Systems | Development | Analytics | API | Testing

Ever wondered if there’s more to software quality than just testing? The distinction between software testing and quality assurance (QA) might surprise you. While they are closely related and aim to ensure high-quality software, their roles and processes differ. Let’s evaluate the differences between QA and software testing and where they overlap.

It’s probably the most frustrating issue customers encounter. After all, they took the time to search for a product they wanted to purchase, made it all the way to checkout, and entered their information, only to have the transaction fail. If they’re like most online shoppers, chances are this experience left them frustrated enough to take their business elsewhere.

Regarding charging for API usage, we usually gravitate towards charging per API call. While this can work for many use cases, it’s not optimal for everyone. This is where choosing the right metric to bill upon and finding a platform that supports it is crucial. In this blog, we will talk about choosing the right metric to bill upon and how to implement it in Moesif. Let’s begin by digging into what a billing metric is and how to decide which one is best to use with your AI APIs and services.

The end of the zero-interest-rate period (ZIRP) has caused an awkward shift in the Minimum Viable Product (MVP) model, forcing the tech industry to redefine how quality is defined in an era when capital is low, the software market is saturated, and switching costs are lower than they’ve ever been.

Since it was created by Facebook in 2012 and made publicly available in 2015, GarphQL has changed everything about how we fetch data from servers for our front-end apps. Most front-end clients typically use REST APIs to retrieve data from the server, this includes mobile apps for platforms like Android, iOS, and Flutter, as well as JavaScript frameworks like React, Angular, Vue, and Next. A huge advantage of GraphQL is that it enables front-end clients to request only the API they require.

Most developers who upload mobile apps to the App Store or Play Market have likely encountered unexpected bugs in new releases. To address this issue, a new build with the fix must be created and checked again by the platform where they are uploading. They also have to wait for the approval process again for the new version to be released. Unfortunately, this entire process takes a considerable amount of time, but there is an alternative way.

IoT is driving the digital metamorphosis in the banking industry. The surge in internet-enabled devices, like smartphones, tablets, and smartwatches, signifies an increase in opportunities for fintech software development to scale their business through the Bank of Things (BoT). IoT technologies are enabling real-time connections, making banking more efficient and customer-centric.

‍McKinsey’s latest research projects that generative AI could contribute between $2.6 trillion and $4.4 trillion annually across various sectors. Experts have also observed that integrating AI-driven automation, threat detection, and low-code platforms redefines next-gen software development. Whether it is code generation, bug fixing, or even designing a new digital component, generative AI is seeping into all product engineering processes.

‍ A survey suggests that 61% of companies' compliance teams have identified that keeping abreast of upcoming regulatory changes should be a top strategic priority. This indicates that businesses are not only striving to meet current compliance requirements but are also proactively staying ahead of them. However, maintaining such compliance requirements needs tight security strategies.

Testing tools in AI and data automation have evolved to offer sophisticated features that ensure the quality of the end product and reduce its time to market. With reports suggesting 50% of manual testing being replaced by automated, these tools can help with various aspects of software testing, including unit testing, performance testing, and security testing. They can be integrated practically anywhere across the CI/CD pipeline for continuous testing and shift left testing.