From its initial appearance in the dev-tools space, GenAI has had an outsized impact on how developers approach day-to-day tasks (just ask any developer about when they first started using GitHub’s copilot). While any risks are still being evaluated—like potential for introducing anti-patterns or inadvertently running afoul of compliance requirements, many engineering teams have successfully implemented GenAI with measurable gains in collaboration and productivity.

Companies obsess over end user experience, whether it is Amazon’s customer-centric innovation or Steve Jobs suggesting starting with the customer experience and working backwards to technology. But as our world becomes more knowledge-based and digital, we also need to consider the most important stakeholder on the payroll - software engineers.

Observability tools help engineering teams understand the health and behavior of software. But the term “health” in the context of this type of tooling is fairly narrow in scope—pertaining to real-time performance, reliability, and availability. While these are three important metrics to monitor, they’re lagging indicators of bigger issues happening upstream.

You might have heard of Spotify Backstage (backstage.io), an open-source option for building internal developer portals. But what is Spotify Backstage? This article will help answer your questions about Backstage and explain important considerations to think about if you’re deciding whether to adopt in your team.

Quality engineering (QE), or software quality engineering (SQE), is a discipline within software development focused on ensuring the quality, reliability, and performance of software products. With an increase in development environment complexity in recent years, the focus has shifted back from detecting defects in later stages, as QA has typically done, to proactively ensuring quality throughout the entire development lifecycle.

Reliability metrics in software development are metrics that help teams quantify how dependable and consistent their software systems are over time. By converting a wide range of technical properties into hard data, these provide quantifiable information to understand the probability of software running failure-free in a given environment over time. These metrics are a subset of developer-focused key performance indicators (KPIs), data that is gathered to emphasize developers' output.

Software development is a sophisticated process that comes with complexity built in. Enter DevOps (many years ago, of course) to foster an environment where developers can build complex applications while minimizing backend overhead, ensuring that processes are replicable and software design is standardized. One of the first examples of DevOps in action was the surge in popularity for service-oriented architecture.

Software engineering key performance indicators (KPIs) help engineer leadings keep teams accountable while ensuring focus on highest leverage activities. They are essential for driving process improvement, managing risks, supporting data-driven decision making, and ensuring customer satisfaction. Without KPIs, teams may encounter challenges related to visibility, efficiency, decision-making, quality, and customer satisfaction, which can ultimately impact project success and organizational performance.

Agile metrics are key performance indicators (KPIs) that help measure, evaluate, and optimize the efficiency of Agile software development practices, processes, and outputs. They provide visibility into how well Agile teams are delivering value, enabling data-driven decisions, and fostering continuous improvement. Agile metrics sit under the broader umbrella of software engineering metrics, which track code quality, system performance, release velocity, and more.

Good software development teams are focused on outputs, and can bring key metrics to bear that illustrate just what the engineering organization is building on a daily, monthly and yearly basis. Developer productivity is often assessed retrospectively: if the team is hitting DORA metrics, we assume everything in the lifecycle before production is sound. But the best teams dig deeper, and aim to solve the problem backwards as well as forwards by looking at the process as well as the results.