principles for what should be tested & which part of the testing should be automated will enhance accuracy & will save time. iv. Bugs are unavoidable, so automate fixing bugs that are more likely to appear. This will also add to the scalability of the software or functionality. v. Automate the process of document parsing, submissions, extraction of static content & alerts. vi. Features with the least user impact & relevance should not be allocated resources for automation. Automate the testing of features with the highest frequency of usage. vii. Pick up repetitive tasks and then set up an automation testing environment. The trade-off among tasks should be done on a case-to-case basis. viii. Set up some ground rules for overall test structures with well-defined test flags. It must include the test framework and scope of maintainability. This will result in easy adaptability across testing environments & enhance efficiency. ix. Automation should be adopted in an early phase of development, i.e., unit testing. Instead of automating the testing for the whole feature, one should set up testing environments for a specific unit that requires automation. Types of automation testing: Unit Testing: Unit testing, also known as granular testing, is carried out at a lower level of development. It is a part of white box testing, and is used for continuous testing processes, where the tester runs the test on smaller chunks of individual source codes or components. It can be refactored till all the test cases are passed. Integration testing: This is done at a mid-level or end-level of development. The significance of integration testing is to validate the behavior of the source code when smaller chunks of codes are tested combinedly. It is usually followed by unit testing. The approach to integration testing depends on case to case; it can be a top-down or a bottom-up approach. Regression testing: This type of testing is done to validate the behavior of the source codes when certain changes are carried out. These changes can be an upgrade or addition to an existing feature. Hence, it is also referred to as maintenance testing. In regression testing, test cases are repeatedly run to check whether the changes made are working as expected or whether those changes have affected the existing code. System Testing: This testing is used to validate the behavior and evaluate compliance with the system. Stating it in simple terms, it is done to test the system’s response as an application when all components are tested together. Major features like acceptance and delivery of requirements, security standards, maintainability, scalability, & reliability of the system are evaluated. Data-driven testing: It is an extension of the automated testing process. In this case, external data is fed to the system to validate the automated test cases. The data is usually stored in a tabular format. The best reward of this testing is data reusability. It offers to modify and redesign existing test cases to make them more efficient. Techniques of automation testing: Before we get to testing techniques, let's go through four major automation testing processes: i. Setting up a precise testing plan ii. Designing a testing plan iii. Execution of test cases iv. Final evaluation & analysis Techniques: i. Unified backup automation solution to aggregate backups in maintaining documentation, downloading claims, and synchronizing routine tasks. ii. Procuring an automation tool that can collaborate with the available resource. iii. Selecting a solution with higher versatility to ensure universal support extraction. iv. Increasing the frequency of automation testing & implementing it from an early stage. v. Choosing to integrate automation where the frequency of execution is high. Application of automation testing: i. Improves test coverage & offers easy scalability: Automation provides the scope of coverage in test cases that are in no way possible in manual testing. These solutions can read contents from memory and various states of internal code contents of files. ii. It can act out with millions of virtual users within a network and webs. iii. Expands test accuracy and allows the manual testing team to design efficient test cases with higher complexity in features. One of the tried & tested processes in reducing the overall cost of development. Conclusion: The concept of automation testing boils down to the delivery of efficient & best in class features and software products. So, one of the major tasks is to identify a unified & personalized platform that can figure out hidden requirements and run end-to-end test cases. Testsigma is your go-to solution that offers customized end-to-end test automation.

Automated Testing Guide, types, applications, & techniques.

