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Struggling with Azure DevOps reporting? Learn how to overcome the 6 biggest limitations, from basic dashboards to cross-project analytics, and get actionable insights.
Connect Keypup to Azure DevOps (Cloud & Server) to turn your development data into powerful insights. Track PRs, DORA metrics, and code changes to improve your engineering workflow.
Unsure whether to use DORA or SPACE? Learn the key differences, see when to use each framework, and discover how to combine them for a complete picture of team performance.
A practical guide to using software metrics in software engineering. Learn how to stop managing with intuition and start using data to track DORA, reduce cycle times, and improve performance.
Unlock powerful software development insights with Keypup Custom Formulas. Learn to build BI-level reports, use operators, and analyze your engineering data like never before.
This dashboard is specifically designed to provide a comparative analysis of developer contributions against team-wide statistics, offering a nuanced view of performance, efficiency, and collaboration patterns.
While the SPACE framework provides a robust conceptual model to analyze Developer Productivity, the challenge lies in translating these dimensions into tangible, measurable insights. This is precisely where the Keypup SPACE Software Development Lifecycle (SDLC) analytics dashboard becomes indispensable. It moves beyond subjective assessments and provides factual data to understand, diagnose, and improve the development ecosystem.
Unlock the full potential of your DevOps workflow with the newly released Keypup API. Seamlessly integrate Keypup's powerful insights into your existing tools, automate tasks, and gain unprecedented visibility into your development process. Learn how to create custom Slack bots for streamlined standups, set up proactive monitoring alerts with tools like Datadog and New Relic, and build intelligent, data-driven DevOps flows that adapt to your team's needs. Enhance your team's productivity and build better software, faster, with the Keypup API.
Understanding and optimizing development team performance is a critical concern for engineering leaders. Recently, a customer reached out expressing their pressing need: "We are facing some problems about our dev team performance. Do you have a bunch or metrics that could help us compare and check the people performance?" This interaction highlighted a common challenge – a lack of clear metrics and tools to assess and improve team effectiveness. This blog post explores their situation and our data-driven approach to helping them gain valuable insights.
How can organizations objectively measure the impact of AI on their development teams? This article looks into two different approaches to conduct this quantitative evaluation. The first approach consists in evaluating the impact of AI tools uniformly in a given team, by comparing the overall Software Development Life Cycle (SDLC) performance before and after implementation of AI tools, whilst the second is comparative, and will compare the SDLC performance between 2 groups of developers: one that uses AI tools, and the other one that does not.
In the age of rapid technological advancements, the future of tech is a subject of great interest and debate. One of the most intriguing discussions revolves around the comparison between Artificial Intelligence (AI) and traditional software development. Exploring the pros and cons of AI and traditional software development can help us make informed decisions about which technology to adopt. In this article, we will delve into these aspects, examining the benefits and limitations of each approach and how to measure their respective merits.
AI-driven software development can represent a transformative force in the tech industry. The benefits of AI-driven solutions, such as accelerated development cycles and improved project management, position businesses to thrive in a competitive landscape. Assessing the real the impact of AI-driven software development, however, is crucial for maximizing its benefits. By establishing clear KPIs and analyzing both quantitative and qualitative data, organizations can gain valuable insights into how AI is transforming their development processes and whether the Return on investment of AI-driven development is matching expectations. This data-driven approach allows for continuous improvement, optimized AI adoption, and ultimately, the delivery of higher-quality software faster, more efficiently.