Iterate faster, and lower risks by transitioning to an intuitive no-code interface. Bridge the gap between data scientists, software engineers and managers, once and for all.
Mature ML teams reuse their code over and over again while tuning data and params. Iterate faster by transitioning to a no-code environment - use a visual UI to make and deploy changes instead of manually updating code each time.
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Thorough testing is crucial to the success of a software product. If your software doesn’t work properly, chances are strong that most people won’t buy or use it…at least not for long. But testing to find defects or bugs is time-consuming, expensive, often repetitive, and subject to human error. Automated testing, in which Quality Assurance teams use software tools to run detailed, repetitive, and data-intensive tests automatically, helps teams improve software quality and make the most of their always-limited testing resources.
Use these top tips to ensure that your software testing is successful and you get the maximum return on investment (ROI):
It is impossible to automate all testing, so it is important to determine what test cases should be automated first.
The benefit of automated testing is linked to how many times a given test can be repeated. Tests that are only performed a few times are better left for manual testing. Good test cases for automation are ones that are run frequently and require large amounts of data to perform the same action.
You can get the most benefit out of your automated testing efforts by automating:
Success in test automation requires careful planning and design work. Start out by creating an automation plan. This allows you to identify the initial set of tests to automate and serve as a guide for future tests. First, you should define your goal for automated testing and determine which types of tests to automate. There are a few different types of testing, and each has its place in the testing process. For instance, unit testing is used to test a small part of the intended application. To test a certain piece of the application’s UI, you would use functional or GUI testing.
After determining your goal and which types of tests to automate, you should decide what actions your automated tests will perform. Don’t just create test steps that test various aspects of the application’s behavior at one time. Large, complex automated tests are difficult to edit and debug. It is best to divide your tests into several logical, smaller tests. It makes your test environment more coherent and manageable and allows you to share test code, test data, and processes. You will get more opportunities to update your automated tests just by adding small tests that address new functionality. Test the functionality of your application as you add it, rather than waiting until the whole feature is implemented.
When creating tests, try to keep them small and focused on one objective. For example, separate tests for read-only versus reading/write tests. This allows you to use these individual tests repeatedly without including them in every automated test.
Once you create several simple automated tests, you can group your tests into one, larger automated test. You can organize automated tests by the application’s functional area, major/minor division in the application, common functions, or a base set of test data. If an automated test refers to other tests, you may need to create a test tree, where you can run tests in a specific order.
To get the most out of your automated testing, testing should be started as early as possible and ran as often as needed. The earlier testers get involved in the life cycle of the project the better, and the more you test, the more bugs you find. Automated unit testing can be implemented on day one and then you can gradually build your automated test suite. Bugs detected early are a lot cheaper to fix than those discovered later in production or deployment.
With the shift left movement, developers and advanced testers are now empowered to build and run tests. Tools allow users to run functional UI tests for web and desktop applications from within their favorite IDEs. With support for Visual Studio and Java IDEs such as IntelliJ and Eclipse, developers never have to leave the comfort of their ecosystem to validate application quality meaning teams can quickly and easily shift left to deliver software faster.
Selecting an automated testing tool is essential for test automation. There are a lot of automated testing tools on the market, and it is important to choose the automated testing tool that best suits your overall requirements.
Consider these key points when selecting an automated testing tool:
For detailed information about selecting automated testing tools for automated testing, see Selecting Automated Testing Tools.
Usually, the creation of different tests is based on QA engineers’ skill levels. It is important to identify the level of experience and skills for each of your team members and divide your automated testing efforts accordingly. For instance, writing automated test scripts requires expert knowledge of scripting languages. Thus, in order to perform these tasks, you should have QA engineers that know the script language provided by the automated testing tool.
Some team members may not be versed in writing automated test scripts. These QA engineers may be better at writing test cases. It is better when an automated testing tool has a way to create automated tests that do not require an in-depth knowledge of scripting languages.
You should also collaborate on your automated testing project with other QA engineers in your department. Testing performed by a team is more effective for finding defects and the right automated testing tool allows you to share your projects with several testers.
Good test data is extremely useful for data-driven testing. The data that should be entered into input fields during an automated test is usually stored in an external file. This data might be read from a database or any other data source like text or XML files, Excel sheets, and database tables. A good automated testing tool actually understands the contents of the data files and iterates over the contents in the automated test. Using external data makes your automated tests reusable and easier to maintain. To add different testing scenarios, the data files can be easily extended with new data without needing to edit the actual automated test.