In this article, I will share a field, specifically with the . I want to help you select the best tool for your context, so I’m offering a centralized place with information about the different options on the market. state of the art in the test automation low code approach It’s kind of difficult to distinguish if this is another marketing-created buzzword in the software development world. In literature (blogs, websites, tech journals), I’ve observed how different terms are used for the same idea, including “low code”, “no-code”, “codeless”, “scriptless”, and I’m probably missing some. Basically, “low code” refers to an alternative approach to traditional software development. Instead of defining the expected behavior of the system in code, it tends to be more visual and uses little or no code at all. As I see it, we are increasing the level of abstraction of the language we use to express to machines what to do. We started programming circuits, strings with 0’s and 1’s, assembler instructions, and then different languages, providing more and more abstraction layers. Each layer is removing complexity, closing the gap between the human and the machine. The flip side is that the abstraction also has a cost, typically associated with performance or flexibility, but it typically makes sense to pay that cost in terms of productivity and results. We could say that low code is another abstraction layer on the top, and we are also seeing how we can benefit from this approach for test automation. Low code platforms for test automation aim to simplify the test automation with functionality that doesn’t require the user to write almost any code. You will have a recorder that will let you create test cases easily and edit them with a simple interface, without requiring coding skills. In the last few years, new low code solutions for test automation have been spinning up. There is an interesting cycle: more entrepreneurs are trying to build this type of tool, more investors are supporting them, and the testing community adopts the tools and provides feedback. These are just a few signs that it’s an interesting approach for test automation to follow. Today, we are releasing the first draft of our research, just with the list of tools, as another step toward discovering blind spots and getting early feedback. The next steps are: Describe the details of our research method. Add more information about each tool, highlighting the main characteristics. Analyze results and give our perspective and conclusions. Some considerations: We included only test automation tools for web and mobile. For now, we excluded tools that are specific to a certain platform such as Salesforce or GeneXus (that’s why you won’t find or in the list). Provar GXtest We are trying to exclude those tools that don’t provide enough information to evaluate them and will continue filtering the list as we progress through the research. Low Code Tools for Test Automation ACCELQ AI Codeless Testing Help Center AscentialTest Autify Avo Automation Cerberus Testing CloudQA Co.meta Endtest Functionize Ghost Inspector Ginger iNSta IntelliSWAUT Katalon Kobiton Leapwork Mabl MuukTest Perfecto Scriptlessl Preflight QARA Enterprise Rainforest QA Rapise Cross Browser Testing Reflect.run Sahi Pro Sofy.ai Stela test Telerik test.ai TestArchitect TestComplete Testim TestingWhiz TestProject testRigor Testsigma Usetrace Virtuoso QA Waldo Worksoft ZAPTEST ZeuZ Asking for Feedback If you know a test automation tool with a low-code approach that is not included in the list according to the criteria we followed, please let us know and we will review it, add it to the list if it corresponds, and we will also try to understand why we didn’t find it in our search. I want to thank Danny Gutiérrez, who performed most of the research, also with the help of Andrea González, Leticia Martínez, and Lucía González. Follow us on , , and , to be part of our community! LinkedIn Facebook Twitter Also published here

Low Code Tools for Test Automation

As a product manager, testing is second nature. The build-measure-learn principle from the lean startup methodology encourages teams to test everything they build to get insights to improve subsequent iterations and the product as a whole. The dynamics of how testing is done or how involved product managers are in testing differs for teams, throughout the product development process. Large teams have dedicated QA and testing teams but we may not say the same for small startups. However, the focus of this article is to discuss the common types of tests that are run at various parts of the development cycle, and why product managers should have a grasp of them. Unit Tests When software developers write code they write it in lines, and a unit of code can be about 15 lines, less or more. But the smaller the unit, the easier it is to test. So often, long units of code are broken into multiple smaller units to improve maintainability. Unit testing is testing a coding unit to verify that it is working correctly. This test is done while a software developer is coding. To perform this test, a piece of code [unit test] is written to test a unit. The same concept works for component testing. Unit tests can be automated by integrating tools like reSharper, NUnit, etc, with developer tools to write and