Top 10 Automation Tester Skills to Build a Successful Software Career

The main responsibilities of automation testers include the following:

• They brainstorm and understand product specifications and expected user behavior.
• They develop test scenarios, test strategies, and test plans for the product in question.
• They automate functional test cases, perform coverage analysis, and improve test coverage during the product development phase.
• They find defects through White-box testing during the coding phase.
• They define quality gates and release criteria.
• They perform functional and integration testing and identify and log defects prior to product release.
• They perform all basic testing such as accessibility, performance, security, internationalization, etc.
•  They automate and run tests at all entry points (across browsers, devices, operating systems, etc.)
• They perform daily/weekly regression testing for product changes, analyze the errors, and log the bugs.
• They implement and integrate continuous testing into the production pipeline.

According to a Katalon Studio “State of Quality Reports 2022 survey,” 63% of QA teams surveyed reported that investment in automation had good ROI. Testing has shown good cost efficiency in projects.

As the product landscape changes, so does the role of the tester. During the waterfall model, testers had ample time to write test specifications, create automated tests, and increase the quality and quantity of tests over a period of time. In the agile world, the product life cycle is shortened. This results in the need to automate the test cases very quickly and include high-quality test automation as a part of continuous integration.

With the advent of the “Shift Left” approach, testers will start writing test cases as soon as requirements are clear, and development will continue in parallel with testing. The “Shift Right” approach focuses on testing, monitoring, and updating the app in production environments.

I will share my journey as a Software Design Engineer in Test at Microsoft to help understand the transition. In the early 2000s, we took the “shift left” approach. I worked on an enterprise data backup and recovery product. I started with the requirements phase of the product and created the test specifications. We had ample time for innovation and automation from scratch. For example, daily automation with real storage libraries was expensive because tapes wear out faster. I developed a stub called Library Simulator that behaved like a real storage library agent.

Later, I worked on testing the backup/restore feature of Windows 7. In order to ship our feature, we had to write reliable, faster regression tests with >90% coverage. Before releasing a new bug fix or patch in Windows, all the tests across Windows features would be executed.

When I moved to the Bing Search Engine team, it was an agile environment. We accelerated from a 6-month release cycle to daily releases. We needed to do more White-box testing by understanding and sifting through the product code. We used internal automation tools augmented by image processing techniques to find component-specific issues faster. We also helped developers write basic functional tests and check in with the code. At each check-in, these tests would automatically confirm that existing functionality was not broken. We also followed the “shift right” approach to testing, that is, “testing in production.” 

The “shift right” approach is one of the latest trends in test automation that could become more prevalent in the years to come. 

• Due to faster product cycles, the demand for no-code or low-code tools is increasing. 
• To test the functionality of UX, we used to have to run cross-browser and cross-device tests, which was very time-consuming. With recent advances in Selenium and cloud automation, we can now automate these tests and run them quickly. 
• Many companies are also using AI in their automation frameworks. 
• The latest products are SaaS-based and are also very data intensive. This requires the automation tester to develop new capabilities such as app testing, API testing, data quality testing, and AI testing.

Cloud-Based Cross-Browser Testing

The biggest challenge in web application/mobile application testing is cross-browser and cross-device testing. The product may work well in one browser but not in another. Cross-browser testing is very time-consuming as we have to manually check it across different browsers or write different automation codes. It also incurs high infrastructure management costs as we have to store and maintain all variants of devices/browsers.

Cloud-based cross-browser testing tools make all devices and browsers connected to the lab available. They provide many useful features, such as:

• Provision of unified automation code
• Access to devices from anywhere
• Ability to take screenshots during testing
• Visual comparison of screenshots across different devices

Some of the most popular tools, such as Lambda Test, Selenium Box, and Test Complete, provide these features. Selenium and Cypress are called out as the most required skill in the automation job descriptions.

Mobile Automation Testing

Testing the product/application on mobile is one of the most challenging aspects as it has to support the execution of tests on different browser and OS combinations. Sometimes running on emulators can give different results than running on real devices. Frameworks like Appium are very popular for mobile testing. They support cross-device support and can also support automation in multiple languages. Recently, there have also been innovations in mobile automation that support AI and scriptless automation.

Kobiton supports record and playback methods for creating automated tests. It supports cross-device testing and integration with Continuous Integration and Continuous Delivery (CI/CD) pipelines. The Mobot tool uses mechanical robots and computer vision (AI image understanding technology) to execute tests without code consistently.

