The SPACE analytics dashboard: unlocking Developer Productivity through data-driven, actionable insights

The SPACE analytics dashboard: unlocking Developer Productivity through data-driven, actionable insights

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.

Thomas Williams
Keypup's New API: Supercharge Your Development Workflow and Unleash Your Team's True Potential

Keypup's New API: Supercharge Your Development Workflow and Unleash Your Team's True Potential

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.

Arnaud Lachaume
Measuring a Development Team’s Performance: a practical Customer Use Case

Measuring a Development Team’s Performance: a practical Customer Use Case

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.

Thomas Williams
Assessing the Efficiency of AI-Driven Development: 2 methods of quantitative evaluation of the impact of AI usage in Software Development

Assessing the Efficiency of AI-Driven Development: 2 methods of quantitative evaluation of the impact of AI usage in Software Development

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.

Thomas Williams
The Future of Tech: AI vs Traditional Software Development - Exploring & measuring the Pros and Cons

The Future of Tech: AI vs Traditional Software Development - Exploring & measuring the Pros and Cons

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.

Arnaud Lachaume