Application integrity and user-friendliness have become non-negotiable in today’s competitive marketplace. Six out of ten users abandon online purchases if they have a poor experience.

Comprehensive testing is indispensable to deliver impeccable and resilient software capable of delighting customers and withstanding all environmental shocks.

But conventional, manual testing cannot do a complete job to test today’s complex systems. Orchestrating end-to-end testing takes almost as much time as developing the feature from the ground up. Such lengthy time on quality assurance lengthens the time to market. In today’s fast-paced world, such delays might make the feature redundant very soon.

Enter Artificial Intelligence (AI).

AI increases the scope of test coverage, improves testing accuracy, and speeds up tests. But side-by-side with such positive impacts, there has been concerns about whether AI will make human testers obsolete and lead to massive job losses.

Will AI Testing Automation Lead to Job Losses?

AI automates tests. This leads to the popular perception that AI leads to job losses.

Relentless automation has indeed displaced or will soon displace many of the lower-level testing jobs. AI bots automate several elements of the testing process, until now done by humans.  But AI-driven tools cannot completely replace human software testers. 

AI tools generate test data, execute tests, and analyse results. These tools can identify edge cases and auto-generate unit tests. The algorithms also work through the data to identify bugs and prioritise tests to execute. Automating these processes reduces human involvement and speeds up the end-to-end testing process. 

But AI is not yet capable of performing advanced and complex testing scenarios. For instance, understanding test cases, designing tests, and UI and UX tests still require heavy human involvement. There is a lot of subjectivity in such testing elements that AI cannot fulfill. Likewise, in fast-changing dynamic environments, such as security testing or extreme conditions, human involvement remains indispensable. In most advanced testing scenarios, AI will only be a powerful tool that makes the work of the human tester easy.

There is anyway a crippling skill shortage in many fields, including testers. AI’s automation capabilities will help companies bridge such skill gaps. With AI automating most routine, time-consuming activities, testers can focus on higher-value tasks.

Paradoxically, the advent of AI and digitisation will actually increase the number of human testers. Testers will be much in demand to undertake comprehensive testing of today;s complex applications. They will use AI as a powerful tool to execute high-quality tests in double-quick time.

How AI Will Change Tester Job Profiles

The real impact of AI on testing jobs will be in the nature of the jobs. AI will raise the bar for testers.

The demand for people to complete entry-level work, such as creating tests based on requirements, will reduce. But, the total number of testing jobs might increase since AI increases the scope of testing,

Enhanced Skill Sets

The demand for testers with specific skills will increase.

AI will impact testing jobs in terms of skill sets and knowledge. Until now, programming knowledge sufficed. With AI, human testers will need new competencies. They have to keep abreast of the latest advancements in AI and machine learning. They also need to develop the mentality to unlearn obsolete concepts and commit to proactive change.

Experienced testers who embed AI in their daily work and possess expertise in testing GenAI systems will remain in high demand.

Increased Collaboration

Testers have a big role in developing AI-powered testing systems in the first place. Testers have to work with data scientists and AI engineers to

• Establish benchmarks for the AI system’s intended capabilities. Benchmarks provide clear and objective measures of the testing system’s performance. It enables tracking the performance of the system over time.
• Manage data. The effectiveness of AI testing depends on training the models with the relevant data sets. The onus is on the testers to collect the right data and build robust testing models. Testers need to navigate the data deluge that is common in most digital environments today to identify relevant data. They also need to double-check if the shortlisted training data consists of any customer-sensitive data. If so, testers must be adept at retrieving such information from AI systems.
• Perform penetration testing on genAI-based testing systems. Testers generate malevolent or inappropriate prompts to unearth flaws or errors in the output. In “prompt overflow,” a testing team overloads the AI-based testing system with a large input to disrupt its normal function. Such actions potentially repurpose the AI for unintended outcomes. The underlying purpose is to assess if common attack vectors deployed by hackers can go through. For instance, attackers may manipulate a bot designed for product recommendations. Checking if the bot can execute unauthorised actions if demanded closes such doors.

Monitoring the Integrity of AI Systems

Testers of the future will use AI extensively. AI will become a smart assistant to boost the efficiency, throughput, and quality of testing activities.

But AI, despite its benefits, is still error-prone. For instance, GitHub’s Copilot yields code with security bugs and design flaws 40% of the time.

Testers will have a key role in monitoring, evaluating, and course-correcting AI systems. They have to:

• Identify and mitigate the bias that the AI systems inherit from their training data. Testers have to work with data scientists and developers to undertake fairness and integrity tests.
• Check for drift and degradation. AI systems, even when free from bias, may degrade over time due to changes in data distribution or environmental conditions. Testers in the AI age will have to monitor system performance on a continuous basis. They have to remain on the lookout for drift or degradation and ensure the algorithms always work as intended.
• Check for hallucinations. GenAI systems, at times, indulge in hallucinations or provide wrong answers. This occurs when the algorithms cannot understand the prompt. Such situations occur when the training takes place with insufficient or irrelevant data. Testers running the GenAI system have to be on the lookout for such hallucinations. They have to source appropriate data and tweak the algorithms to avoid such hallucinations.

State-of-the-art tools such as Tricentis Testim allow businesses to develop robust, maintenance-free, self-healing, and resilient tests. These tools leverage AI and predictive modelling capabilities to increase the potency of test automation scripts. Test engineers can leverage these autonomous, no-code tools to record and configure a test in minutes. The test authoring time comes down by a whopping 95% over scripted testing tools and by 50% over other low-code tools.