Integrating Human Intelligence In AI Testing
Mav Turner, chief strategy and product officer of Tricentis, dispels fears of AI replacing human testers in DevOps, emphasizing the growing importance of human-led testing. Critical thinking and oversight are crucial for identifying errors in AI systems, underscoring the enduring value of human skills.
More than a year after the launch of ChatGPT, its capabilities and functions have drawn astonished responses from users. Its ability to communicate intelligently and provide and store copious amounts of information has left the public in awe at how similar its functionality is to that of a human. However, the thinking that usually follows is: is my job now in danger because of this technology?
Generative AI tools like ChatGPT were created to transform industries, especially within DevOps teams in testing roles. Testers who neglect to invest in AI-related skills beyond programming languages will fall behind in favor of experienced testers with a deep knowledge of AI, including how to test GenAI systems. However, there’s no need to panic – there’s still time for testers to learn how AI will impact their roles and how they can remain competitive.
GenAI and Software Testing: A Symbiotic Partnership
GenAI has the impact it does because of three particular capabilities. Let us explore them:
- Widespread knowledge: GenAI has consumed a wide variety of data from the internet, including informational blogs, videos, tutorials, manuals, and more related to software testing and quality assurance. Due to extensive learning and data capture, GenAI is better able to provide information across an array of testing topics.
- Generative nature: GenAI’s ability to provide new content will be a tremendous plus for testers. Its capacity to form test cases, plans, and reports will save valuable time and resources.
- Multiple mediums: In addition to providing content in brand-new text formats, GenAI can also produce content in audio, visual, and video formats. This will be helpful for testers as it allows them to widen the scope of their current testing practices, allowing for application scanning, action execution, and automation steering.
Forrester predicted a 15% increase in productivity for testers who use AI, and it’s no secret why. Workers who have already begun to utilize GenAI in their workflows can speed up their work while still producing quality results. Whether identifying bugs or defects, auto-generating test cases, self-healing, or conducting smart impact analysis, the different ways GenAI is already being used is vast.
See More: Google Cloud Next 2024 Key Insights for Practical AI Apps
What AI Means for Software Testing Jobs
The testing role in the future will change. AI isn’t taking away testing jobs for humans; rather, we will see a heightened demand for testers with skills specific to AI, and a shift in the role of the tester, overall.
Testers who consistently explore new ways and techniques on how to test AI systems best will be highly desired for evolving DevOps teams. Rather than focusing on traditional application testing, their priorities will fall towards validating and monitoring AI systems, ensuring the results they produce are accurate and free of biases.
As more organizations utilize AI tools, they will realize AI makes mistakes. Hence, they will need assistance and frequent testing from humans to ensure it is functioning properly and accurately. Testing will become even more fundamental, and humans will be at the root of this shift. Skills such as heightened critical thinking will be highly desired as a result. Because of AI’s susceptibility to errors, oversight from workers who know how to test for reliability in GenAI systems will be required.
How Can Human Software Testers Maintain a Competitive Edge?
As mentioned above, certain skill sets become highly desired as we venture into the age of AI in software testing. Of course, there is room for testers to improve and hone their skills as this change takes place. Below are some examples of how testers can ensure they have a competitive edge in today’s AI-driven landscape:
- Experience in data analysis: The role of a tester is to sift through the copious amounts of data produced by GenAI systems, readily available to point out errors or vulnerabilities, pull sensitive data when necessary, and determine the best course of action for the information produced. Testers adept in data analysis will be at an advantage.
- Knowledge of testing methods for AI systems: Keeping up to date with the latest techniques to test AI systems is fundamental. This includes awareness of industry-standard techniques to assess AI capabilities and when they have been compromised, as well as testing for issues in security and data privacy.
- Understanding of AI model monitoring: It is paramount to ensure an AI-embedded application is capable of performing the job it was created to do before being launched publicly. This is why establishing benchmarks used to measure AI’s intended capabilities is an effective way to evaluate AI systems’ function. Testers capable of knowing how to set up these benchmarks, monitoring how AI systems are working against those benchmarks, and ensuring the models are free of potential biases or errors will be highly sought after.
- Developed collaborative skills: Testers will be expected to work in conjunction with data scientists, AI engineers, and a wide range of other employees to monitor these AI systems. Having effective collaboration and teamwork skills will be important in ensuring these technologies are running smoothly.
- Customer service: AI frees up a significant amount of time for testers, allowing them to redirect their attention and efforts to the human component of software development and testing. This will include understanding how they can improve the user experience in testing, using AI to enhance that process, and taking these thoughts and ideas back to product teams to improve AI systems further.
Testers who are open to learning new skill sets, and keeping up-to-date on the latest techniques and trends within the testing space, especially regarding AI, will fare a better chance in retaining their competitive edge as AI enters DevOps teams. Additional skills, such as cross-team collaboration, critical thinking and judgment skills, and an interest in improving the relationship with the user as a result of AI integration, will also be sought after.
The role of the tester is not going anywhere. Testers, human testers— will remain at the forefront of quality assurance as AI integrates into DevOps, ensuring that AI can achieve what it must, but responsibly and accurately.
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