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The test automation landscape has matured, with more than 70% of organizations employing it in their workflows in some capacity. 31% of teams have achieved full automation of their API tests.
As per a Gartner report, here are the top reasons for which organizations look towards automating their testing workflows:
However, this large-scale adoption has also led to specific issues that have become widespread and well-known within the industry. While companies report higher test accuracy after automating their tests, most challenges emerge from its implementation.
Here are some of the challenges faced currently in automation testing:
Modern applications are complicated, often comprising numerous modules and dependencies. Creating and dealing with test cases that cover all scenarios is an overwhelming task.
Software development is a dynamic process. Even minor regulatory adjustments can result in substantial changes to present test cases, resulting in enormous maintenance charges.
Finding unsolvable bugs within the codebase has constantly been a challenge. Manual efforts and even conventional automation can miss bugs which can hamper the application.
Performance testing, especially at scale, is complex. Simulating real-world scenarios with thousands of virtual users can strain resources and infrastructure.
Traditional automated testing often requires manual intervention for test case updates, leading to potential human errors and inefficiencies.
AI is already being applied in test automation, making it easier, faster, and more accurate.
Here are some of the benefits of incorporating AI in automation testing:
Impact Analysis: AI helps discover patterns through the historical analysis of data.
Efficiency gains: AI brings cost efficiency and quality by lowering manual efforts, time, and product costs
Maintenance and Scalability: AI facilitates easier maintenance owing to fewer automation failures and faster executions on large applications
Here are some of the current applications of AI in Automation Testing:
These tackle the challenge of a dynamic codebase, which was discussed earlier. Traditional automated testing tools use specific identifiers to keep track of the components of an application. These components may change as the application is updated, leading to broken identifiers and, hence, false negative results. Self-healing AI tools can identify these errors stemming from the fixed identifiers and resolve them, saving manual intervention efforts.
We are seeing the use of AI bots that can autonomously generate new test cases or report the need to create them. This approach not only accelerates the test case creation process but also ensures that the test suite evolves dynamically in response to changing user behaviors and application features.
These have applications in user interface testing, where the hard-coded locators or accessibility IDs of the elements on the screen may have been altered. Visual locators use computer vision to identify and keep track of these elements through revisions, using technologies such as OCR and other image recognition techniques.
Testing generates a vast quantity of data that must be analyzed to derive insights. AI helps in the detection and categorization of the errors generated during testing. Advanced AI algorithms can identify false negatives and true positives among the test cases. This alleviates the manual efforts of the QA team.
Generating test case data is also a challenge faced in testing. For example, testing a form requires multiple data combinations, strings, numbers, and valid and invalid formats. AI models can create diverse sets of data for a wide range of scenarios and synthesize realistic test data that closely resembles the production data.
A potential use case of AI in automation testing is the crawling of web applications. This was traditionally achieved through automation tools like Selenium. This technique retrieves relevant data such as screenshots and HTML using Beautiful Soup.
After collating the data, we can apply various unsupervised learning algorithms to identify visual regressions or broken elements. We can also employ supervised learning algorithms by feeding HTML labels to perform classification. It can use this data to understand the look, flow, and function of the application, allowing it to identify errors in the test cases.
We are witnessing rapid advancements in the research and adoption of generative AI across the tech sector. Undoubtedly, automation testing also stands to benefit greatly from GenAI integration, thereby alleviating some of the challenges we’ve discussed above.
Experts point to two trends: Both GenAI and low code/no code testing will see major adoption and bring efficiency improvements to the output of QA teams while changing their headcount and daily responsibilities.
Coming to the areas that will be impacted the most, experts predict these top five use cases:
Source: Gartner
By leveraging historical data, these models will be able to predict common bugs and errors in the application, hence detecting potential areas of vulnerability and suggesting pre-emptive fixes.
These models will be able to identify correlations, patterns, and anomalies within the test data, giving comprehensive insights into the quality and performance of the application. This will help QA teams gain a nuanced understanding of the application’s behavior under various conditions.
In addition to bug identification, the AI system can also propose efficient bug fixes, accelerating the debugging process.
A proactive approach to test scenario generation by understanding the relationships between different components and the critical paths within the application.
By automating the analysis of test results, bug reports, and code changes, the AI system can provide timely and actionable feedback to development teams.
As these generative models are adopted into the QA processes, teams must also know their limitations and how to extract their maximum potential.
It is well known by now that LLMs like ChatGPT often fabricate information and pass it off as factual data. Its limitations in comprehending the context and intricacies of complex systems may result in irrelevant or nonsensical tests.
Large transformer models or GANs require substantial computing resources for their training and operation. This will add to the problems of upfront costs that organizations already face with automating processes.
The teams must also be trained on how to understand and interpret these AI-generated tests effectively.
Poor or biased data will result in inaccurate tests and results.
These integrations will also affect the traditional workflows, requiring upskilling and facing resistance from QA testers.
This intersection of AI and automation testing is certainly going to bring a paradigm shift in the way testing is conducted through automated, context-specific tests, reducing manual intervention and vastly improving test coverage. The broader perspective is gaining a better strategic alignment with CI/CD pipelines, enhancing both the software development and delivery & optimization processes.
However, this shift will also require the upskilling of the QA staff, which will affect the team composition and bring a change in their responsibilities. Inculcating the right approach will result in significant benefits to the development processes, hence making automation testing even more valuable in releasing high-quality, robust, and reliable applications.
