Apexon worked with this global telecom giant beginning in 2018 for over a year.
The focus was on optimizing device and service testing and Apexon deployed a solution utilizing AI to optimize the most crucial components of the QE process.
Success Story High Tech
Global Telecom Accelerates New Digital Services with AIApexon optimizes QE methodology to keep up with rapid release cycles
This company is one of the world’s leading telecommunications groups, with a significant presence in Europe, the Middle East, Africa and Asia-Pacific.
A leader in network quality, the company prides itself on offering excellent customer experience and providing integrated, worry-free solutions. The company’s roots are in mobile, but its digital service offering has expanded dramatically across multiple channels in order to stay ahead of the competition.
This put enormous pressure on delivery teams to churn out multiple applications, devices, and maintenance releases. It’s QE processes could not keep up and service levels and customer satisfaction suffered.
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450M+ mobile customers
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10m+ fixed broadband customers
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9M+ TV customers
the Results
Key Outcomes
Cost Savings
Cost savings of nearly 1.4M EURO over a three-year period
Faster Cycle Time
More effective testing cycles are speeding up testing execution by as much as 35%
Higher Service Levels
Better ability to predict risks and failures prevents device and application errors
Go Digital
Accelerating the delivery of new digital initiatives with confidence
Be digital
Creating the infrastructure and foundation to scale digital initiatives
Evolve Digital
Leveraging data and analytics to continuously improve digital delivery processes
Enable digital adoption in a quick, and agile manner
Apexon developed QE strategies and solutions that enabled the company to continue launching and updating devices at frequent intervals, but with lower risk and higher accuracy.
Build digital infrastructure and foundation for enterprises to scale
Apexon developed an AI-powered testing framework to ensure that a diverse universe of devices, services and apps work seamlessly on the company’s networks.
Leverage data engineering to make strategic decisions and get digital right every time
Apexon implemented tools for predictive analytics and prescriptive insights based on historical data to assess risk level and test case failure probability for each area of an upcoming release.
The challenge
Heightened QE load & complexity
The rapid adoption of smartphones and next generation digital devices resulted in OEMs launching and updating devices at more frequent intervals. Similarly, new applications and digital services were also being launched to engage customers.
Unable to handle growth unassisted
The company needed a partner with the ability to leverage the latest digital tools and platforms to help it get control of its growing QE requirements for up to 40 different applications, 200 new devices, 25 different device families and 1,000+ maintenance releases.
Lacking technology solutions
The company needed a solution and platform that could leverage the latest technologies like AI, machine learning, cloud computing, and predictive analytics to manage their hefty yearly validation requirements.
Dated digital services
The inability to provide engaging digital experiences and service quality would lead directly to dissatisfied customers and revenue loss.
Difficulty keeping up with evolving technology
This explosion of applications, devices, and maintenance dramatically increased the QE load for the company.
Testing difficulties
Increased testing complexity further exacerbated the situation.
The Solution
Optimizing the qe process
Apexon set out to optimize the most crucial components of the company’s QE process using ASTUTE, an AI-powered accelerator tool that uses machine learning.
Using ASTUTE, Apexon provided predictive analytics and prescriptive insights based on historical testing data.
Leveraging data from past releases such as defect data and release notes – features implemented and defects fixed, ASTUTE provided risk levels and test case failure probabilities for each area of an upcoming release.
6-12 Month Timeline
Platform Development, On-Boarding Service, Deployment and Rollout.
- App Testing
- Device Testing
- Test Case Optimization
- Module Risk Prediction
Based on historical defects
- Defect Prediction
For a future regression test run based on the same historical defects, source code process metrics and current release information
- Integration into Test Automation Framework
To automatically trigger Jenkins job
- Regression Test Generation
Based on historical defects and current release information data
With the solution in place, the company has gained substantial cost savings, faster time to market and increased revenue capability.
Some of the key enablers of these benefits include:
80% average risk prediction accuracy for device testing
77% test case failure accuracy for device testing
71% defect range prediction accuracy for app testing
83% average risk prediction accuracy for app testing
50% decrease in total 12K data rows, data set