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In today's fast-paced digital era, the ability of businesses to effectively maintain and update their software is one of the key determinants for their success. This puts significant pressure on IT teams to be highly agile and responsive, resulting in a quick adaption to evolving business requirements.
However, the traditional testing method is often riddled with bottlenecks, including a high volume of tests, siloed automation, a shortage of end-to-end visibility of requirements, etc. Many wealthy enterprises have emerged as frontrunners in next-gen software testing adoption by leveraging artificial intelligence (AI)-based approach that harnesses the power of machine learning (ML) to orchestrate quality across the testing process. As speed and security are top priorities, transforming to Autonomous Testing and zero-touch QA strategy can help enterprises keep pace with the dynamic market and make the delivered software ‘defect-free’.
The guideline in this article will help you leverage enterprise software testing to deliver impeccable software within tight timelines.
Key Concept of Autonomous Testing
Autonomous testing represents a cutting-edge approach to software testing where AI/ML technologies take on the responsibility of creating, driving, and managing tests, eliminating the need for human involvement. This represents a significant advancement in the field of automation testing as AI technologies have continuously advanced to enable seamless integration between humans and machines.
At an enterprise level, this testing practice provides comprehensive control over the end-to-end testing process by utilising intelligent algorithms. This technology illustrates a constant improvement process, leveraging historical test data to adapt and cater to the organisation's specific requirements. Additionally, it excels at conducting integrated testing, effectively assessing the interconnectedness of different codebase components to ensure cohesiveness within complex-structured enterprise software.
One specific aspect of autonomous testing is Autonomous Test Generation (ATG). ATG focuses on the automatic generation of test cases, test data, or test scripts based on certain criteria or algorithms.
Benefits of Autonomous Testing in Enterprise Software Testing
Compared to traditional manual and automated testing methods, this AI-powered testing approach can offer several advantages to enterprise software:
1. Enhanced Test Coverage
Enterprise software has a complex structure, as they accommodate multi-layered workflows and offer a wide range of features; therefore, it demands comprehensive test coverage to ensure all things work as intended.
By leveraging advanced algorithms and techniques to generate relevant test cases automatically, you can execute a large number of test cases with precision and accuracy, providing extensive coverage across different scenarios, configurations, and user journeys. This helps identify defects early and ensures the software performs reliably under various conditions.
2. Time and Cost Efficiency
Autonomous testing takes the acceleration of enterprise software testing to a whole new level, surpassing the efficiency of automation testing by AI-based leaps and bounds. The sophisticated and intricate functionality of enterprise software can be time-consuming to test thoroughly. By automating various testing processes and eliminating the need for manual intervention, autonomous testing reduces the time required for test execution and analysis. It enables faster test cycles, allowing for quicker identification and resolution of software defects.
Moreover, autonomous testing minimises the need for extensive human resources dedicated to testing tasks. The advanced algorithms and machine learning capabilities employed in autonomous testing tools can perform complicated testing scenarios with speed and accuracy, reducing the reliance on large testing teams. This results in cost savings by optimising resource allocation and reducing labour costs associated with manual testing efforts.
3. Improved Accuracy and Reliability
Automation eliminates human errors and ensures consistent test execution, leading to more accurate and reliable results. Test scripts can be executed repeatedly without deviations, reducing the risk of missing critical defects. Besides, ATG follow predefined rules and algorithms to design tests accurately, resulting in reliable and reproducible outcomes.
With machine learning capabilities, autonomous testing tools analyse test results and patterns, identify areas of improvement, and refine test cases accordingly. This iterative learning process enhances the accuracy of future testing iterations and reduces false positives or negatives.
Six Stages from Manual to Autonomous Testing
Since enterprises continue to innovate and embrace digital transformation testing, there is an evolution beyond agile software development and DevOps approaches and shortening software delivery cycles from months or weeks to hours or less.
