Systems | Development | Analytics | API | Testing

If you’ve worked in QA long enough, you’ve seen how much testing has changed inside Jira. What started as a mix of spreadsheets, manual checklists, and endless review cycles has grown into fully integrated Test Management workflows. But even with automation, some challenges never went away. Writing test cases from requirements still takes time.

Testing is a very important and necessary step in the SDLC, but most teams ignore it or don’t care much about it, while some teams spend most of their time on testing instead of building features. AI is really changing the way we write code, but most people use it mainly for writing test cases, and we still end up doing it manually. So in this blog, let’s see what AI testing is, how AI helps in testing our software, what AI tools are available, and which tools help with which part of testing.

‍ Healthcare isn’t short on data; it’s short on usable insight. Hospitals generate an enormous share of global data, and about 30% of the world’s data now comes from healthcare, growing at one of the fastest rates across industries (RBC Capital Markets). Yet an estimated 97% of health data still goes unused for care and operations (World Economic Forum).

When we first published this report last year, AI in software testing was still an experiment for most teams. Since then, well… everything has changed. In 2025, AI hasn’t been a promise of what’s to come — it’s become an everyday reality.

Have you ever considered that even with intensive software testing and effort, important software bugs can still get through? The issue, in most cases, isn’t that you didn’t put in enough effort, but that testers evidently lack the knowledge of a proper process to follow. A test script in software testing provides very precise instructions, step by step, to manage tester consistency, use proper test data, and confirm the expected results.

As the automotive industry accelerates toward increasingly connected and autonomous vehicles, the importance of cybersecurity in embedded software development has never been greater. But how should you go about choosing the right programming language for automotive cybersecurity?

‍ Every day, hidden in plain sight, fraud drains hundreds of billions of dollars in the financial world. In fact, in 2023 alone, losses from fraud-related scams and bank schemes reached an estimated USD 485.6 billion globally. For organisations from fintechs to enterprise incumbents, every transaction, API call, and user onboarding step is now a potential battleground.

‍ Think about this: you’re a high-net-worth client logging into your wealth management portal. Instead of a sleek, tailored dashboard, you're greeted by slow loading, opaque fee disclosures, and cryptic charts that require a finance degree to decipher. Frustrating, right? That’s precisely the problem many legacy wealth platforms are saddled with today.