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.

Users have higher expectations than ever when it comes to performance and reliability in the apps they use every day. A critical part of meeting these expectations is having a robust monitoring system in place. This article focuses on monitoring applications using a microservice architecture—it will go over key concepts, common challenges, and useful tools every engineer should know.

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.

There are many missteps in the app modernization journey. For more than ten years, we’ve worked with clients on hundreds of modernization projects, from single apps to portfolios of apps in large enterprises and our experience has led us to identify four of the most common anti-patterns impacting organizations.

Even the most reliable and well-designed software systems experience failures. Tracking incident response metrics helps teams strengthen both organizational preparedness and system resilience by uncovering trends, gaps, and opportunities for improvement. In short, important metrics for incident management are: Understanding these metrics helps engineering leaders improve service uptime, meet SLAs, and align operational capacity.

In the complex landscape of microservices within Amazon Elastic Kubernetes Service (EKS), maintaining seamless connectivity within microservices is paramount for ensuring optimal performance and reliability. As organizations increasingly embrace microservices architecture, understanding how to observe and troubleshoot microservices connectivity issues in Amazon EKS becomes a critical skill set.