run the tests. Since mostly for a project, all tests can be run at once in no particular order, it’s best that there is no dependency between the tests. Let’s use a mobile phone as an analogy to give more clarity. A phone has a battery, a sim slot, a charging port, an earpiece port, etc; right? Unit testing is like testing these phone parts individually to ensure that they work fine. Integration Tests It’s one thing to test different units of code in isolation and they pass, and another when combined as a group. Integration testing is performed after unit testing by testing different units, modules, or components of an application together, to check that these units combined work smoothly. This is not the same as system testing. Integration testing focuses on the interfacing of units and identifying interface errors, while system testing tests the finished product to help identify system errors. Also, integration testing is generally limited to the functional aspects of the interconnected units, while system testing covers both the functional and non-functional aspects of the application. Smoke Tests Smoke testing also known as the build verification test is used to check that the product is stable. The word “stable” in testing springs up these common questions: Does stable mean ready to be released? Does stable mean it no longer contains a bug? I would like to think that the standard for what stable is will differ in companies. But generally, stability should portray ‘bug-free enough’ to be pushed to production, such that the product’s behavior does not change anymore. Hence, stable! Smoke testing is a type of acceptance testing that is done to ensure a build is stable enough to undergo other tests like regression and functional testing. Typically, smoke testing is done manually, but automating it in a CI pipeline helps to prevent an unstable build from being pushed into the production environment. To make the best use of UAT, smoke testing should be conducted on every new build. Also, conducting smoke tests first before asking select users to test, saves a lot of time. Else, the bug list would constitute bugs that should have been otherwise fished out earlier and fixed. Sanity regression test is another type of testing that provides a gate check or indication that a build is ready for more testing, just like with smoke testing. Regression Tests When changes are made to the codebase, regression testing is done to ensure the overall stability of the existing features to verify that the change did not adversely affect any existing functionality. This entails re-running already executed test cases. The extent of the regression test depends on the scope of the newly added code to the codebase; whether it is going to be a unit regression test, partial or complete regression. Some regression testing tools to explore: Avo Assure, Subject 7, Digivante, Testsigma, etc. Acceptance Tests Towards the end of a software testing cycle, the new application is tested in line with the real-time scenarios to verify that it meets all the specifications, expected quality, and design before it is pushed to commercial production. This test commonly called User Acceptance Testing or End-to-End testing is an acceptance test. As the name implies, an acceptance test is a test for acceptability. If an application does not meet the specifications, then it has to be modified, debugged, or what else is required to suit the needs of the users and business. Acceptance testing is done in stages, first, there is the Alpha testing, and next, the Beta testing. Users or other authorized entities can determine whether to accept the application or not when they test. Let’s dive a little into some types of acceptance testing. undefined [ ] Alpha Tests Alpha testing is a type of User Acceptance Testing performed by internal team members who put themselves in the user’s shoes while testing. They are knowledgeable of the product but not directly involved in the development. This test is done in the development environment and has a longer test cycle—The testers give feedback to the development team, and test again once the bugs or feedback are implemented. undefined [ ] Beta Tests Beta testing serves as a soft launch and is done by a select number of potential-real users called beta testers, in the production environment. This test enables you to get unbiased feedback about the user experience and usability of the application. It also helps you identify critical bugs to be fixed before release to a wider user base. In summary, Commonly, product managers are focused on testing at the end of the development cycle, but it makes more sense to create a culture of product testing that keeps the whole team on the same page about QA and testing. Even if you are not directly concerned with QA and testing, it is recommended that you at least know the types of tests that need to be run at various aspects of the development life cycle of your product. This will help improve the quality of your product. Know a non-technical person who is trying to get into the product management space? You may want to share my previous article on the topic with them. Thanks for sharing! Explore Must-Know Technical Concepts for Product Managers: Your Guide to the Mind of a Dev Feel free to connect with me on . LinkedIn

