QA Career Path 101: Conquer the World of Autonomous Testing

The role of QA in today’s software development has evolved with the latest industry trends. Technology is changing at such a rapid pace that it becomes hard to keep pace with it. The latest technologies, such as IoT, Cloud, Blockchain, and more, with something new, always come into the fold. The role of QA was earlier considered as one where the manual test was planned and performed based on the system and documentation requirements, which only happened in the testing phase. As the role has evolved, QA releases products every week or two as part of the continuous delivery process.

Since the adoption of digital transformation has accelerated in the past few years, and per the IDG Research survey, 59% of IT decision-makers say that the pandemic is allowing organizations to evolve and reinvent their digital transformation efforts. At the same time, QA initiatives are gaining significant momentum. The benefits of quality management are compelling – allowing organizations to improve their offerings, optimize processes, reduce overall operating costs, and streamline audit workflows and reporting mechanisms. Digital Transformation and Quality Assurance plans tend to co-exist. You can achieve the benefits of automated quality management using Digital Transformation. This allows you to improve customer satisfaction and overall brand experience. Quality management can be a form of digital transformation. You can consider some of the following real-time scenarios:

• Cloud-Based Quality Management Systems enable companies to guide continuous improvement efforts, define process standards, and drive other quality initiatives.
• Advanced analytics are used to identify process inefficiencies, drive actionable decision-making, and track quality improvement.

QA initiatives are undoubtedly accelerating and improving the digital transformation process. If an organization is pursuing a shift to the cloud, then you can use quality management systems to ensure processes are optimized, risks are analyzed, and compliance obligations are met. QA ensures continuous improvement where you can measure the success of the new technologies that are in place.

Some of the best practices to consider when you are taking a quality approach to digital transformation are given below.

• Embrace Digital-First Mindset
  
  It is all about embracing a digital-first mindset that allows you to make the much-needed transition. You need to evaluate all aspects of your business in terms of digital value. You can customize the same solution for other areas of the business as well. Engage representatives from across the organization to identify how you can achieve digital transformation using QA best practices.
   
• Establish a Design Thinking Mindset
  
  Design thinking works on the people-centric approach, allowing you a unique perspective to address a problem. Before thinking about any transformation, gather all key stakeholders – from end users, business unit heads, and management – to discuss needs, frustrations, goals and brainstorm digital solutions. Digital transformation is a significant step forward that can make or break a business, and it requires incremental steps before any full-blown implementation.

QA provides the foundation to improve essential processes for organizations embarking on the path toward digital transformation. Instead of asking whether QA drives digital transformation or the other way around, we should be asking how to leverage and align digital technologies and quality initiatives to help organizations achieve their strategic goals faster.

The pace of change driven by digital transformation is rapidly increasing, and enterprises need to look at how they approach QA in a better way. Most organizations spend a considerable amount of effort in creating QA tests for applications. The manual testing approach produces very little outcome as it slows down development and overall productivity. You can automate the testing by using test automation scripts at every stage of the test cycle to address the problem of lost productivity. But test automation is not sufficient in the long run as they require human assistance and guidance for test logic and maintenance. Test automation can only reduce test coverage in the long run.

To overcome the challenges faced by manual testing and test automation, there is a pressing need for autonomous software testing to reduce the time and effort spent in creating tests. Autonomous testing is considered a holy grail of test automation and involves the automated creation and maintenance of tests. Autonomous tools will be able to take control of test creation, maintenance, and execution. It allows you to learn from failed tests and make decisions on how new tests should be created and executed when there is a change in the overall conditions. Autonomous software testing is an increasingly popular approach to testing using Artificial Intelligence (AI) and Machine Learning (ML), where machines tend to handle and manage test scripts autonomously without human intervention. These autonomous machines provide the QA team with the capability to expand their test coverage and increase the speed of application releases.

Autonomous testing is changing the way QA teams work and deliver. IT leaders may look at the current automated testing reality and shift their focus toward autonomous testing. The emerging trends in autonomous testing include:

• Rise of automated “Testing as a Service” platforms that are powered by AI.
• Test automation solutions eventually mature to become Robotic Process Automation (RPA) types of solutions.

