As AI continues to advance, its application is spread to various industries, including software testing. It has undoubtedly revolutionised the testing process but has also stirred a debate whether manual testers are now obsolete. Are they?
Here are 5 arguments to prove it, that even in the age of AI – we need manual testers. Because they possess unique abilities & domain expertise that AI in software testing currently lack.
Spotting unusual bugs
Manual testers can notice even the smallest of inconsistencies and can work on it accordingly. On the other hand, AI follows rules- unlike humans, it can’t have instincts or intuitions, it will work on the principle which it understands. Humans can understand the context if something is wrong, but AI won’t.
Requirements
The requirements may not always be flawless; they could have missing details, conflicting expectations, or even vague descriptions. Manual testers will step in to identify any missed requirements, assess if there is a need for changes in the requirements, and raise new ones to fill these gaps. They then perform extensive analysis as part of static testing and brief all stakeholders before acting accordingly. On the other hand, AI may struggle with unclear or vague requirements and unstructured flows, as in most instances, the business team may not clearly define what they want. AI won’t be able to think beyond what it was designed and built to do.
Adapting Software projects
As requirements and priorities change due to stakeholder feedback or market trends and needs, manual testers can respond to these shifts with greater efficiency and effectiveness, by modifying their strategies, approach, and methods accordingly. AI may find it challenging to match the ever-changing landscape of software development.
User experience and usability testing
AI can check whether buttons and links function, but it lacks the human intuition to determine whether an interface is user-friendly, or visually pleasing. By assessing features like usability, message clarity, and emotional effect, manual testers can replicate real-world usage and offer essential information about how users will really interact with the product.
AI forms decisions & predictions by algorithms & data
They lack human consciousness, emotion and most importantly- judgement. Incorporating ethics and morality are another important feature where humans excel. They bring human perspective that AI can’t replicate.
Improving the testing process allows for early detection of potential issues while also refining user experience to meet diverse requirements. Such initiatives- combining the strengths of AI tools with the unique expertise of manual testers, deliver higher-quality software products to customers. This balanced approach ensures that the final product not only operates well, but also provides an improved, satisfying experience for the customer, who gains enhanced reliability, better usability, and more seamless performance from the apps.
Let’s look at some of the real-time scenarios:
Interest Calculation
AI can’t build a complex scenario for Credit card interest like:
Scenario 1: Verify the interest calculation for an account. A purchase has been made during the interest-free period. A partial payment is posted before the billing cycle. A cash advance transaction is also posted, and another purchase is converted to an EMI transaction before the billing cycle. A statement is generated to validate the interest for each component, with different interest rates applied to each credit plan.
Scenario 2: Verify the interest calculation for an account on the next billing cycle. Assume the following on your credit card:
- Billing Cycle: 30th every month
- Due Date: 20th of the next month
Interest Rates
- Retail Plan: 36% p.a. (3% monthly or 0.0986% daily)
- Cash Plan: 48% p.a. (4% monthly or 0.1316% daily)
- EMI Plan: 24% p.a. (2% monthly or 0.00547% daily) + 2% processing fee
Transactions:
- ₹20,000 purchase on 5th July
- ₹10,000 cash advance on 10th July
- ₹5,000 EMI balance started on 15th July
- You pay ₹5,000 on 20th July (not full payment)
II. AI can’t fully replace Fraud Risk Management
Scenario 1: The cardholder informs the bank that he is traveling to Singapore, so any card-present transaction made domestically during their stay outside their home country would be considered a fraudulent transaction. However, in the case of a flight delay or cancellation, if the cardholder remains at a domestic airport or stays back, all his domestic transactions will be declined, as he didn’t inform the bank or update any feed on social media.
Impact- Causes significant customer dissatisfaction or frustration.
Scenario 2: For suspected transactions, manual intervention involves temporarily blocking the card or contacting the customer and replacing the card only if the transaction is confirmed to be illegitimate. However, if AI makes the decision, the card may be automatically blocked or replaced, which can lead to high customer frustration. Removing a temporary block through AI-powered decision-making is challenging, as it requires analysis of numerous factors. AI powered IVR (Interactive Voice Response) systems may also be part of the integrated solution, but they often fail to resolve the cardholder’s issue effectively.
Impact- Causes significant customer dissatisfaction or frustration.
Scenario 3: AI-powered automatic actions- like blocking a card, replacing it, or declining transactions- may ensure quick fraud prevention, but they can also lead to negative customer impact if not handled with the right balance of accuracy and empathy.
Scenario 4: If trending fraudulent activities are not monitored, or when rules are not on production on an immediate basis, or if the scoring engine is not resulting in better fraud detection, it will lead to huge fraud loss.
Impact- The financial institution will completely lose the market to competitors.
At the end of the day, all technology — whether it’s AI or automation — exists to make things better for the end consumer. But ensuring that improvement truly happens still needs a human touch. Can AI really replace the instincts, empathy, and judgment of a manual tester — or is the smartest future one, where both work together? You tell us!
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