Software Quality Assurance Testing Guide Tips And Practices

Artificial Intelligence   To meet the digital transformation goals where DevOps and Continuous Delivery are the key paradigms, businesses have their task cut out. The growing competition has led companies to innovate fast and deliver products and services that are free of glitches. This means assessing risks on a real time basis across the SDLC by leveraging advanced technologies such as Artificial Intelligence and Machine Learning. Also, the availability of big data and computing power has led the IT thinktanks in business to consider AI as a strategic investment. To be a digital disruptor in your domain and hold a competitive edge, there should be adroit management of the customer experience. This can, however, be a difficult proposition given the broad spectrum of software testing. Today, a software application or system interacts with myriad digital touchpoints through APIs in a nonlinear fashion. Also, the digital environment is witnessing rapid changes driven by ...

Artificial intelligence Software applications have grown in complexity thanks to their interfacing with myriad devices, legacy systems, operating systems, frameworks, networks, browsers, and cloud platforms. Moreover, the complexity often grows in a non-linear fashion making the job of testers a challenging exercise. Since the proper functioning of these applications has a bearing on achieving customer satisfaction, not to speak of the business staying competitive, the quality needs to be top notch. The changing dynamics of evolving technologies and customer choices has led businesses to adopt the methodology of Agile-DevOps. The objectives of Agile-DevOps in terms of ensuring Continuous Integration and Delivery have led software testers to embrace test automation in a big way. However, test automation can have its pitfalls too as follows: The inability to identify the processes for test automation. The difficulty in writing automated test scripts for soft...

AI application Testing   The digital transformation initiatives of enterprises have become comprehensive and complex in their scope and challenges. These initiatives entail the development and delivery of myriad software applications, which in turn leverage a host of digital elements. These elements include API’s, legacy systems, platforms, frameworks, cloud, cutting edge devices, operating systems, browsers, and networks. The complexities and challenges brought about by the interaction of applications and digital elements (as mentioned earlier) have witnessed a paradigm shift in the Quality Assurance process. To ensure deliverables such as quality and cost savings within small turnarounds, the leveraging of machine based intelligence has become important. Artificial intelligence, apart from improving the quality and scope of test automation, helps in analyzing huge chunks of data to draw meaningful inferences. AI application testing is poised to do away with traditional ...