These are the different testing phases that have evolved over the years:

• Manual Testing
• Assisted Testing
• Partial Automation
• Full Automation
• Unassisted Automation (Autonomous Testing)

One of the major advantages of Autonomous Testing is that you do not need extensive training to perform these tests. The tests are performed by specialized machines, which means that human intervention is minimal. You do not need skilled testing professionals when you are managing and handling autonomous capabilities.

Following are some of the other benefits related to Autonomous Testing:

• Accelerated Testing
  
  Autonomous testing allows you to continuously generate test scenarios and test automation scripts. You can easily manage them as your code evolves, saving a significant amount of time for the project teams.
   
• Improved Productivity
   
  Autonomous testing empowers teams to improve their existing development life cycle, thus helping them to deliver their product releases faster and reducing the time to market.
   
• Improved System Versatility
   
  The system becomes more versatile when it can easily adapt itself to the changes in the product because of continuous learning algorithms. With time, autonomous testing systems can deliver better and best test scenarios.
   
• Cost-Effectiveness
   
  With enterprises needing fewer resources to deliver their testing requirements, we can expect a high level of productivity per resource on an organizational level, which indicates the cost-effectiveness factor.
   
• Competitive Edge
   
  It is all about gaining an edge over the rest of your competitors. This helps your organization to gain the next level of growth and innovation. QA teams can focus on critical tasks while developers can gain instant feedback for their builds.

Data Trends on Implementation Rate of Autonomous Testing

Autonomous Testing is the next game-changing strategy for many leading IT organizations in years to come. It has gained interest among the testing community and will show prolific growth in the coming years. As per the Omnia Report 2021, autonomous testing will be adopted by most organizations in 2022. Almost 90% of all the surveyed companies will have fully deployed autonomous testing by 2024.

Here are the huge growth prospects offered by the autonomous testing market:

Autonomous testing is changing the way organizations test and deliver their product requirements. Autonomous testing helps you to create a test suite autonomously without too much human effort and intervention. This is the very first step for successful automation if you are trying to shift your focus when it comes to automation testing.

Autonomous testing can help the testing teams to make faster and more informed decisions when it comes to choosing a scenario to test under various circumstances. You can seamlessly perform automation by leveraging modern AI/ML techniques. This means that you can rely on modern cutting-edge technologies for your automation requirements.

Autonomous testing provides self-healing test scripts and test scenarios in case there are changes at the application level. You can automate your test requirements, thus helping you to avoid maintenance issues with the change in scope at the application level.

With DevOps and an iterative development approach, autonomous testing plays a significant role in ensuring that automation testing is implemented right from the beginning of the SDLC process. With automation, enterprises can reduce manual efforts and scale up their business by focusing on innovation and adapting to newer business and IT requirements. Autonomous testing is a step ahead when compared to automation; in fact, you can use autonomous testing to seamlessly achieve the next level of test automation.

There are some challenges that organizations can encounter when they are implementing autonomous testing in the existing setup:

• In the absence of a suitable QA environment, teams can face challenges in testing their requirements using live data. Also, you cannot set up a staging environment in a quick time if the live data is not available.
• Small-to-medium enterprises can face high maintenance overheads when teams need to update their tests with new functionality for testing out the user experience.
• The lack of ability to test negative scenarios is one of the major drawbacks which teams encounter.

As companies are looking to change the way they operate, they are increasingly embracing test automation. AI might enable an even more predictive form of testing that will redefine the way QA teams evolve and grow. AI and ML can take test automation to the next level in the form of autonomous testing as QA teams learn the technical concepts related to test automation, especially when it comes to writing scripts. Autonomous testing can empower teams to be more strategic in their efforts.

With conventional automation technology, testers must spend a considerable amount of time and effort learning how to create test scripts for each test scenario. With autonomous testing, testers can spend more time working on specialized tools and contributing to QA management initiatives, which are great for business. Autonomous testing helps QA teams to tackle critical objectives around bringing AI into the organization, which is beneficial to the customers in the long run. You can also rely on autonomous testing tools for delivering your testing requirements. With time as these autonomous tools mature, you can use their capabilities to enable testing teams to spend more time on the non-functional aspects of a project, such as performance and security.

