Cutting Regression Time From 624 Hours to 50

A global cruise line leader saw defective mobile apps drive guest dissatisfaction, with low automation coverage and slow manual regression delaying launches. Myridius reassessed the QA operating model and automated more than 90 percent of the regression suite, cutting regression execution from 624 hours to 50 and reducing regression run cost by more than 90 percent.

Key Outcomes

• More than 90 percent of the regression suite automated.
• Regression execution cut from 624 hours to 50 hours.
• More than 90 percent reduction in regression run cost.

Overview

A global cruise line leader faced guest dissatisfaction driven by defective mobile apps. Gaps in the existing QA process allowed defects to leak into production, while low test automation coverage increased time-to-market across web, mobile, and shipboard applications, and slow manual regression cycles delayed launches. Myridius performed an exhaustive assessment of the QA process to diagnose root causes and redesign regression execution, then materially expanded automated testing across customer-facing channels. As a result, the client automated more than 90 percent of the existing regression suite, cut regression execution time from 624 hours to 50 hours, achieved more than a 90 percent reduction in regression run cost, and improved software quality and time-to-market across web, mobile, and shipboard applications.

Client Context

The client is a global cruise line leader delivering digital experiences to guests across web, native mobile, and shipboard applications.

Reliable QA mattered here because defective apps directly harmed the guest experience, and slow manual regression cycles constrained how quickly the business could release new features. What was at stake was both guest satisfaction and release velocity across a complex set of customer-facing channels.

The Challenge

Defective mobile apps were driving guest dissatisfaction and a sub-par customer experience. Gaps in the existing QA process allowed defects to leak into production, while low test automation coverage increased time-to-market and manual regression cycles delayed launches. The desired state was a stronger QA operating model with high automation coverage and fast, low-cost regression.

Consider a release cycle. Running regression manually took 624 hours, a multi-week effort that delayed launches and constrained release velocity, while coverage gaps let defects slip into production where guests encountered them. The combination undermined both quality and speed.

Transformation Goals

The engagement focused on north stars that connected stronger QA discipline to higher automation and faster, lower-cost releases.

• Reduce Defect Leakage: Strengthen QA governance and regression discipline to keep defects out of production.

• Increase Automation Coverage: Expand automation to accelerate releases across web, mobile, and shipboard applications.

• Lower Regression Cost and Time: Reduce regression execution time and cost while improving overall software quality.

The Solution

The engagement assessed the QA process to diagnose root causes of production defects and redesign regression execution, then expanded automation across customer-facing channels. Myridius orchestrated a refreshed QA operating model, embedded automated testing into the release process, and reimagined regression as a fast, repeatable capability. The progression moved from deploying a QA assessment and program-management approach, to embedding regression automation at scale, to reimagining release readiness as fast and consistent.

• Orchestrated the foundation: Reviewed existing QA process gaps, identified root causes of production defects, and recommended improvements to regression strategy and program management.

• Embedded intelligence into the journey: Streamlined integration and acceptance testing for customer-facing web and native mobile applications to improve the testing workflow.

• Reimagined the operating model: Automated more than 90 percent of the existing regression suite to reduce manual effort and accelerate release readiness across channels.

Governance and Trust

Because this engagement reshaped how quality was assured across customer-facing channels, governance and regression discipline were central. The QA assessment identified root causes of defect leakage and established stronger program management and regression strategy, embedding quality control into the release process rather than leaving it to manual, ad hoc effort.

Automating more than 90 percent of the regression suite gave the program consistent, repeatable coverage, reducing the variability and risk of manual testing. Streamlined integration and acceptance testing for web and native mobile applications ensured that quality checks were applied consistently across the channels guests rely on.

Results

The engagement transformed slow, manual, leaky QA into a fast, automated, disciplined operating model. The result was dramatic time and cost reductions and better software quality.

The result:

• Automated more than 90 percent of the regression suite to improve release velocity and consistency.

• Reduced regression execution time from 624 hours to 50 hours, a more than 90 percent run-cost reduction.

• Improved software quality and time-to-market across web, mobile, and shipboard applications.

Technology Stack

Quality Engineering

QA process assessment, QA program management improvements
Diagnose root causes and strengthen regression discipline

Testing Scope

Web, native mobile, and shipboard application testing
Cover the full set of customer-facing channels

Automation

Regression suite automation (more than 90 percent coverage)
Cut manual effort and accelerate release readiness

Test Workflow

Streamlined integration and acceptance testing
Improve testing flow for web and mobile applications

For a digital-first travel brand, slow manual regression is a direct brake on release velocity and a risk to guest experience. This case shows how regression automation turns QA into a speed advantage. This was not a tooling tweak. It was a QA operating-model redesign with regression automation at scale.