Stakes
Walmart Labs operated at massive engineering scale, running thousands of automation tests across mobile and web platforms in parallel. Continuous integration systems validated releases around the clock, yet visibility into build health, failure severity, and performance drift was fragmented across multiple tools.
Signal volume was increasing rapidly, but interpretable clarity was not.
Engineering velocity depended on real-time insight that did not yet exist in a unified form.
Operational Risk
As test volume and concurrency increased, visibility decreased. Engineers spent significant time interpreting logs, navigating disconnected dashboards, and manually aggregating signals to understand system health. Diagnostic effort scaled alongside complexity, slowing development velocity and increasing cognitive load.
As concurrency scaled beyond human interpretability, signal density exceeded signal clarity.
The Challenge
This was not a dashboard redesign. It was a signal-to-decision architecture problem.
The goal was to transform high-volume automation output into prioritized, actionable insight engineers could trust in real time. The system needed to surface build health, isolate failure severity, detect performance drift, and support rapid diagnostic workflows—all within a single coherent interface.
It had to support more than 900 concurrent test executions, scale across mobile and web environments, reduce diagnostic latency, and drive sustained adoption across 30,000 engineers without disrupting live delivery cycles.
Clarity had to scale with complexity.
My Role
I led UX strategy and design for the continuous integration and functional testing platform. My role spanned research, engineer interviews, persona development, workflow analysis, architectural modeling, user story definition, wireframing, prototyping, and final UI design.
Beyond interface design, the work required simplifying system-level workflows and restructuring how automation signals were surfaced and prioritized.
This was as much an information architecture effort as it was a product design effort.
Strategic Approach
Rather than layering visual refinement onto existing reporting tools, we reframed the effort around signal architecture. Engineers were not lacking data; they were overwhelmed by it. The issue was not data availability but prioritization and interpretability.
We redesigned the system to elevate real-time health signals, reduce noise, standardize severity states, and integrate monitoring and investigation into a unified workflow. Progressive disclosure allowed engineers to scan system health at a macro level, then move seamlessly into granular diagnostics without context switching.
We began by diagnosing how engineers interpreted automation signals across tools and environments.
Structural Insight
Research revealed that diagnostic workflows were fragmented not only across tools but across mental models. Different teams interpreted build health and failure states differently depending on domain context.
We reorganized automation outputs into a unified signal architecture that emphasized actionable states over raw logs. Health indicators, trend trajectories, build states, and test results were structured to reduce interpretation time and enable rapid decision-making under pressure.
Clarity became the primary performance objective.
Workflow Architecture
The system supported real-time visibility across more than 900 concurrent test executions. Engineers could identify build instability, isolate failure clusters, trace errors across environments, and evaluate trend history within a single interface.
The interaction architecture balanced speed and depth. Engineers could remain at a macro monitoring view or transition into granular diagnostic detail without leaving context. This eliminated repeated environment switching and reduced diagnostic time significantly.
Interface System
The final interface introduced a unified CI health dashboard capable of aggregating test results, severity indicators, historical performance trends, and parallel execution states in real time.
Visual hierarchy emphasized signal intensity and performance drift. Temporal patterns became immediately visible. Failure impact and recurrence were surfaced alongside trend context, supporting faster resolution decisions.
Mobile and web interfaces were aligned to ensure consistency across devices, enabling engineering teams to monitor system stability wherever they were operating.
Outcomes
The platform increased adoption across engineering teams and reduced time spent on CI diagnostics by approximately 25 percent for more than 30,000 engineers.
As part of a broader performance tooling initiative, the effort contributed to an estimated 635,000 engineering hours saved across 74 global teams. Automation infrastructure scaled to support more than 900 concurrent tests with reliable, real-time visibility.
Engineering throughput increased while cognitive load decreased.
Operational clarity scaled alongside technical scale.
Strategic Reflection
At enterprise engineering scale, productivity is constrained by signal clarity. Fragmented systems create diagnostic drag, which compounds across teams. When signal is unified, interpretable, and actionable, velocity increases without increasing effort.
Designing for engineers requires designing for leverage. Systems visibility is a performance multiplier.