Katalon supports both web and mobile automation. It supports low-code script automation.

Zero-Code or No-Code Solutions

The need for no-code/low-code automation frameworks is on the rise. These solutions use less or no code to create test automation. The tools could use plain text/drag and drop/record and playback features to reduce automation development time.

The most widely used and popular UX automation tool, Selenium, offers both coded and codeless automation versions. Users can drag and drop to create automation tests. Using coding, maintainable tests could be written with minimal code changes. The catch with these solutions is that these tests must be recreated every time the product/UX is changed. Therefore, we need a self-healing feature supported in these frameworks.

Some tools that support the no-code/low-code automation and are in demand as popular automation testing skills are listed below:

• The Cucumber tool would write tests in English-like language. Any person without specific programming knowledge can create tests.
• Katalon Studio helps the user in creating tests with the help of the drag-and-drop feature. This drag-and-drop functionality helps create tests automatically.
• Tools like Virtuoso use AI to detect UI objects and simplify automation development automatically

API Testing

Application Programmable Interface (API) can be called independently for specific functionality. Products can also use APIs to integrate with UX. The product can also be delivered as a standalone API. API testing involves passing various parameters and checking the expected behavior. API tests include setting up a test environment, setting up the database with state, passing the correct set of parameters, and validating the response.

• The Load Mill tool uses AI to generate API tests automatically.
• The Postman tool is used for standalone API tests.
• ACCELQ helps automate UX and API tests in a continuous cloud-based platform.

Dev Test Ops

As DevOps is popular for building end-to-end CI/CD pipelines, it is important to integrate continuous testing into this pipeline. Automation tests run as part of the deployment process, which helps us to detect bugs upfront. There are multiple automation frameworks designed for this purpose. 

Teststigma is a complete automation framework that runs tests in the cloud. It is powered by AI, and hence, tests can be employed in English. It is integrated with collaboration tools like Jenkins and Jira, which would be helpful for CI/CD. Similarly, Katalon Studio is a comprehensive solution for continuous testing in CI/CD pipelines. It supports integration with tools like Jira, Jenkins, Azure, and CircleCI. 

Most companies expect automation testers to understand continuous integration and get familiar with tools like Jira and Jenkins.

DataOps

As products become more data-heavy and data-centric, it is important to test data quality in data pipelines. DataOps validation includes data validation, data consistency check, data integrity, and data governance.

The QuerySurge tool is an intelligent test automation solution that automates data validation of Big Data and ETL pipelines.

The Talend tool identifies data quality issues, detects anomalies, and identifies patterns. It also enriches data with external resources. It would also mask data to meet compliance/privacy requirements.

Non-Functional Automation

End-to-end testing of a product involves testing multiple aspects of the product in addition to pure functionality. Some of the key testing dimensions are mentioned here. Integrating the automation of all these aspects becomes important in the Agile world.

• Accessibility Testing: Accessibility testing involves testing whether the product can be used by everyone, including the specially-abled. Accessibility testing tools should consist of speaker software, speech recognition software, etc.
• Security Testing: Cyber-attacks are increasing at a rapid pace these days. Security testing would proactively reveal security vulnerabilities in the product. Vega is a Java-based security scanner and testing tool for web applications. ImmuniWeb is a cloud security and compliance tool.
• Internationalization Testing: It is important to ensure the product can be used in all international markets and languages. Internationalization testing helps identify errors in shipping the product internationally. Localization testing verifies that the product works in different regions.
• Performance Testing: Performance testing measures user response time for various functions of the product. Latency issues between the product’s components are identified.
• Firmware Testing: Firmware testing tests whether the firmware design meets the functional specifications.
• Reliability Testing: Reliability testing tests whether the product operates reliably over a specified period of time.
• Compliance Testing: Compliance testing ensures that the product meets the compliance rules.
• Scalability Testing: This test verifies that the product meets scalability requirements, such as increased traffic, increased number of transactions, or increased catalog size.
• Stress Testing: This tests product behavior under stressful conditions like row resources. Tools such as LoadRunner are useful for stress testing.
• Performance and Load Testing: JMeter is used for performance and load testing for web applications.