The software development journey and the way software is delivered completely changed after every single Agile came into the picture. Before the agile approach, the software was delivered on a monthly or bi-monthly basis, which is a thing of the past. To keep pace with the latest technology trends, the world is moving toward the direction of digital transformation. We need to anticipate the market requirements well in advance and develop a system that is scalable and predictive for handling future trends. Hence, it is all about adopting autonomous testing, where the role of AI becomes important in the way QA operates in organizations.

Autonomous testing helps to overcome the following testing challenges:

• Siloed Automation
• The absence of end-to-end requirement visibility
• The high volume of testing

Autonomous testing provides the following benefits from a QA perspective, changing the way organizations operate:

• It helps to accelerate the overall testing process by identifying potential issues or gaps right from the beginning.
• It allows the software to automatically generate a test suite with test data and analyzes software output to spot any errors or bugs. These errors or bugs are hard to be identified when you are working with traditional functional tests.
• QA teams can work with AI testing tools to supplement their normal testing efforts. This helps to improve overall team productivity in the long run.

Autonomous testing has completely changed the QA dynamics. QA teams have moved beyond the traditional ways of working and started delivering quality outcomes quickly. You do not require any specific skills or expertise when it comes to autonomous testing; hence, the QA teams can be productive in a short period without having to worry about technical skill sets.

Autonomous Testing is used to optimize QA-related activities using the following ways:

• Effective Test Execution and Management
   
  Autonomous Testing allows you to create different test cases covering all the possible scenarios leading to better test coverage. The testing process is improvised, as autonomous testing can generate a large volume of varied test data for multiple test conditions. This results in saving a lot of time and effort in generating and maintaining test cases as well as test data.
   
• Unique Test Automation Strategy
   
  Autonomous testing can easily identify, understand, and analyze frequent changes done to the software product resulting in self-healing. You can execute mini-test suites instead of executing the entire test suite allowing you to optimize your QA strategy.
   
• Smart Reporting Mechanism
   
  Quick feedback and comprehensive reporting allow you to resolve your issues during the early stages saving time and resources. Autonomous testing ensures that you optimize your QA processes when you can make more informed testing decisions considering the different issues and dependencies.

With releases delivered every week and software updates being rolled out on almost every alternate day, there is a growing need to streamline software testing by making it smarter and more effective. This is what can be achieved using Autonomous Testing leveraging AI and ML as the latest cutting-edge technologies.

This can be achieved using the following functionalities:

• Automated Visual Testing for UI
   
  You need to perform visual validation to ensure that the UI is appearing correctly to the users. Using AI, it is ensured that UI appears correctly to all the users and that UI elements are not overlapped. The AI and ML tests can automatically detect all the visual bugs that help you to validate the visual correctness of different apps and websites.
   
• API Testing
   
  APIs are an integral component of a development setup. Creating applicable scenarios to test API can be challenging since it requires a solid understanding of the API. You can use AI-driven test automation as AI helps you to identify patterns and connections between API calls and can group the required scenarios that help you to deliver adequate test coverage.
   
• Test Maintenance
   
  The stability of the apps can be impacted by frequent changes and updates. When the changes are made directly to the app in the form of new screens, buttons, or changes in the user flow, the static test scripts tend to fail to adapt the changes, thus resulting in a test or build failures. Using AI and ML, you can fix the test scripts using self-healing capabilities, reducing the noise related to testing failures.
   
• Test Data Generation
   
  You need appropriate and correct data for performing robust testing. Manually creating the data can be time-consuming and be also error-prone. With AI, data can be generated easily and can be used to test different scenarios.

As a professional, it becomes important to learn the basics related to Autonomous Testing. This is where you need to upgrade your skill set so that you can learn and evolve. There are a lot of online learning platforms that offer specialized courses. One such prominent platform is Careervira, which provides best-in-class learning courses that you just cannot afford to miss. If you are a beginner or an experienced professional that loves to upskill, then you should check the wide range of courses offered by this leading online learning platform.

