QASource Blog

In this newsletter, we will be discussing WebRTC technology, its testing approach, and how we can measure the performance of WebRTC-based applications providing unified real-time communication.

Technical debt is one of the biggest problems in software testing and quality assurance (QA). Left unchecked, this can hurt companies in the long run, affecting their most critical processes and lowering their return on investment (ROI). So, what is technical debt? How does it happen? And what can you do to manage and mitigate the problems associated with it?

With recent trends in automated and digitized data acquisition, and deep learning algorithms, AI and ML applications are growing rapidly in fields that were initially to be solely managed by human expertise. By introducing AI and ML into automated testing tools, QA teams are able to address many of the conventional problems of automated software testing. According to a report by Digital Transformation Trends, 80% of businesses admitted that AI and ML improved their productivity, brand value, and ROI. And now, these two components have become an integral part of the software testing life cycle.

The Agile testing environment creates strength in numbers. It aligns the talents and vision of your entire team to ensure the promise of your product becomes a market reality. Every stakeholder in your software development life cycle is involved from beginning to end. You can maintain an end-user focus through the development and analysis of daily builds using a cyclical, continuous integration model. The core of the Agile environment is the relationship between developers and testers. Each plays a vital role in what has traditionally been the other's exclusive domain. Developers participate in automated unit testing, for example, while testers are involved right from the initial sprint planning.

It is essential to test your medical devices for accurate diagnosis, prevention, and treatment of illnesses. Various factors like regulatory norms, cyberattacks, or software glitches can affect the functionality of the medical device. Hence, to ensure high-quality output, quality assurance and quality control have become the need of the hour in medical device testing.

While companies rely on robotic process automation (RPA) using computer-coded rule-based software robots to automate repetitive activities like reading emails and documents, understanding speech and analyzing language, the growing complexity of organizational processes and spread of digitization has necessitated the implementation of AI to help robots resolve inconsistencies, perform cognitive tasks and resolve uncertainties.

In recent years, conventional SaaS models have evolved into sophisticated service-oriented platforms geared toward enhancing business efficiencies and capabilities between the company and its suppliers, clients, and business partners. Artificial intelligence has become an integral part of SaaS 2.0, as AI-based SaaS integrations allow businesses to cater to their customers’ needs in DTC, B2C, and B2B markets.

Software development and testing are complex processes that require the extensive knowledge and expertise of developers. This is especially the case for teams who are eyeing to implement behavior-driven development (BDD). There could be various techniques and strategies to simplify the process, but the outcome will also depend on the efficacy of the former.

In a recent report, Juniper Research states that more than 33 billion records will be stolen by cybercriminals by 2023. Hence, effective cybersecurity testing becomes even more important than ever. It protects all categories of data like sensitive data, personally identifiable information, and protected health information, from theft and damage.