Hyper-automation in Testing/QA
As Artificial Intelligence (AI) and Machine Learning (ML) continue to make their way into the mainstream, their application in business operations has become inevitable. Testing and quality assurance (QA) are no exception to this. These technologies reduce overhead costs and enhance the accuracy of testing processes, while enabling shorter time-to-market. Your business will, therefore, remain unhindered by quality concerns and continue to focus on bringing about superior outcomes.
Integrating smart automation in testing/QA improves processes for software and app trials with little-to-no error. As organizations around the globe continue to embrace digital transformation, you cannot afford to ignore these forward-looking technologies. Only adopting them proactively will enable innovation and accelerated efficiencies going forward.
As emerging technologies like ML are combined with RPA (Robotic Process Automation) and basic process redesign, superior testing automation outcomes are being achieved. These next-generation tools are being leveraged for business process improvements and to eliminate replicable and repetitive tasks. Enhancing customer journeys and simplifying interactions have, therefore, become easier.
Intelligent testing and QA processes involve emulating tasks performed by human testers – and learning to execute them more efficiently. Due to advancements in cognitive technology and deep learning, rule-based automation is resulting in better and faster decision-making capabilities. This has led to improved performance, shorter response times, enhanced customer journeys and efficiencies, as well as lowered operational risks.
Process mining can be used to derive detailed insights about business processes from the data available in its information systems. Through process mining, it is possible to create a streamlined model based on this data. The model-based approach helps organizations implement end-to-end testing practice across business systems. This allows them to achieve the goals of higher test coverage, efficiency in test cases, and alleviated maintenance costs.
Smart algorithms integrate the analytical technologies involved in the product testing lifecycle. As a result, outcomes are assessed automatically, further lowering the involvement of manual workers. Using AI and ML successfully in testing and QA involves identifying and harnessing their strengths to close the gaps, then use these technologies to perform only those tasks.
What Types of Testing Can Be Automated?
Test automation involves the use of various frameworks, such as Data-Driven Automation Framework, Keyword Driven Automation Framework, Modular Automation Framework, Hybrid Automation Framework, Behavior Driven Development Framework, and Linear Scripting Framework.
Depending on your software application, different types of testing can be automated. Some of the most crucial types are as follows:
1. Unit Testing
Here, each unit or component of a web application is tested. While unit tests are written by developers, automation testers can also create them. Unit testing of a web app is conducted during the development stage, and is considered the first level of testing.
2. Smoke Testing
This type of testing helps examine whether or not the deployed build is constant. It helps verify the working of the essential features of an application, and enables testers to carry out further testing.
3. Functional Testing
As the name suggests, this type of testing is carried out determine if the web application is functioning like it is supposed to. Elements included in functional testing include APIs, user interface, security, database, client-server applications, and the overall functionality.
4. Cloud Testing
Cloud testing helps in testing the software for functional as well as non-functional requirements using cloud computing services. This ensures faster availability with scalability, thereby saving time and cost for software testing.
5. Security Testing Security testing
is used to identify threats and potential vulnerabilities in the software application in order to mitigate them and prevent the app from becoming defunct and from exploitation. It also helps in detecting security loopholes. Developers can then fix the problems through coding, and prevent the loss of organization’s data, revenue, and reputation.
6. Integration Testing
Here, the application components are integrated systematically and tested collectively. This type of testing verifies the data communication between the various components of the web app.
7. Regression Testing
This type of testing is conducted to determine whether or not any recent change in code is impacting the existing features of a web app. It verifies if the old code works in the same way as it did before the new changes came into effect.
8. Graphical User Interfaces (GUI)
Testing GUI testing verifies whether or not the functionalities of the web application are working as per specifications. This is done by examining screens and controls like menus, icons, design structure, images, and buttons.
RPA Augments Testing and QA
Technological advancements and process enhancements have triggered the need for transforming Testing and QA processes. The rising demand for shorter time-to-market and high-velocity deployments has made it necessary for organizations to embrace an agile and automated Testing and QA strategy. There is also the need to facilitate speedy releases that uphold high quality standards.
Organizations are increasingly turning to RPA technologies that are powered by AI and ML to usher in a new era of automation. The days of manual testing methodologies are now in the past with RPA implementations taking charge. End-to-end testing is now enabled through tools such as Selenium, Lambda, Ranorex, Sikuli, AutoIT, and so on, which fast-track and streamline testing.
Further, businesses that implement RPA using traditional approaches to automation essentially limit themselves to using structured data for performing repetitive tasks. However, tasks that involve non-repetitive processes and use unstructured data necessitate the availability of cognitive abilities. Thanks to the evolution of Natural Language Processing (NLP) and ML, RPA tools imitate processes and adapt to changing requirements by identifying, understanding, and interpreting user actions through predictive analytics.
When it comes to Testing and QA, RPA typically finds use in customer service processing, data management, data migration, IT support, and back-office operations in industries like healthcare, insurance, and manufacturing. One use of automated Testing and QA is in the claims processing function in the insurance industry. When traditionally performed, this is a manual, time-consuming, and error-prone process. With RPA, these tasks are being carried out using fewer resources and causing minimal errors, while eliminating delays in processing claims.
Software testing processes are poised to become more tool-oriented, with most automated testing and QA procedures being performed with the help of cognitive RPA. This technology now looks beyond repetitive tasks, and performs complex processes by teaching the software to learn the processes over time and adapt to changing requirements. QA tasks that can be automated through RPA include, but aren’t limited to, credential verification, data authentication, UI validation, test data creation, test management automation, and automated bug tracking.
RPA Is a Key Differentiator in Testing and QA
RPA is being increasingly adopted by industries due to its security, integration, as well as data capturing and data migration capabilities. In security testing, for instance, RPA archives each manual and automated action in the audit logs. It can process an array of data formats, interfaces, and systems. Because RPA tools can operate on existing GUIs, data variation is detected in real-time.
Moreover, test data is generated more easily, enhancing the quality of test automation. RPA uses Optical Character Recognition (OCR) technology to distinguish characters in fluctuating display conditions. Implementing RPA with proper planning can prove to be a game changer for organizations. It can mitigate implementation challenges by choosing the right processes to automate. This means repetitive, rule-based, and high-volume tasks will be correctly identified.
Also, decisions related to technology and processes will be made based on organization-specific experiences.
Key Benefits for Organizations
Some of the major benefits of automation in testing and QA processes include:
• Smart prioritization of test cases
• Higher test coverage throughout organizational processes
• More efficient predictive capabilities to identify defects – and recommend solutions
• Automated test environment generation
• Automated test data management
• Quicker root cause analysis
• Faster infrastructure optimization
How Hyper-Botz Can Help You
Hyper-Botz has extensive experience in and understanding of the workings of several industries. We understand that your organization has certain specific needs and are committed to providing you with custom automation solutions that fulfil your requirements.
At Hyper-Botz, our experts will devise strategies that apply AI to QA and testing. This can enable your systems to go beyond test optimization and prediction of failure. We will equip you to prevent failures and reduce not only testing efforts – but dependencies on a human workforce altogether.
Our skilled quality engineers will leverage AI-based frameworks and tools to assess your existing automation frameworks, and develop new ones, if required.
Our intelligent testing and QA solutions come with self-learning capabilities, which make it the preferred solution for detecting critical paths for testing and enhancing accuracy.
With our technology, we will help you augment the output of your mobile and web applications to engender superior user experiences. Our solutions and strategies are built to increase release velocity, reduce testing effort, shorten time to market, and ensure greater returns on your investment.