Saqib Jan

5 days ago | datasciencecentral.com | Saqib Jan

AI has radically changed Quality Assurance, breaking old inefficient ways of test automation, promising huge leaps in speed and the ability to test things we otherwise couldn’t easily test before. But getting AI to be trusted by QA teams is a major challenge across the industry. A big part of the difficulty stems from the “black box” nature of AI tools where the internal processing is abstracted away.

2 weeks ago | devops.com | Saqib Jan

Application programming interfaces (APIs) are the backbone of modern applications. However, failures — whether from third-party outages, network issues or rate limits — can cause major disruptions. While traditional testing falls short in preparing teams for these real-world issues, something more is needed to build a truly resilient system. Most companies often make common engineering mistakes when adopting technology, generation after generation.

2 weeks ago | buff.ly | Saqib Jan

Application programming interfaces (APIs) are the backbone of modern applications. However, failures — whether from third-party outages, network issues or rate limits — can cause major disruptions. While traditional testing falls short in preparing teams for these real-world issues, something more is needed to build a truly resilient system. Most companies often make common engineering mistakes when adopting technology, generation after generation.

3 weeks ago | thenewstack.io | Saqib Jan

Generative AI (GenAI) is helping many of us with things we do every day. It is also rapidly advancing quality assurance (QA), fueling breakthroughs in testing that promise to exponentially speed up delivery and achieve unprecedented scale, potentially shattering old limits on automation. But getting testing teams to trust GenAI is also proving more challenging than it looks. A big part of the problem is the feeling that AI is a black box with unclear processes and outputs.

1 month ago | datasciencecentral.com | Saqib Jan

Software development cycles accelerate constantly, pushing quality assurance teams to keep pace. However, the pressure engineering leaders face to ensure quality under the speed and complexity modern pipelines require is also immense. And simply doing more of the same old way of things isn’t enough with the advancing user demands. Interestingly, while much of the focus has been on accelerating coding or transforming creative workflows, GenAI is profoundly reshaping quality assurance.