Systems | Development | Analytics | API | Testing

Here at SmartBear, our motto is that quality isn’t just the goal, it’s the whole point. The software development process is intricate and dynamic, with constant changes being made to meet evolving user requirements and address defects. However, with these changes come potential risks. One of the most significant challenges in software development is ensuring that making modifications to an application does not inadvertently break existing functionality.

Application programming interface (API) automation testing is a type of automated testing that focuses on the performance and functionality of APIs. This process can test APIs for correctness, compatibility, and efficiency. API automation testing can ensure that APIs function properly and meet consumers’ expectations. Here’s the key things to know about API automation testing: Table of Contents.

With Node.js and Express, you can easily create a JavaScript server. Then the obvious question would be, “Why Typescript?” While JavaScript remains the most popular scripting language, TypeScript emerges as a great alternative when your application becomes more complex or when you collaborate with a distributed team of developers.

Unlock enhanced platform engineering with traffic replay for real-world performance testing, ensuring faster feature delivery with consistent quality.

With more emerging players entering the insurance market and insurance companies recognizing the importance of the digital experience, consumer demands for a connected insurance experience have grown to new heights. For insurance customers, the claims process is a critical moment of truth, making it essential that insurers deliver a hassle-free, seamless experience with faster claims processing and excellent customer service.

What determines the success of an API? A significant part lies in mastering the API lifecycle—from planning to retiring, every step impacts your API’s performance and adoption. The API lifecycle involves several critical stages—planning, designing, developing, deploying, consuming, monitoring, and retiring. Each stage dictates the successful implementation of APIs, including governance models, transparency, and security being crucial throughout.

The quick and dirty definition of data mapping is the process of connecting different types of data from various data sources. Data mapping is essential for integration, migration, and transformation of different data sets; it allows you to improve your data quality by preventing duplications and redundancies in your data fields. Data mapping is a crucial step in data modeling and can help organizations achieve their business goals by enabling data integration, migration, transformation, and quality.

Implementing a PIM or PXM* solution will bring numerous benefits to your organization, in terms of improving efficiency, increasing sales and conversions, reducing returns, and promoting customer loyalty through more accurate, more complete, and more engaging product content. Here we explore these benefits in more detail.

Artificial intelligence (AI) apps are becoming increasingly crucial for individual customers and businesses alike. These apps bring many benefits, such as task automation, efficient analysis of large data sets, and data-informed decision-making, making AI-powered applications highly valuable. As a result, DevOps teams working on AI apps can’t afford poor performance.

With all the recent discussion about the use of artificial intelligence (AI) and large language models (LLMS) like ChatGPT, you may think that AI is a new phenomenon. But in fact, the US Department of Defense (DoD) has been investing in AI for more than 60 years.