What Product Managers Should Know About App Testing

Recently, we are seeing a growing inclination of enterprises to adopt DevOps practices. If we refer to this the global DevOps Platform market size is estimated to grow up to by , from in , which looks like a of during . report, USD 26,370 million 2028 USD 6,737.6 million 2021 CAGR 20.7% 2022-2028 Let’s have a brief introduction to Devops and understand the devops lifecycle. DevOps can be defined as a cultural approach towards a collaborative atmosphere around the dev team and IT team. It is an amalgamation of philosophies, practices, and tools that enhance the organization’s efficiency in the deliverability of products and services, i.e., faster time to market. So, how is it actually helping in faster software delivery? Increases agility Reduces manual effort Efficient cross-functional team collaboration Continuous innovation Minimal defects (which is obvious when Dev and IT teams work in tandem) To understand how devops practices make this possible, let’s understand the lifecycle of devops. What is DevOps Lifecycle? The DevOps lifecycle is an iterative process of automated software development, integration, testing, deployment, and monitoring. The DevOps approach is all about continuous experimentation and learning, followed by continuous improvements. These continuous improvements are what you see as new updates in your everyday software application updates. So a simple answer to - “What is DevOps lifecycle?”- is all the processes that ensure end-to-end optimization of the entire software development lifecycle, facilitating faster deliverability. Decoding the Lifecycle of DevOps Moving forward from “What is DevOps lifecycle?” to “What are the different stages that constitute the lifecycle of devops?” From planning to monitoring, the entire process has been divided into 7 different stages. Any stage or phase, out of these 7 stages, can iterate multiple times throughout the project until it is finished or conforms to our requirements. The following are the devops lifecycle phases: Continuous Development Continuous Integration Continuous Testing Continuous Feedback Continuous Monitoring Continuous Deployment Continuous Operations Continuous Development This is the very first phase of the devops lifecycle where the objectives of a project are mapped, based on which the entire software development process is envisioned. Here, the DevOps team primarily focuses on the planning and coding part of the project, in which depending upon the business needs, the developers start coding the source code for the application. The choices used for coding applications are and . Though we don’t really require any tool for coding but maintaining the codes is vital. We see a plethora of version control tools available such as and many more. This process of maintaining the source code is known as (SCM). Furthermore, these codes can be packaged into .exe files with the use of tools like , , and similar other tools. most popular language JavaScript, C/C++, Ruby, Python GIT, TFS, GitLab, Subversion, Mercurial, Jira, BitBucket, Source Code Management Garden Maven Continuous Integration The source code goes through multiple modifications, as it is a continuous process of review and the changes are implemented frequently. Therefore, this phase is also known as the Code Integration phase and is the most crucial in the entire lifecycle of devops. The new codes having additional functionalities are built and integrated into the existing code. The new code generated and integrated with the existing code is made to pass through different stages of unit testing, code review, and integration testing, which finally leads to compilation, and packaging. This process of continuous integration also helps in reflecting the changes, the end-users would possibly experience with the updated code. Moreover, this is also the stage where developers plan the tests required in the later stages of the devops lifecycle. There are various tools that are used in the procuring of the updated code and structuring it into .exe format. To name a few - , and many more. Among them, Jenkin is an open-source tool, used widely to automate these builds and tests. Jenkin, Bamboo, GitLab, CI Continuous Testing Some developers may prefer this step of the devops lifecycle, prior to Continuous Integration. In this phase, the Quality Analysts test the code constantly for bugs and errors, and upon discovery of any, it is sent back to the Continuous Integration phase for appropriate modifications. These tests are automated, where a test environment is simulated with the use of . These automated tests not only save us time and effort but also reports generated from them, simplify the analysis of failed test cases. Thereby, reducing the provisioning and maintenance costs of test environments. Docker Containers Following these automated tests, the code is then passed through the process or process; qualifying this, the resultant code is simpler and bug-free. UAT User Assessment Testing There are different devops tools used for continuous testing - , and . Selenium is an open-source automation testing tool, a popular choice, as it seamlessly supports multiple platforms and browsers. We do have a unified AI-driven test automation platform, TestSigma, that eliminates the technical complexities of automated tests through artificial intelligence. JUnit, Selenium, TestNG TestSigma Continuous Feedback In this phase of devops lifecycle, the continuous improvements implemented to the code during the continuous integration and continuous testing, are analyzed. The developers measure and analyze the outcome of all the modifications implemented into the code. It is the stage in the lifecycle of devops where the users/customers who tested the code, give their feedback as per their experience and expectations. The feedback received is assessed promptly and the modifications recommended are again implemented into the code. A positive response from the customers paves way for the release of a new version or update of the software application. Continuous Monitoring This phase in the lifecycle of devops, looks forward to the major participation of the IT team. The developers record data of the application usage and constantly monitor each functionality. The most common errors that are resolved by the developers are - “ ,” “ ,” etc. Server not reachable Memory Down Through continuous monitoring, we can sustain the availability of services of an application by determining the threats and root causes of recurring system failures. The role of the IT team seems vital, as they supervise the entire user activity for any unusual behavior and trace the presence of bugs. Some popular DevOps tools used in this are - , and . These tools help in empowering the IT teams in monitoring the performance of the system, the production server, and subsequently the application. NewRelic, Sensu, ELK Stack, Splunk Nagios If any major issue is observed, then the application is made to rerun all the earlier stages of the devops lifecycle. Continuous Deployment Though conventionally this step occurs before the continuous monitoring, the developers ensure that this step remains active throughout the lifecycle of devops, especially after our application is live and starts receiving traffic. In this phase, the finalized tested code is deployed to the production server. The key process in this stage of the devops lifecycle is Configuration Management, which ensures accurate deployment of the codes. Essentially, configuration management is responsible for maintaining the consistency of the application in terms of performance and functioning, right from the release of codes to the servers - to scheduling the updates, whilst ensuring the configurations are kept consistent throughout. Some popular devops tools used for Configuration Management are - , and . Containerisation tools like , are also used in achieving continuous deployment through configuration management.  Vagrant is known for developing coherence between different environments - from the development and testing of code to staging and production. Likewise, the devops teams use Docker for achieving scalability of continuous deployment. The benefit of these containerization tools is that they help in nullifying the production failures and also system errors by replicating and packaging the software couplings from the phases of development, testing, and staging. Ultimately the application runs smoothly on any device. Ansible, Puppet Chef Vagrant Continuous Operation The last phase in the lifecycle of devops, and the shortest and least complicated of all stages. The purpose of this process is to automate the release of the application and its subsequent updates. This is one of the crucial phases in the devops lifecycle as it aims at eliminating planned downtime. A planned downtime refers to the time when the servers are offline for releasing updates. This downtime is equated to a loss, as the customers won’t be able to use it. With the automation of these processes, continuous operation boosts the uptime of the application. The tools like and , container orchestrators, are most commonly used in this phase, to simplify the steps in the build, test, and deployment of applications to multiple environments. Kubernetes Docker Conclusion We have reached the end of this discussion, from understanding “What is DevOps lifecycle?” to going into detail about the 7Cs of the DevOps lifecycle with their commonly preferred tools. The key objective behind learning the lifecycle of devops, is to be able to identify the steps involved in the development, and how we can maintain the continuity and optimize automation. This entire approach is all about the collaborative efforts of the developers, testers, and operations teams, and the elimination of siloed structure of working, working in a way where the software gets delivered quickly. To make this process even simpler, we have a lot of platforms ( , , ) available in the market, supporting a wide range of integrations of these devops tools, plus supporting a variety of source code languages. BuildPiper Akana Rancher Labs The devops culture is new, but it is here to stay for a very long time. With its aim at delivering the highest quality standards to your software, it is about time for businesses to focus on keep rolling new updates with zero downtime and zero errors. Also Published here

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