Synthetic Data

As applications become increasingly data-driven, it is important to test the product for different data fields. Long text or different data values can crash UX. Synthetic data generation comes to the rescue at these times. For example, we created regression tests for Bing search UX for event data behavior. But the real events expired after a certain time, and the tests failed. We created automation using dummy injected events that never expire and contain variations of data. 

Today, some testing frameworks use AI to generate synthetic data. Synthetic data is useful for handling edge cases and also in cases where real data cannot be accessed for privacy reasons. Synthetic data can also be used for stress testing.

We can use Tonic.ai to reference different databases and data types. The synthetic AI data generator usually produces statistically and structurally similar data to the real sample data.

Cloud Testing

Testing in the cloud is becoming increasingly important as many industries move their infrastructure to the cloud. In the coming years, we will see an increase in multi-cloud and multi-premise environments. With cloud testing, companies would test the scalability, performance, multi-tenancy, security, disaster recovery, and reliability of their products.

There are many tools available for functional testing in the cloud.

• APP Perfect develops various monitoring products for monitoring and analyzing web-based applications.
• The SOASTA Cloud Test Tool can be used to test the performance and scalability of small- to medium-sized cloud-based web and mobile applications.
• LoadStorm and JMeter can be used for performance/load testing.
• Nessus is a security tool for identifying security vulnerabilities.

Hyper-Automation Testing

Hyper-automation intelligently automates any repetitive manual task. This can be any task, such as accounting, lead generation, or customer service. Hyper-automation uses the following technologies to accomplish a specific task:

• Artificial Intelligence/Machine Learning: This is the science of learning, perceiving, and inferring information through machines.
• Robotic Process Automation: It is a form of business process management used to manage software robots.
• Natural Language Processing (NLP): This part deals with understanding and processing natural language.
• Optical Character Recognition (OCR): This software recognizes text and formats from an image.
• Digital Twin of an Organization (DTO): This term is an extension of the digital twin. DTO is the digital twin of a process, product, or organization.

Let us take an example; the company needs to approve an employee’s travel expenses. Hyper-automation would use OCR on the employee’s invoices, extract the information, and validate it against the company’s compliance rules. If the compliance rules are met, the payment is approved. In cases where the system is not sure whether the rules are met, it passes the invoice to a human to review.

Deloitte’s company used IBM’s Robotic Process Automation solution for its travel approval process. It reduced the time it took to create approval reports from 3 hours to 10 minutes.

This is just one example to demonstrate the power of hyper-automation. This field opens up avenues for automation for tasks that were never imagined. Robotic Process Automation is one of the automation testing key skills in demand in 2023.

The Capgemini World Quality Report provides a detailed overview of the automation status in each sector. Key excerpts from the report are reproduced here:

• In the automotive sector, testing of end-to-end systems (mechanical and software) is important. Traditional automotive sectors and EV startups should maintain the quality of new complex ecosystems.
• In the consumer goods and retail sectors, automation needs to be integrated into the agile ecosystem.
• In the energy industry, many new technologies are being deployed, such as the installation of sensors and the creation of digital twins complemented with augmented reality/virtual reality solutions. Building automated monitoring and sustainable solutions is the key requirement here.
• Mobile banking, blockchain, and digital payments are the latest technological developments in the financial sector. In the financial sector, the focus of automation is shifting from text-only execution to test lifecycle management. In finance teams, the focus has been on functional automation. In addition to functional testing, performance testing (to handle many transactions) and security testing are two important aspects of financial services automation.
• In healthcare, there are many technological advances due to COVID. The quality team will focus heavily on satisfying customers. Patient data protection is also very important here. Therefore, the focus is on generating and testing synthetic data. The testing team will also need to use DevOps and agile methodologies. Companies could also look for automation testers with specific knowledge in healthcare.
• In the manufacturing segment, automation includes automating the physical manufacturing process. Some manufacturing industries are exploring digital twins, quantum computing, and metaverse for their solutions. Testing these new technologies will be a challenge for the quality team.
• In the public sector, governments are moving their data to the cloud. Data privacy and security are very important here. The public sector is also going through an agile transformation. Quality teams also need to follow this trend. Data privacy, ethics, and sustainability are important quality aspects in this sector. Providing an easier interface for the user and getting tasks done would be the most important quality objective in this sector.
• In the telecom sector, scope management and data protection are key. There is also a move from the waterfall model to the agile model. Besides functional testing, security and performance testing are the most important test areas.