Autonomous Testing as the future of software testing

Introduction

With the ever-growing race toward a digital transformation, IT organizations are focusing more on the continuous evolution of their agile software development and testing practices and attempting to move towards the highest level of automation. With the availability of a wide variety of automation tools, there has been a boom in Test Automation. However, organizations still face gaps and barriers in several areas, such as automated failure analysis and corrective actions. The concept of autonomous software testing is the future of software testing that promises to drastically reduce the manual intervention and delivery cycles from weeks to hours or even less.

What is Autonomous Testing?

Autonomous Testing refers to creating the software tests autonomously, maintaining the tests, and analyzing the resulting output without needing the user to take any action. Autonomous software testing is the future of software testing that allows the user to create various tests without writing any code, also called script-less Testing. It saves the engineering teams from writing hundreds and thousands of lines of code, thus reducing the workload.

This automation is backed by Artificial Intelligence (AI) and Machine Learning (ML) based approach, which is getting increasingly popular. Machines are fed with historical data and intelligent algorithms that enable them to manage the tests automatically based on past interactions and create new test scripts without any human interaction. In a way, autonomous Testing helps the QA engineers create test cases by making predictions using minimal inputs. This further allows the QA teams to speed up the application releases and reduce the time-to-market.

Transformation to Autonomous Testing

Adopting and implementing autonomous Testing gradually moves from manual to semi-assisted and then to a fully automated testing mode. Organizations adopt and integrate some automation in their software testing lifecycle at each stage.

1. Manual Testing : Traditionally, the software testing process involves writing test cases in a simple spreadsheet or specific dedicated tools to create a list of inputs and outputs scenarios. Then, a team of testers would run these tests, feeding the information to the tested software applications and validating the generated output. Writing the test cases requires a thorough understanding of the application and the goals to be achieved with it. Manual Testing stands as the initial stage for autonomous Testing. The tester’s knowledge and intelligence are used to train the machine for developing scenarios that function as the initial feed for the machine. This sets the stage for defining the scope of the automation process and making the system familiar with the requirements.
2. Assistance-supported Testing : Once the test cases are written, the next step is to identify how to automate the test case execution. Some parts of the Testing can be automated using Automation testing, but most areas require the intervention of humans for general management and quality assurance. It also involves the identification of additional requirements, like correctly defining the objects to be evaluated, which further enables the creation of tests for cross-platform Testing.
3. Semi-automated Testing : This stage involves big, automated tasks across the testing lifecycle, each having a well-structured test block with defined test logic and success or failure criteria. These are based on industry best practices and can be run independently. This also comprises a continuous feedback and loopback system to ensure agility. QA teams can run tests for multiple scenarios with quick verification and user accessibility options. Although, human intervention is still relevant for the overall management and quality assurance and makes fine tunings for any failure or overall performance of the application.
4. Automated Testing : Test automation tools take a front seat across the entire testing pipeline at this stage. Support for automated Testing in distributed environments enables the development of complex testing procedures. Availability of source control and version control mechanisms provides the testing teams with better management and control over the entire testing environment. However, the involvement of testing teams is still essential for maintaining and finetuning the practical details of the testing environment, like ensuring the application’s performance against any bugs or failures.
5. Unattended Testing : The next level aims to eliminate the need for the testing team’s involvement in the end-to-end process. At this stage, ML gets involved in helping automate the tasks like regular monitoring of test results and making minor modifications in the tests based on the changes in the test scenarios. Intelligent tools get trained daily to understand the requirements and make their own decisions for any anomalies in the test output or bugs. In addition, QA teams can start working with AI-enabled test script creation and self-healing systems.
6. Autonomous Testing : This entire practice aims to attain an autonomous level in the testing lifecycle by leveraging upon the assets and artifacts built across all the previous stages. The automation mechanism is capable enough to learn from past failures and adjust the tests according to any changes in the inputs or suggestions in the feedback cycle. The entire testing pipeline is managed autonomously, needing no information from the QA Teams, leaving them ample time to work on new projects.