Typically, you create test data manually and then save it to the desired data storage. However, you will find tools that provide you with the Data Generator that assists you in creating Table variables and Excel files that store test data. This approach lets you generate data of the desired type (integer numbers, strings, boolean values, and so on) and automatically save this data to the specified variable or file. Using this feature, you decrease the time spent on preparing test data for data-driven tests.
Creating test data for your automated tests is boring, but you should invest time and effort into creating data that is well structured. With good test data available, writing automated tests becomes a lot easier. The earlier you create good-quality data, the easier it is to extend existing automated tests along with the application’s development.
Automated tests created with scripts or keyword tests are dependent on the application under test. The user interface of the application may change between builds, especially in the early stages. These changes may affect the test results, or your automated tests may no longer work with future versions of the application. The problem is automated testing tools use a series of properties to identify and locate an object. Sometimes a testing tool relies on location coordinates to find the object. For instance, if the control caption or its location has changed, the automated test will no longer be able to find the object when it runs and will fail. To run the automated test successfully, you may need to replace old names with new ones in the entire project, before running the test against the new version of the application. However, if you provide unique names for your controls, it makes your automated tests resistant to these UI changes and ensures that your automated tests work without having to make changes to the text itself. This also eliminates the automated testing tool from relying on location coordinates to find the control, which is less stable and breaks easily.
#automation-testing-tool #automation-testing #automation-tips #automation-software #automation
With great power comes great responsibility.
More and more organisations are moving towards a DevOps based organisational model, putting more and more responsibility into the hands of the teams delivering software. As part of that change - and the need due to the markets moving faster and faster - more and more organisations are investing into means to release more milestones into production faster. Therefore one of the main goals within these organisations is to automate, audit, secure and ensure correct repeatability of actions.
Barriers to creating a harmonious flow are found in organizations that require more stringent verification methods on their software release mechanisms. One of the more common requirements is that of the four-eyes principle, requiring extra approval controls before release.
Let’s look at defining and implementing the four-eyes principle in a DevOps automation process.
If we look around the world we’ll find the four-eyes principle as an integral part of many business domains. Before we look closer at implementing the solution for this principle, let’s take a look at it’s definition by the United Nations Industrial Development Organization.
_The four-eyes principle means that a certain activity, i.e. a decision, transaction, etc., must be approved by at least two people. This controlling mechanism is used to facilitate delegation of authority and increase transparency. The processes in UNIDO’s new business model are based on the four-eyes principle, which are facilitated by electronic approvals and workflows in the ERP system. This approach not only ensures the efficiency of processes by enabling fast decision-making while ensuring effective control and monitoring, but also brings about cultural change. Staff members are able to perform these processes irrespective whether they are at Headquarters or in the field. _
There are two really interesting (highlighted in bold text) fragments in this definition that we’ll be applying in our implementation example:
Both of these aspects, automated approval using a rule based system and process automation workflows, can be applied to our software DevOps delivery model.
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In this article, we are going to learn about Azure ML and ML Studio. As we know Azure is Microsoft Cloud computing service. And Machine learning supported by Azure is called Azure ML. It’s a complete automated framework to build, teach, train and deploy as a web service and have visual development environment to make it easy for data scientists.
Benefits of having Azure ML as a cloud solution,
Supported Input data types
There are many other data sources; you can check it on the Microsoft portal.
Here I will give an overview of Azure ML Studio. You need to sign up in Azure portal and select Machine Learning Studio to launch it. It opens in the browser and looks like the below image.
It’s workbench software which has predefined protocols to follow while building and training a model. As per the image, the visual workspace enables developers to quickly create models and visualize data with just some clicks.
It has 6 high level navigations menus and those are Projects, Experiments, Web Service, Dataset, Trained Model and Settings.
It lists all projects and models created by users. Project contains combinations of all module experiments and datasets.
It allows developers to build, test and iterate multiple times on either its new model or existing model. You can copy models and do many experiments and get accurate predictive results.
Tested and trained models are deployed as web services as public APIs to use outside of the Azure environment. It predict results based on input parameters. It returns value based on trained deployed model data.
Dataset contains uploaded datasets in Azure ML studio. It lists uploaded datasets and you can also pick from Microsoft sample datasets, which can be utilized for your experiments. You can use big New + buttons to add data files from your local computer.
Save your trained models and experiment for future uses.
Settings tab allows us to view and edit workspace and regenerate authorization token.
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TestProject is a free automation tool that promises to give painless automation experience. It has the feature of record and plays associated with a developer SDK. It also has the capability to build and utilize addons as per need. It is based on automation tools like Appium and Selenium.
Having said that, TestProject removes the complication of maintaining and downloading multiple browser drivers required for testing an application in various platforms and browsers. This is overcome by having an executable file that can run in the majority of browsers and devices.
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