Software·43,969 discussions

How does AI/ML turn software testing from reactive to predictive?

Tags: Community, Software, Web development

I’m looking to understand how AI/ML tools are being practically applied in enterprise software testing to predict issues before they occur. What models or techniques are commonly used? And how do they integrate with existing QA workflows or DevOps pipelines?

Topic

No.

In reply to How does AI/ML turn software testing from reactive to predictive?

Current gen GPT and such was trained so everything you get from such has been done before.

Interesting point

In reply to No.

Yes, AI models like GPT are trained on existing data, means they learn from what’s been done before that much like humans do through reading observation and practice. But just like a skilled writer doesn’t plagiarize everything they’ve read, GPT doesn’t just regurgitate information.

What makes it powerful is how it combines, reinterprets and reframes existing knowledge in ways that often feel new or innovative. It’s capable of creative recombination pulling ideas from different domains to offer fresh perspectives that may not have been explicitly written before.

For example, if you ask GPT to help you design a fitness app for people with ADHD using gamification, behavioral psychology and UI best practices. it may synthesize a solution that’s technically novel, even though each concept existed individually.

the outputs can be unique and even thought-provoking when the prompt is rich or imaginative enough.

it’s still a tool and just like any tool, the creativity often comes from how you use it. 😊

Regurgitated content is my own term for the articles, essays, and other

In reply to Interesting point

Regurgitated content is my own term for the articles, essays, and other content that ChatGPT generates.

Here’s my message: “Why should I read what no one wrote?”

Great Question

In reply to How does AI/ML turn software testing from reactive to predictive?

One common technique is anomaly detection models (like Isolation Forests or Autoencoders). These help spot irregularities in app behavior or infrastructure before they cause major issues. Predictive analytics models often built with supervised learning (Random Forests, Gradient Boosting Machines) can anticipate failure-prone areas based on historical defect patterns, user behavior logs and even code changes.

NLP models are also playing a major role, they can automatically analyze test cases, requirements.

Integrate with existing QA workflows or DevOps pipelines:

– Using AI tools like Test.ai, Applitools or Functionize add into CI/CD pipelines
– some are embedding AI bots into their monitoring systems like integrating ML models with tools like Datadog or Splunk

Good Luck…

[Author]

Replies