How Software Quality Metrics Boost Team Performance

Measuring software delivery speed has become second nature for many of today’s tech leaders. But what about the quality of the code being delivered?

Popular frameworks like DORA have championed metrics such as deployment frequency and change lead time. While valuable, these metrics focus almost exclusively on velocity. This helps tell us how fast teams are shipping code, but not whether the code is any good.

This blind spot in software performance measurement is beginning to show. According to BlueOptima’s Global Drivers of Performance: Quality report, organisations focusing on delivery metrics alone risk missing deeper, structural issues that influence long-term productivity, maintainability, and cost.

The message is clear: to improve performance sustainably, businesses must start measuring the right quality metrics.

Not All Metrics Are Created Equal

Speed-focused metrics dominate DevOps dashboards for understandable reasons. They are relatively easy to collect and track. But as McKinsey and other analysts have noted, these workflow metrics offer only part of the picture.

More nuanced insights require a deeper look at the source code itself. That is where metrics like Aberrant Coding Effort (Ab.CE), developed by BlueOptima, provide a decisive advantage.

Unlike workflow metrics that tell you how quickly code moves through a pipeline, Ab.CE measures the maintainability of the code being written. It captures how much of a developer’s output deviates from recognised best practices, highlighting issues like unnecessary complexity or poor readability.

High Ab.CE scores correlate with lower developer productivity, higher rates of rework, and ultimately, greater cost. On the flip side, developers with lower Ab.CE scores deliver cleaner, more maintainable code, and they do so faster.

The Quality–Performance Link

The numbers speak volumes. According to BlueOptima’s report, improving a developer’s Ab.CE rating from “Requires Improvement” to “Moderate” can result in tangible cost savings. Improvements from “Good” to “Best” generate even more value, with nearly 19% lower cost per unit of effort. Across large teams, these marginal gains accumulate quickly into six- or seven-figure annual savings

More importantly, the report shows that quality improvements are not just about individual developers. The pre-existing maintainability of a codebase, measured through metrics like File-Level Analysis of Relative Thresholds (FLART), has the greatest influence on future code quality. In other words, even your best engineers will struggle to perform if forced to work in a poorly maintained codebase.

This finding aligns with broader industry insights and research. Effective quality metrics must be “sound, consistent, and actionable” to truly guide improvement.

Key Metrics That Matter

Understanding and tracking the right metrics is crucial for improving code quality and team performance:

• Aberrant Coding Effort (Ab.CE): Represents the percentage of a developer’s coding effort that deviates from established best practices, signalling unmaintainable code. Lower Ab.CE values are strongly associated with higher productivity and lower development costs.
  
• File-Level Analysis of Relative Thresholds (FLART): Provides a maintainability score for each file based on static analysis post-commit. It helps teams identify problematic files before they become sources of technical debt.
  
• Developer-Level ART (DART): Measures how frequently a developer’s code violates maintainability thresholds. It’s a powerful indicator of individual code quality and adherence to organisational standards.
  
• Design Anti-Patterns: Identifies recurring structural issues, such as God Classes, poor readability, or functionality overload, that correlate with declining code maintainability and developer efficiency.
  

These metrics go beyond surface-level performance, enabling teams to detect and resolve quality issues before they escalate into major blockers.

Design Patterns Over Delivery Speed

A key insight from the report is the critical role of design anti-patterns in dragging down code quality. A regression model linking these patterns to Ab.CE scores showed them to be one of the strongest predictors of maintainability outcomes.

What does this mean in practice? It suggests that organisations should move beyond generic notions of “good code” and embed structured, pattern-driven development practices. Rather than optimising purely for speed, teams must invest in consistent architecture, meaningful refactoring, and static code analysis.

Balancing Speed and Quality

The takeaway isn’t to abandon DORA metrics and other frameworks entirely. Rather, it’s to support them with greater, more meaningful, visibility. Delivery speed certainly matters. But so does the quality of what is being delivered.

As BlueOptima’s report shows, pairing velocity metrics with maintainability metrics like Ab.CE and FLART provides a fuller, more actionable view of engineering performance. This combination empowers leaders to pinpoint which teams are succeeding and, more importantly, why.

At a time when the cost of poor-quality software is rising, these insights are more than academic. Back in 2021, a BusinessWire study estimated that ineffective software delivery could cost enterprises up to $126 million annually. In today’s competitive landscape, where speed and quality are both non-negotiable, that figure is likely even higher. For enterprise leaders, overlooking code quality is no longer just a technical oversight, it’s a strategic liability.

Learn More

For organisations looking to boost engineering performance, the key lies in measuring what matters. The Global Drivers of Performance: Quality report offers in-depth analysis, practical recommendations, and real-world data drawn from over 500,000 developers across 43 enterprises.

It is essential reading for any executive serious about software quality, cost control, and sustainable delivery velocity.

Download the full report here.