Systems | Development | Analytics | API | Testing

Testlio Clients Achieve Estimated 5x ROI on Payments Testing

Company drives effort to reduce $1.1 trillion in failed digital payments. Austin, TX, July 9, 2024 – Testlio, a leading quality management company, today announced that its clients achieve an estimated 5x return on investment (ROI) through its payments testing services. This milestone underscores Testlio’s pivotal role in mitigating the critical issue of failed digital payments, which cost businesses an estimated $1.1 trillion annually.

Unveiling the Future of Testing: Automation for All with SmartBear HaloAI

SmartBear is revolutionizing the way teams deliver high-quality software, faster than ever before. Today, we’re announcing a set of major product enhancements to Zephyr Scale, the leading Jira-native test management platform, to empower everyone on your team to automate tests, regardless of coding experience. This “Automation for All” is fueled by the power of SmartBear HaloAI, which is transforming software development and testing productivity.

Quickly Establish a Performance Baseline: A Simple Guide for Immediate Results

Creating a performance baseline is a fundamental step in ensuring that your software applications run smoothly and efficiently. This guide will focus on how to quickly create a performance baseline for load testing, making it accessible to everyone from non-technical business owners to seasoned software engineers. Let’s dive in!

Save Time and Improve the Accuracy of Your NetSuite Reporting

Financial and operational reporting for NetSuite can be a challenge. As is the case with many ERP systems, NetSuite’s reporting capabilities tend to be somewhat restrictive. It can be difficult to pull information from multiple NetSuite modules into a single, cohesive report. In other instances, information for which there ought to be a fairly straightforward reporting process turns out to be inaccessible.

Using Moesif, AWS, and Stripe to Monetize Your AI APIs Part-2: Setting up Metering and API Access

In the previous article, we set up the AI API with AWS Lambda and Gateway, integrated it with Moesif, and then connected Stripe with Moesif. We now have the infrastructure to begin billing for API usage. In this article, we move on to configuring Moesif with the following steps in the API monetization journey: First, let’s set the prices we want to charge for API usage in Moesif.

Providing a Secure In-App Login Experience with Authentication API

Application developers want to provide the most secure and seamless login experience for their users, but even when following OAuth and OpenID Connect (OIDC) best practices, user experience issues can still be a problem. In this article, we will walk through how developers can provide a secure and seamless login experience to users by providing the login functionality natively within the app itself.

Choosing the Right Chart Type for Good Data Visualization

An effective dashboard requires careful design to present data in the best way, and to help more people (users, customers) find insights without feeling overwhelmed. Yellowfin BI comes with a wide variety of chart types as part of its extensive data visualization tools, and while it is tempting to use a lot of eye-catching charts to make a dashboard that looks great, it is important to select the right chart for the right situation.

Think twice before you hire a QA engineer

When you’re ready to automate your manual tests, you might naturally think you need to hire someone with a technical skill set who specializes in automating end-to-end tests. That is, you might think you need to hire a QA engineer. It’s not an unreasonable assumption. But for many startups, it’s the wrong thing to do. QA engineers are quite expensive (in more ways than one), bottleneck release processes with their complex tooling, and can present other types of business risks.

RAG vs Fine-Tuning: Navigating the Path to Enhanced LLMs

RAG and Fine-Tuning are two prominent LLM customization approaches. While RAG involves providing external and dynamic resources to trained models, fine-tuning involves further training on specialized datasets, altering the model. Each approach can be used for different use cases. In this blog post, we explain each approach, compare the two and recommend when to use them and which pitfalls to avoid.