Test Automation vs. Autonomous Testing

A few decades ago, as the world adopted a dynamic, agile software development model over the status waterfall model, the size and scope of software applications evolved drastically, leading to complex architecture. Engineering teams felt the need for better testing tools to accelerate the software testing processes. This desire to automate the test case execution leads to the evolution of automated testing tools to better manage test data, execute the tests, and analyze the results. These automated testing tools could mimic the click of a mouse or keystrokes on a keyboard using some scripts or programmatic code, thus accelerating the areas involving monotonous tasks. Selenium is one such tool that is widely used for test automation.

Usually, a test automation framework includes a set of automation testing tools and standard practices with rules and guidelines for all stages of software development. Organizations incorporate both automation tools and best practices in practicing test automation, which helps fasten the testing process, reduce cost, and improve overall efficiency.

However, with the gradual evolution of the software development process, engineering teams started finding some shortcomings in the test automation processes. They realized that writing test automation scripts require skilled professionals with domain expertise. Finding professionals who understand their business requirements and proficiency in the language of programming needed became a cumbersome task. And even after writing perfect automation scripts for any phase, organizations had to depend either on the same person or someone with a similar set of skills for making any further updates in the scripts. Moreover, debugging test scripts is also a significant challenge. With rapid changes in the business applications, QA testers are often required to update or improvise the test scripts, which further demands deep application knowledge and the proper skill set. Furthermore, test engineers need to spend considerable time coding the test scripts when performing data-driven Testing.

Several testing automation tools are based on playback methods or replaying the exact record steps multiple times. Making minor changes in such scripts and tools requires the organization to replay and re-record the entire sequence; it is often time-consuming. Maintaining the test data becomes problematic when the test script involves multiple windows and screens. Depending upon the size or complexity, any changes in one application area would require complex iterations across other screens, thus increasing the complexity exponentially.

Benefits of Autonomous Testing

Autonomous Testing has several advantages over the traditional test automation frameworks. While writing test automation scripts requires the QA testers to write the code for creating tests, Autonomous Testing enables the creation of an entire test suite, including test scenarios and automation scripts, faster, without any human intervention. Moreover, it often includes test case design and test execution as well. Autonomous Testing accelerates choosing various scenarios for the test under different environments and circumstances. Automated identification of test scenarios and good test data further saves cost and time. It increases overall productivity and efficiency while reducing the broad timespan of software development and time-to-market.

Autonomous Testing leverages modern AI/ML techniques to automate the test case selection and significantly reduce the testing time. It also detects bugs early using the root cause analysis, thus reducing the software failure risk. The test scenarios get updated and adjusted automatically when the application changes, thus providing auto-alignment capabilities. Unlike test automation, the Autonomous software testing process does not require any specific skill set or expertise, thus reducing the dependency on skill sets and increasing the productivity of resources.

Introducing Autonomous Testing Services

To achieve the goal of Autonomous Testing, you need optimized automation testing services. Hexaware has built a unified platform called Autonomous Test Orchestration Platform or ATOP. Its plug-and-play architecture allows DevOps to operate at the highest level of test automation maturity by addressing all the possible use case scenarios per industry needs. It comprises 205 use case scenarios, covering a wide range of personas and types of Testing. These scenarios can address all requirements in an automated fashion, eventually making the software testing process independent of human intervention.

ATOP integrates with all effective application lifecycle management tools, application performance management tools, log aggregators, defect management tools, and app stores available in the market. Organizations can deploy these services in the following modes:

• Out of the box (OOB): ATOP will be deployed with Hexaware’s automation, test data management, performance, and security testing solutions
• Custom: ATOP is integrated with the existing solutions for automation, test data management, performance, and security testing, as necessary

For more information, please visit Hexaware Autonomous Testing Services