In the past, testing for non-functional requirements (NFR) was often neglected, done to the extent possible at the end of the life cycle. For modern development teams, that is no longer an option - to deliver high-quality applications, they have to implement a comprehensive testing strategy with proper allocation of resources to both functional and non-functional components.

Isn't it remarkable how the market for android app development has seen such exponential growth, with an unheard-of CAGR of almost 8.83%? It's also projected to hit the US$673.80bn mark by the end of 2027, all credit goes to the ongoing technological advancements and the digital transformation happening across industries. ‍ But amidst this surge, have businesses truly pondered the intricacies of mobile app development? And what makes a particular app stand out and perform exceptionally well?

A data catalog is a central inventory of organizational data. It provides a comprehensive view of all data assets in an organization, including databases, tables, files, and data sources. Efficiently managing large amounts of information is crucial for companies to stay competitive. This practice is especially applicable to large organizations with scattered data.

In December 2023, Snowflake announced its acquisition of data clean room technology provider Samooha. Samooha’s intuitive UI and focus on reducing the complexity of sharing data led to it being named one of the most innovative data science companies of 2024 by Fast Company. Now, Samooha’s offering is integrated into Snowflake and launched as Snowflake Data Clean Rooms, a Snowflake Native App on Snowflake Marketplace, generally available to customers in AWS East, AWS West and Azure West.

In today’s dynamic business world, seamless transitions between application life cycle management systems (ALM) are crucial for organizations striving to optimize workflows and enhance productivity. JIRA, a robust and versatile project management tool, often becomes the best choice for teams seeking efficient task management, issue tracking, and collaboration capabilities.

The software testing landscape, while crucial for ensuring application quality, has grappled with limitations in traditional methods for decades. This article explores how Artificial Intelligence (AI) is emerging as a game-changer, addressing these challenges and augmenting the capabilities of testers. By combining advanced machine learning, deep learning, natural language processing, and other techniques, AI offers a powerful toolkit to propel software testing into a new era.

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With the explosion of artificial intelligence (AI), innovative companies are harnessing enterprise AI at scale for a variety of business use cases, including repetitive task automation, product and service development, supply chain management, customer service operations, decision-making systems, and more.

Just shy of a year ago and coinciding with the Atlassian Team ‘23 conference, Testlio unveiled an initiative to help product teams adopt signal-driven testing as a core pillar of the future of software quality engineering. A lot of exciting things have happened and continue to happen since that announcement, which collectively serves as validation of the opportunity for product teams to dramatically improve test coverage efficiency through signal-driven testing.