Codecov by Sentry, a dedicated code coverage reporting solution, announced Bundle Analysis and Test Analytics, two new solutions designed to accelerate workflows and arm developers with actionable insights to create a seamless development experience.
- Bundle Analysis: Bundle Analysis helps improve an application's performance, bandwidth usage, and load times by letting the developer know if what they're about to merge will cause any performance lapses. It also enables a developer to explore all of the modules in their JavaScript bundle and determine where they might be able to streamline the bundle size or find areas of concern. This makes it easier to spot performance issues before they hit production, so end users avoid load time frustrations and stay on the website.
- Test Analytics: Test Analytics, now in open Beta, is a new set of tools that identifies and flags to the developer exactly why a test failed within the Codecov Pull Request (PR) comment to spend less time debugging an issue. The updated Codecov PR comment tells a developer if their PR failure is from a test failure associated with the proposed code changes or the result of a systemic flaky test. Codecov also added Test Analytics into the Codecov UI providing developers with a simple summary of the tests in a project's test suite. This includes average duration, failure rate, commits failed, and the last time a test ran. Developers can gather insight into how tests impact CI performance and the developer experience.
"Developers don't have hours to spend debugging code or deciphering performance issues," said Eli Hooten, Director of Engineering for Codecov at Sentry. "We're excited to introduce our first solutions outside of code coverage and offer developers a complete solution to accelerate workflows and performance."
To use Bundle Analysis, users will need a bundler plugin setup with support for the current version of Rollup, Vite, or Webpack. Test Analytics is now available for all Codecov users in Open Beta.
The Latest
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...
AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...
Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...
A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...
IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...
A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...
According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...
2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...
Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...