QASource Blog

Voice recognition and voice assistants have become central to the software as a service (SaaS) industry, changing how we interact with SaaS and promising benefits for businesses and users alike. But if you plan to offer a voice assistant or other voice recognition tool, it’s important to thoroughly test your offering before bringing it to market.

Analysts predict AI's market size in software testing will reach an impressive $4 billion by 2024. But the impact of all this AI is tied to the precision and robustness of the underlying models.

AI in mobile testing is booming, pushing developers to stay competitive and enhancing regression testing, script, data, performance, usability, and visual testing.

Software testing is vital because it allows you to test if an application is working properly. There are various types of tests that you can perform if you work in the software industry. Nevertheless, all these tests fall under one of these two classifications: positive and negative testing. Both categories are equally important, but positive testing is more popular than negative testing.

Micro-services architecture excels in complex, large-scale apps with diverse tech needs and evolving features. Performance testing is crucial for smooth interactions, scalability, bottleneck detection, and reliability. This series explores AI-driven techniques for assessing efficiency and system performance in micro-services architecture.

Tech debt from trade-offs during software development can result in substantial costs for your business. A survey conducted across 200 tech companies disclosed that an average of 25-40% of a developer's time is spent addressing tech debt. And 79% of tech leaders cited tech debt as a significant hurdle in achieving their business objectives, signifying the gravity of this issue. But AI is a game-changer when it comes to reducing it.

In the new era, Automation testing is expected to play a crucial role in software development and quality assurance processes in the coming years. In 2024, it is anticipated that automation testing will become even more prevalent and sophisticated because of advancements in technologies like artificial intelligence and machine learning are likely to enhance automation testing capabilities further. AI-powered testing tools can analyze vast amounts of data, identify patterns, and generate test cases, thereby increasing the efficiency and effectiveness of testing processes.

First, the answer. The best approach to test automation development has five steps: Choose the right QA partner for test automation development Identify project requirements and outline a strategy. Identify the right tools for you Build a framework and create scripts Provide a demonstration and full reporting

Product companies always deal with unfamiliar situations. Since companies are running their operations remotely, opportunistic threat actors have increased their attacks which has increased the importance of cybersecurity.