Imagine sending your kid to a school where the teacher is an AI.
Efficient in executing lesson plans, finishing the curriculum, providing practice worksheets, reviewing homework, conducting exams, and declaring results.
What looks perfect at the outset hides the imperfections of this trained teacher like
- Modifying the teaching methodology in real-time to adapt to the dynamic learning class pace on every topic
- Observing the individual nuances of the students and including them in the one-to-one interactions
- Providing customized feedback on strengths and areas of improvement for every child in PTMs and other interactions
- Answering all the curious, quirky, relevant, insightful, funny, and child-like questions replicating the tempo while still delivering wisdom for kids to absorb
- Knowing when to be strict and lean
- Empathizing with the learners
- Knowing the difference between scores and learning
- Mirroring the energy and curiosity of young learners
- Showcasing life skills in an environment that encourages kids to imbibe them
In short, your kid missed the opportunity of holistic development (all-around development) in the name of efficient rot-memorization.
Devs want to avoid this from happening to their codes, hence the trust issue!
Undoubtedly, AI has been broadening the scope of the technological landscape and generating much value for businesses by taking over repetitive and mundane jobs. But it has limitations since it’s only a trained machine capable of producing more accurate output at speed. Hence, your dev guy would want to avoid entirely relying on AI for testing their code since its limitations could restrict the scope and impact the quality of the final product.
Like other creators, Devs want to build products that add value to the end user’s life and make this world better. And hence, they would want to validate their software for its technical functionality and overall performance under varied scenarios. This requires an ability to go beyond the technical and have a deep and empathetic understanding of the user journey, domain expertise, and real-world scenarios to incorporate them into the testing process.
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Where’s the Gap: What AI Can And Cannot
#1 AI can execute pre-defined test cases repetitively. It doesn’t get exhausted or bored. Hence, it can do them faster and more accurately than manual testers every time.
However, it cannot plan or design test cases or testing strategies since these require a thorough understanding of the software, domain, user journey, and business requirements. The ability to understand, visualize and create something that delivers on a set of goals requires higher-order intelligence, which AI currently lacks. That is why your dev team requires seasoned and expert testers at the beginning of your testing cycle.
#2 AI excels at pattern recognition, and they do it without any biased approach. It comes in handy when analyzing logs, test reports, or other forms of extensive data, aiding manual testers with essential insights to examine reports and make better sense of the testing data.
However, AI can still not analyze complex data, non-linear connections, and layered interdependencies. Such sophisticated information requires a unique mix of abstract thinking, wisdom from experience, and an out-of-the-box approach to make sense of and generate valuable insights – an area exclusive to humans.
In software testing, as crucial as it is to design and execute a relevant testing strategy, its continuous evaluation and refinement are equally critical. That is where your Dev needs reliable, experienced and creative testers.
#3 What about exploratory testing and usability testing? AI can assist manual testers with automation only to a limited extent since these tests rely on the experience, creativity of the tester, and subjective understanding of a user’s needs and preferences to be able to validate the software.
For example, usability testing validates whether the end user can use it without hassle or a learning curve. Steve Krug’s “Don’t make me think” defines what usability testing aims to achieve. But to validate this, you need to be able to get into your user’s shoes and understand usability from their point of view – a quality that AI is oblivious to.
Similarly, exploratory testing deals with unpredictable and dynamic scenarios and uses the results to improve the product. And AI thrives on certainty and pre-defined data. Hence, the gap!
#4 AI can repeat a pre-set test case multiple times with the same accuracy rate and speed, but it fails to adapt and needs manual intervention.
First, AI lacks the reasoning to understand any change’s intent. Also, it cannot update itself with the changes (in business requirement/product). It requires human testers to grasp the changes and their implications and adapt the testing cycle accordingly. This lowers the confidence of your dev team in AI’s ability to handle the entire STLC cycle independently.
We see how AI could assist and augment software testing, but replacing manual testing capabilities is still a distant dream. Is this always going to be the case? Well, it depends on the AI’s capability to replicate human intelligence, abstract thinking, and creativity. Hence, using AI’s strength to complement software testing with the speed and accuracy of pre-defined tests is ideal for now and the foreseeable future.