For organisations that are still reliant on manual testing, they need to adopt the Autonomous Software Testing Model (ASTM) - navigating through six essential stages of human-machine integration - to achieve futuristic software testing practices aligned with the rapid pace of digital advancements.
Level 0: Manual Testing
Humans manually perform all activities and actions, including test generation, test optimisation and test maintenance. Testers are responsible for testing even a single modification added to the enterprise software and writing repetitive code.
Level 1: Assisted Test Automation
At this level of automation, computers provide some level of support to testers, but the main responsibilities and decisions for testing the System Under Testing (SUT) are still in the hands of humans.
Certain testing actions are carried out automatically by the tool and technology, but under the control of the human. This assistance can include a large number of software testing actions, such as supplying facilities in GUI, selecting test cases, and more.
Level 2: Partial Test Automation
At this level, the computer takes on a more involved role, providing in-depth analytic information regarding test cases, source code changes, test coverage metrics, and more, empowering testers to make well-informed testing decisions.
The computer can perform testing actions and generate potential decision options, but may still rely on the decisions and strategies made by testers.
Level 3: Integrated Automated Testing
The key advance of level 3 is the shared control in decision-making between two sides. The automation testing tool has the capability to generate a range of strategies for human testers to select from. Once the decision is made, the tool then automatically executes the required testing actions as commanded by the tester.
Moreover, additional manual tests can be incorporated into the existing test suite. Enterprises can benefit from a balanced approach to software testing, in which automated tests provide repeatability, scalability, and efficiency, while manual tests offer human insight, creativity, and the ability to assess complex scenarios.
Level 4: Intelligent Automated Testing
Compared to level 3, the technology in level 4 is more intelligent in determining the best testing strategy for the enterprise. The tool can also detect and prioritise high-risk areas, optimise test coverage, and suggest improvements to the testing process.
One of the key advantages of the Intelligent Automated Testing approach is dynamically adapting and responding to changing system requirements, evolving user needs, and complex testing scenarios. The intelligent algorithms can learn from previous testing experiences, predict potential anomalies, and adjust the testing approach accordingly.
There is still a need for human validation to improve the recommendations.
Level 5: Autonomous Testing
When reaching level 5, the computer can execute testing ultimately automatically without human intervention. At a high level of autonomy, AI practices are implemented to perform autonomous and intelligent decisions, for instance, about test analysis and report, such as generating test oracles, defect reports and allocation.
Intelligent algorithms can analyse vast amounts of data, execute parallel tests, and enable multiple tests to run simultaneously on different environments.
Use Cases of Autonomous Testing in Enterprise Software Testing Process
Here’re some use cases of autonomous testing:
- User story acceptance criteria serve as the foundation for generating feature files, test cases, and their associated automation scripts.
- Instantaneous choosing test cases that need to be run in the DevOps pipeline for a specific update.
- Utilising model-based testing, AI-based algorithms, and historical data analysis to automatically generate comprehensive test scenarios.
- Failure analysis entails examining collective test failures and taking the proper corrective tests to minimise the requirement for test execution analysis.
- Proactively defining production issues and creating critical test data and automation scripts.
- Build performance models using real-time application usage data obtained from production logs.
- Sentiment analysis can be employed to determine app features that have received negative customer feedback, enabling the strengthening of test coverage. However, this use case is still in the realm of future possibilities, as existing tools often lack the capability to analyze written software feedback.
Final Thoughts
In summary, autonomous testing is an ambitious and forward-looking undertaking that is poised to revolutionise the process of enterprise testing. It empowers enterprises to deliver high-quality software products at a faster pace, with reduced costs and enhanced accuracy.
As technology continues to advance, the adoption of autonomous testing will need to go through 6 stages from ‘zero’ automation to ’fully adopted’ automation. To successfully implement an autonomous testing strategy and take advantage of ATG in your organisation, join our webinar “AI-Powered Testing: Unleashing the Power of Autonomous Test Generation”, in which our expert will share valuable insights into the challenges and limitations of autonomous test generation.
