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Generative AI
Cloud
Testing
Artificial intelligence
Security
The seamless integration of AI has made it a part of our routine without us even realizing it, exemplified by using AI-powered assistants like Alexa to control our entertainment systems.
AI and ML are household terms already. Over the last decade, the proximity of common people to those terms has seen on a steady incline. Most of us are speaking about infusing AI into our day-to-day activities. In fact, the infusion has been so seamless that we haven’t even realized that we are already a few years into our AI journeys. For instance, YouTube on our smartphones knows what we are thinking, what we might be interested in watching. The online shopping apps know what we might need at a certain time of the year and start coming up with suggestions. Rewind 10 years back and we would not be able to think of many of these SMART additions into our lives. Such has been the impact of AI on us that these days we ask Alexa to launch a movie on an OTT platform on a TV in living room Even the revolutionary remote control seems obsolete at times. While these are the examples of things we do in our leisure time, we can’t ignore the impact of AI at work.
Generative AI (a.k.a. Gen AI) has been with us for a while, its history dates back to 1950s and 1960s but in recent times Gen AI is seen as a disruptive technology that could completely change the way we live our lives. Thanks to ChatGPT 4, this revolution is now at everyone’s fingertips. ChatGPT can write your profile for you, it can tell you nearly everything about everything. So, it’s no surprise that Gen AI has brought disruption in the way we work in the IT industry. People have been talking about automating the development activities, automated code generation etc., for a while now and Microsoft Power platform, Mendix etc., are some of the already established low-code / no-code platforms in the market. With the advent of ChatGPT, developers have been able to get automatically generated code snippets easily and this has already been observed to be quite effective. With all those proven capabilities, the application of Gen AI towards software testing becomes an interesting proposition.
Typically, Gen AI relies on Large Learning Models (LLMs) aided by training data to generate the model output after analysing the user input. Many of us have already tried and seen ChatGPT in action but the application of Gen AI is not limited to just text-based interactions. Gen AI models can generate a wide variety of output. Below are some of them:
Fig 1: Gen AI models for various output formats
Having analysed how Gen AI works, its applicability can be seen at multiple points during the software testing journey:
Fig 2: Role of Gen AI in quality engineering journey
All the above potential applications and many more sound very promising but Gen AI is still far from being completely accurate. It is very important to understand that the power of Gen AI comes with some caveats, here are a few of them:
Practising Ethical AI, which requires a system to be inclusive, unbiased, explainable, built with right purpose, responsible in terms of data usage etc., can be a big challenge due to all of the above caveats and hence introducing Gen AI in business-critical operations poses a significant risk. Worse, nobody can even predict the likelihood and the extent of this risk. For example, in medical field, Gen AI might conclude on a patient’s diagnosis based partly on the patient’s background, medical history, similarity of symptoms with other patients etc., thereby producing potentially inaccurate, biased reports. Unless supervised by an expert, this might lead to disastrous outcomes.
There are many more such risks and they are being uncovered every passing day. Despite its huge potential in software testing, it is very important that human test engineers and domain experts supervise the work done by Gen AI to mitigate those risks to the maximum extent possible.
In a nutshell, the human expertise it not yet outrightly replaceable, in fact it becomes even more important in making the right interpretations and taking the right decisions. Stephen Hawking once said for a reason “Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks.”
In my view, the human intelligence is here to stay. There is still a new learning curve for all of us to understand how we can leverage Gen AI more for its virtues rather than its still mostly unknown risks.
Technical Architect, QE&T Practice, Sogeti India