According to a study by Stack Overflow, developers spend approximately 50% of their time debugging and fixing issues in their code. As a software engineer, I understand the problem first-hand. We pour our heart and soul into building applications, yet bugs persist despite our best efforts. While some are caught during compilation, others lurk undetected, potentially causing damage and financial woes. That’s why rigorous testing of applications becomes crucial before market launch.

Nearly every facet of society has felt the impact of AI since it burst into the mainstream in late 2022 with the public launch of ChatGPT. In 2024, the retail and consumer goods industry is expected to experience massive upheaval due to the proliferation of generative AI (gen AI) tools as well as changes in customer engagement and the general manner in which products are now sold.

It’s no secret that the expectations for public sector digital services have changed significantly over the past few years; services need to be rolled out fast to ensure usable and secure software that can help agencies deliver on their mission. These expectations can be met by combining DevSecOps with codeless automated software testing.

‍For almost a decade now, Countly has been extendable through the plugin system. But it also had its cost. In this article, we will explore how it worked before, what the limitations were, and what we did to improve the situation.

Testing software is like putting it through a series of challenges to make sure it’s tough enough for real-world use. Whether we’re testing each piece individually (unit testing) or how they all work together (integration testing), we need to be prepared for different situations. Sometimes, testing is tricky.There are times, when we need to isolate parts of our code from their dependencies.

In the dynamic landscape of software development, where agility and precision are paramount, testing reports emerge as a cornerstone for quality assurance, tracking progress, and fostering effective communication among stakeholders. Let's embark on a journey to explore the intricacies of testing reports, unraveling their significance, types, content, and the tools that can elevate your reporting game.

Financial services companies are leveraging data and machine learning to mitigate risks like fraud and cyber threats and to provide a modern customer experience. By following these measures, they are able to comply with regulations, optimize their trading and answer their customers’ needs. In today’s competitive digital world, these changes are essential for ensuring their relevance and efficiency.

In the past, querying a database required Structured Query Language (SQL) skills, or knowledge of other database query languages, such as Kibana Query Language (KQL). Today, with the emergence of generative AI (GenAI), teams can query their analytic database using natural language — and get plain English results in return. Or, if you prefer to still use SQL, many teams use GenAI for database query optimization, making queries faster and more efficient.

The journey from manual testing to test automation can be daunting, with several potential roadblocks limiting success. Some factors that make this transition challenging include the high upfront costs, limited access to talent, and the need to implement sustainable script maintenance. These factors can be overwhelming and seem at odds with the objective of leveraging automation, which is to relieve pressure from product executives to increase software release velocity, improve efficiency, and cut costs.

Continual is proud to announce that we are now SOC 2 Type 1 compliant and SOC 2 Type 2 in progress. This certification demonstrates our core commitment to your data security and privacy. We expect to make additional announcements around our security certification efforts over the coming months. Beyond third party attestations, Continual is built from the ground up for data security, privacy, and governance at enterprise scale.