Month: February 2020

Suyash Dubey | Posted on | 2 min Read

5 Best Practices To Avoid Test Automation Failure

Automation testing forms the core of any CI/CD pipeline and enterprises are keen to practice test automation to enhance the efficiency of the development process. Test automation saves resources and reduces the cost of any project in the long run. But there are some important points to keep in mind while testing to avoid automation failure. Let’s have a look at these salient points.

Leverage Parallel Execution

Once you are done automating the test cases, the challenge will be the complex test suites taking a long time to get executed. It affects the quality of the test queue in the test automation framework or IDE. This leads to queue timeout issues and test cases being halted abruptly due to the sequential execution of the test cases.
 
Parallel execution in different test environments is preferred over sequential execution as it saves a lot of time. Although in automated testing, unintended code interactions could happen. This is why you need a thorough reporting mechanism to debug the causes of test failure.
 
Leverage Parallel Execution

Pick The Right Tools

Choosing the right tool for test automation is critical to the success of automation testing. There has to be a set of clear requirements/parameters on the basis of which the tools have to be selected. Some important points that are to be kept in mind while selecting the tool are:
 

  • The team should be clear about the test tool requirements.
  • The testing requirements of the application under test (AUT) should be analyzed thoroughly.
  • The team’s skill set should be accessed accurately.
  • The cost-benefit analysis should be performed to calculate the return on investment.
  • Tool vendor and capability should be evaluated as technical support might be required while using the tools.

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    One tool might not be enough to meet any organization’s automation needs. Also, test automation engineers have to be a part of the tool evaluation process so that they can help in selecting the right set of tools. For example, you can use Appium for test automation but you need pCloudy to perform automation testing on multiple real devices in parallel.

    Analyze The Test Reports

    Test reports provide insights into the underlying issues that are to be resolved. A detailed test report gives an idea of the efficiency of the test automation and the automation team can analyze the report to look for the scope of improvement. While selecting an automation tool you need to make sure that the tool generates test reports to be analyzed by the test automation engineer. There will always be some tests that will fail to execute and it is necessary to analyze the test report to get an understanding of the scenario.

    Test Automation Metrics

    Test automation metrics will help you gauge the quality of the tests performed based on some essential parameters like test duration, unit test coverage, path coverage, number of defects found, percentage of broken builds, etc. The test metrics will give you a clear picture of how well the code is tested. In an agile process, there are frequent iterations to the builds and it becomes important to track the quality of each build. With test automation metrics you can figure out what is the percentage of your tests that passed and what was the reason behind the failed tests.

    Optimum Device Coverage

    Test automation is effective when the tests are executed on multiple devices in parallel. Device coverage is the most prevalent challenge as we have witnessed growing device fragmentation across the world. To ensure the smooth functioning of mobile apps on all the devices, you need to perform automation testing on hundreds of device-OS combinations.
     
    Test automation should be designed to make the app compatible with most of the popular devices. The frequent release of new versions of OS from both Android and iOS is a major factor that drives device fragmentation. The only way to overcome this challenge is by testing the app on a cloud-based testing platform. In pCloudy, you will get the benefit of testing the app on more than 5000 device browser combinations in parallel ensuring optimum device coverage.

    Summing It Up

    Test automation has many benefits like better test coverage, faster feedback, and accelerated results which reduces the time to market of any application. Using the practices mentioned above you can ensure coherent test automation and increased productivity. Apart from these points, there are many other things you can do like writing original code and not copying it as the code taken from other sources might not work in your test environment. But you will always find new roadblocks which you will have to deal with spontaneously.

    Suyash Dubey | Posted on | 2 min Read

    The Scope Of Automation Testing In The Intelligent Digital Mesh

    Intelligent Digital Mesh is the entwining of people, devices, content, and services enabled by digital models, business platforms and a rich, intelligent set of services to support digital business. We have witnessed the implementation of AI in every technology to leverage the benefits of autonomous systems. Enterprises are now focusing on using AI with technologies like blockchain and immersive technology which will create new categories of apps. In this type of environment, attaining optimum device coverage will be essential to ensure quality services. Now let’s understand the fundamentals of the intelligent digital mesh.

    Intelligent

    In the near future, most of the mobile applications and services will use artificial intelligence or machine learning at some level. AI will be the inconspicuous force of most of the popular app categories while creating some new ones. Intelligent apps also create a new intelligent layer between people and systems as seen in enterprise advisors and virtual user assistants. Augmented analytics is also gaining ground and helping enterprises in enhancing business intelligence and data analytics using ML and NLP. Another use of AI and ML is in intelligent things like smart vacuums, drones, autonomous farming vehicles. Intelligent devices are getting smarter to serve better and reduce human dependency to a minimum.
     
    Top 10 Strategic Technology Trends for 2019

    Source: Gartner.com

    Digital

    When we talk about digital, we mean digital twins, cloud to the edge, conversational platforms, and Immersive Experience. A digital twin is a digital representation of real-world objects. It offers information on the state of the counterparts, improves operations and adds value to the operations by responding to the changes. In the near future, all the aspects of human life and the real world will be interconnected with their digital representation capable of advance simulation, analysis, and operation. This combined with immersive technologies like AR, VR, and MR will take extended reality to a new level.

    Mesh

    Mesh is the connection between devices, people, businesses, services, and content to build a digital ecosystem that yields high-quality results. Here mesh refers to technologies like Blockchain, Event-driven, and continuous adaptive risk and trust (CARTA). Enterprises are keen to find new ways to sense the new business events to get the most out of it. A business event can be a change in the status of the deal like finalizing a deal. Using new technologies like AI, it will be easier to detect a business event and analyze it in greater detail.
     
    Security is one of the most important and ever-evolving processes in digital businesses. There is a need to think beyond infrastructure and parameter protection. Continuous adaptive risk and trust assessment is a people-centric security approach that allows for real-time risk and trust-based decision making. New methodologies like DevSecOps and adaptive honeypots should be implemented to strengthen the security of digital businesses.

    Automation Testing For Intelligent Apps

    Intelligent apps are at the core of the intelligent digital mesh. Nowadays most of the apps use artificial intelligence, machine learning or predictive analysis to make suggestions to the customers. The apps use real-time and historical data from user interactions and other sources to predict the needs of their users.
     
    To ensure the quality of apps it is important to test the apps using futuristic tools. Manual testing is just enough and even automation needs to be scalable to get better results. Testing the app on a cloud-based app testing platform is the best choice as you can use as many devices as you want to test your app. Also, parallel testing increases app testing efficiency by multifold.
     
    pCloudy’s AI-powered autonomous testing bot steals the show when it comes to testing intelligent apps. The bot tests the app on real devices with just a single click and generates a detailed report based on the test result.

    Conclusion

    Mobile devices, by and large, are the focal point of most of the innovations that are happening around the intelligent digital mesh. Whether it is Ai driven development, autonomous things or immersive experience, mobile apps still used as a foundation to provide the technology to the masses. But the growing complexities of intelligent apps makes it crucial to implement new methods of app testing. A cloud-based app testing platform like pCloudy is suitable to ensure quality at speed in mobile app testing. The freedom of accessing hundreds of real devices from anywhere at any time and perform manual or automation testing using futuristic features is the correct way to test intelligent apps.