Here is the list of courses you can go through that help you understand Autonomous Testing a lot better:

• Start Your API Testing Journey with Postman Tool
   
  This online self-paced course is best suited if you are looking to kickstart your API Testing journey using Postman as a specialized tool. You can create faster and better APIs to streamline your autonomous testing processes. This course will be more beneficial if you have understood the overall concepts and fundamentals related to APIs and their overall system behavior. The testing professionals who are beginners have rated this course highly in terms of content and overall execution.
   
• Cypress: End-to-end JavaScript Testing
   
  This course helps to understand end-to-end JavaScript testing for your web applications. You will also learn how testing tools like Cypress can be effectively used to attain the required testing outcomes. You have different case studies and capstone projects along with the modular learning approach so that you can understand the industry practices and handle real-world technical problems with utmost ease and perfection.
   
• Master’s Certification Program in Autonomous Vehicles
   
  There will always be challenges working in the Autonomous domain since it is new and evolving. This is where you can rely on a Master Certification Program when it comes to autonomous vehicles, which allows you to handle a different set of requirements in this domain. This course is designed keeping in mind the future market trends and evolving technology trends. If you are passionate about Autonomous Vehicles and are looking to make a transition, then this course will help you to make an instant impact.
   
• MCA with Specialization in Machine Learning
   
  Autonomous testing is driven by the latest technology trends. ML is one such technology that you need to master if you are trying to implement and execute autonomous testing in the existing setup. This is a specialized course that spans 2 years and is divided into 4 semesters. If you are a professional who is a fresh graduate or in the final year of college, then this course will help you understand MCA along with the specialized ML concepts helping you to grow in the long run.
   
• Artificial Intelligence Course Online
   
  AI plays a crucial role when you are looking to improvise or implement Autonomous Testing processes. This online course allows you to upskill and expand your technical capabilities when it comes to AI. You will understand different concepts such as data handling, pre-processing, model selection, and model evaluation, which form the core of AI.
   
• AI Workflow: AI in Production
   
  You have mastered the art of Autonomous Testing if you are aware of the AI workflows that are in place. This course provides you with the right guidance when it comes to understanding Data Science and AI. The in-depth knowledge related to Data Science can go a long way in helping you make a transition into this field.

We have discussed how Autonomous Testing can be beneficial to the testing teams working in close collaboration and will redefine the way testing results are delivered. Autonomous testing is backed by the latest cutting-edge technologies, such as AI and ML, which are getting increasingly popular and is the future of technology innovation. Machines are fed with historical data and intelligent algorithms due to autonomous testing capabilities allowing you to create new test scripts without any human interaction. Autonomous testing will allow QA teams to speed up their delivery processes, reducing the time to market.

The future of autonomous testing in QA will largely depend on the following factors:

• Adopting and implementing autonomous testing processes and strategies
• Making a transition from a traditional to an autonomous testing approach
• Skillset and expertise of QA analysts

Autonomous testing is here to stay for years to come, which indicates that organizations are trying to evolve and re-invent with the rapid changes in technology trends.

Here are the top 5 benefits offered by Autonomous Testing for the longer run:

• You can detect bugs during the early stages by implementing in-depth analysis, which reduces the probability of your software application failure.
• The productivity of the development and testing team improves drastically since human intervention is reduced during the testing phase.
• The testing can be done much faster when you are using scripts for test automation and test scenario creation.
• The development cycle is shorter due to the smart testing capabilities offered by Autonomous Testing.
• You can shorten the time to market a product for new digital goods and applications. This can help you to gain an edge over your competitors.

There has been a recent shift in the testing industry with the introduction of autonomous testing. AI-based technologies continue to evolve and become more advanced, ensuring a promising era of autonomous testing awaits in the years to come. There are leading software brands, such as Google, Amazon, Microsoft, etc., that have implemented different use cases related to autonomous testing. It is expected that more and more software companies that rely on AI and ML will be implementing autonomous testing soon. It remains to be seen how the autonomous testing domain shapes up.

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