Does QA practices still exist when AI is dominating the digital world?
Dhemz
6 replies
They can simply ask their AI in advance "What's wrong with our code".
Replies
Walter Mendoza@wm23
The following is what ChatGPT says:
"Yes, QA practices still hold significance even in a world where AI is prevalent. While AI can assist in various aspects of software development and testing, it does not eliminate the need for QA practices. Here's why:
AI is not infallible: AI systems are trained based on historical data and patterns, which means they may not capture all possible scenarios or uncover all types of issues. QA practices help identify edge cases, validate system behavior, and ensure comprehensive testing.
Ensuring quality standards: QA practices involve defining and implementing quality standards, test plans, and test cases. These practices help ensure that software meets the desired quality level, adheres to requirements, and functions as intended.
Human perspective and creativity: QA professionals bring human judgment, intuition, and creativity to the testing process. They can think critically, identify potential issues that AI might overlook, and provide insights based on their experience and domain knowledge.
Validation and verification: QA practices involve validating the software against business requirements and verifying its functionality, usability, performance, security, and other aspects. These activities help ensure that the software meets the desired objectives and performs optimally.
Continuous improvement: QA practices emphasize continuous improvement by identifying areas for enhancement, addressing customer feedback, and refining processes. This ongoing evaluation and refinement contribute to the overall quality and reliability of software products.
In summary, while AI can enhance and automate certain aspects of testing, QA practices remain essential to ensure software quality, validate requirements, address diverse scenarios, and provide a human perspective to the testing process."
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@wm23 Those are pretentious dialogues by chatGPT!
what chatGPT says on the opposite side:
"Alright, so here's the thing: With AI running the show, QA's become kind of a backseat driver. Why? Let's break it down.
First up, AI's not just smart, it's super smart. It's designed to learn from past scenarios and can pretty much catch anything thrown its way. We don't need QA to baby-sit.
Then there's the quality angle. Our AI's so good it guarantees top-notch software all by itself. It knows what it's doing, and double-checking everything with QA just feels like overkill.
And about the human touch, well, AI's gotten so advanced it's almost human-like. It has the ability to predict and spot stuff even before we do. So, we don't really need a human to do what AI can do better.
Finally, AI's not just about doing the job, it's about improving all the time. It learns from user feedback and tweaks itself. So, we don't really need QA to keep a check.
So yeah, while QA was cool once, with the advanced AI we have now, we can handle testing just fine without it. QA, in a way, has become more of an old-school way of doing things. AI's our main star now, making sure our software is top-tier and reliable."
They are bad robots if you read them in the opposite scenario.
@cryptodhemz I'm curious now...what prompt did you use to trigger the above? xD
That actually makes sense. I tend to just ask AI to receive accurate answers 🤔 Is QA one of the thing that gets obsoleted by AI?
@nrbz The following is what ChatGPT says,
"While AI can assist in certain aspects of QA, it does not render it obsolete.
QA encompasses a range of activities beyond just answering questions. It involves ensuring the overall quality, functionality, and reliability of products or services. While AI can automate some QA tasks, human involvement remains critical for strategic planning, designing test cases, evaluating user experience, and making subjective judgments.
Furthermore, AI systems like myself are trained on existing data, which means there can be limitations and biases in the information provided. Human QA professionals bring domain expertise, critical thinking, and the ability to adapt to new scenarios or unexpected issues.
Rather than replacing QA, AI can enhance and streamline certain aspects of the process, improving efficiency and scalability. It can help automate repetitive tasks, perform data analysis, or assist in identifying patterns or anomalies. This allows QA professionals to focus on higher-level tasks, such as developing comprehensive testing strategies and ensuring the overall quality of products.
In summary, while AI has a role to play in QA, it complements rather than replaces human involvement. By leveraging AI in QA processes, we can achieve more accurate and efficient outcomes, but human expertise remains essential for ensuring comprehensive quality control and meeting user expectations."