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 ...

Quality Assurance The role of Artificial Intelligence and Machine Learning has been growing steadily in business and our daily lives. These technologies are no longer the preserve of science fiction but have been implemented in business operations. You might have a brush with them while shopping on Amazon (by getting recommendations,) receiving ‘likeminded’ feeds on the social media, or asking Siri, Cortana or Google Assistant to play a specific piece of music. In case these appear to be the more visible ways in which AI or ML work, there are many areas where these technologies function in the background. For example, finding out the credit worthiness of bank customers, filtering spam, categorising emails, working as chatbots on websites to resolve customer queries, dynamic price optimization, detecting frauds in online transactions, curating content, pattern and image recognition among many others. Notwithstanding the adoption of AI and ML technologies, these are still ea...

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...