While digging through data, Anna spots an interesting trend - some customers buy 3 times more than others. A segment of super-high spenders? This could make it rain for the company! She rushes to her boss, to show them the data, only to hear: “Yeah, I know. We have a bug, which inserts every order three times in the database. Were you not paying attention during our daily meeting?” Aw-kward.
This blog post is a guide to help you write Gatling scripts in order to load test web applications efficiently. It follows our second Gatling Simulation scripts parameterization article. We will continue to load test a fake e-commerce, and so we are going to improve our Virtual User to make it browse the store in a more humanly way. We start where the previous blog post ended, with a simulation script that uses a CSV feeder and a Regular Expression extractor to visit dynamic pages of the pet store.
To quote the study, “For every USD 14 million delivery by the software development and QA team, a hidden USD 3 million was being spent on data management.
Working from home is the new norm for teams all over the world. For software development teams, adapting to the new working environment has been going on for more than one month now. To further understand the challenges of working (or testing) remotely, we conducted a quick survey on the state of working from home for the testing community.
Memory leaks are something every developer has to eventually face. They are common in most languages, even if the language automatically manages memory for you. Memory leaks can result in problems such as application slowdowns, crashes, high latency, and so on. In this blog post, we will look at what memory leaks are and how you can avoid them in your NodeJS application. Though this is more focused on NodeJS, it should generally apply to JavaScript and TypeScript as well.
Today, we're kicking off a study on the impact of COVID-19 on mobile development, -developers and the companies that employ them. Participate in the survey here, and we'll share our findings with you in a few weeks. Read on to find out exactly what we're planning.
It seems everyone is talking about machine learning (ML) these days — and ML’s use in products and services we consume everyday continues to be increasingly ubiquitous. But for many enterprise organizations, the promise of embedding ML models across the business and scaling use cases remains elusive. So what about ML makes it difficult for enterprises to adopt at scale?
Too often, companies are finding out after the fact that customers have stopped using their product or service, without enough notice to have done anything about it. The term customer churn is used to describe the loss of existing customers. These are people or organizations that were using a company’s products and/or services and have decided not to use them anymore, in favor of a competitor. Tracking customer churn is a key business metric for most companies.
