Autonomous Testing in 2024: Benefits, Use Cases & Challenges

Quality Assurance (QA) which involves thoroughly testing software, is a key factor in software delivery-focused organizations that want to optimize the user experience. However, QA is challenging since user experience takes place across a variety of: 

• Operating systems 
• Vendors 
• Unique software for each device

While software testing is a critical component of QA, it is also regarded as one of the primary constraints of the software delivery process. ¹ Software testing remains prevalent as the global test market exceeded $40 billion in 2020, and it is expected to grow at a 7-12% CAGR between 2021 and 2025.²

Autonomous testing aims to reduce human involvement in the software testing process and reduce the workload for businesses. This article aims to inform readers about this technology by outlining its definition, advantages, and difficulties.

What is autonomous testing?

Autonomous testing is the process of creating, maintaining, and analyzing a series of automated tests (i.e. a test suite) without human assistance. Thus, it can reduce the workload of engineering teams by avoiding the need to write additional code for testing practices. 

Autonomous testing aims to increase test automation to the level of no human intervention; you can see the levels of automation in Figure 1. Autonomous testing can also be seen as similar to codeless automated testing since no coding is required. 

Figure 1: The Six Levels of Testing Autonomy

Source: Medium³

Getting a correct test automation approach depends on an organization’s autonomous capabilities, production environments, and QA team. However, as we have covered above, repetitive manual tasks are proven to be a time-consuming process. Automated testing and automation tools reduce such processes while improving software quality.

Top 5 benefits of autonomous testing

With an autonomous testing approach, businesses can more easily enable continuous testing and continuous integration, which also provides increased test automation. Automation testing is faster than manual testing, thus pushing businesses to adopt automated tasks.⁴

The benefits of autonomous testing are included, but not limited to:

1. Detecting bugs early by implementing impact and root-cause analysis reduces the probability of program failure and contributes to software competency.
2. Higher productivity per developer thanks to reduced human involvement in testing thanks to
   • Automated detection of test scenarios and data.
   • Automated maintenance: When an application has modifications or changes, test scenarios and test automation scripts automatically repair themselves without needing human intervention.
3. Faster testing utilizing scripts for test automation and test scenario creation that are generated automatically. 
4. Contributing to shorter development life cycle due to a series of automation tests.
5. Shorter development life cycle will also shorten the time to market for new digital goods and applications

What is the difference between test automation and autonomous testing?

Test automation and autonomous testing should be taken as complementary practices rather than distinct ones. While test automation is a practice that uses automated testing tools to achieve the desired end result with a record-and-play approach, autonomous testing is a practice that creates and executes tests without human intervention via application blueprints. 

Both practices use Artificial Intelligence (AI) and Machine Learning (ML) technologies. While test automation and autonomous testing both exceed manual testing in terms of automation, what makes the distinction between them is the level of human involvement. Manual testing is human-centered, automation testing is human-assisted, and autonomous testing aims for complete human absence. (see figure 2).

Figure 2: Testing Practices

Autonomous testing can reduce human involvement to almost zero and provide the benefit of not needing a particular skill or expertise in testing because of its autonomous tools. However, test automation provides the ability to focus on specific issues of software and APIs on more than one machine or entity, with better control over testing while still maintaining automation. 

Top 6 use cases of autonomous testing

We can list the following use cases for autonomous testing:

1. User story acceptance criteria are used to generate feature files, test cases, and corresponding automation scripts. 
2. Instantaneous selection of test cases that need to be run in the DevOps pipeline for a specific update. 
3. Failure analysis involves analyzing group test failures and taking the appropriate corrective steps to minimize the need for test execution analysis. 
4. Proactively identifying production problems and producing the necessary test data and automation scripts. 
5. Construct performance models based on real-time application usage from production logs. 
6. Sentiment analysis can be used to identify application features that have received negative customer feedback, and the test coverage can subsequently be strengthened. However, this is a futuristic use case. Currently, most tools are not capable of analyzing written software feedback.

What are the challenges of autonomous testing? 

Challenges of autonomous testing can depend on an organization’s capability, as well as autonomous testing’s way of working:

• If there is an absence of a suitable QA environment, teams in this situation are unable to test using live data, and setting up a staging environment for autonomous testing will be challenging.
• High overhead for maintenance is a concern for small to medium enterprises as teams must update tests with new functionality to test the user experience from end-to-end completely.
• The lack of ability to test negative scenarios in autonomous testing is a drawback.

If you want to know more about testing processes, functional testing tools, automated testing, and API test automation, reach us out:

External Links

• 1. Wayne Ariola. DevOps: Are You Pushing Bugs to Your Clients Faster? https://uploads.pnsqc.org/2015/papers/t-007_Ariola_paper.pdf
• 2. KPMG. Software Testing: Trends Shaping the Industry. https://assets.kpmg/content/dam/kpmg/in/pdf/2022/05/software-testing-trends-india-2022.pdf
• 3. Gil Tayar. Not Only Cars: The Six Levels of Autonomous Testing https://medium.com/@giltayar/not-only-cars-the-six-levels-of-autonomous-testing-a22611ab1d7f
• 4. C. Klammer and R. Ramler, “A Journey from Manual Testing to Automated Test Generation in an Industry Project,” 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), 2017, pp. 591-592, doi: 10.1109/QRS-C.2017.108.https://ieeexplore.ieee.org/abstract/document/8004387