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Case study

Major Telecommunications Provider Tests Software for 5G and 6G Networks With Endava

Telecommunications

Challenge

When testing their 5G and 6G networks, this global telecommunications provider discovered manual processes that could potentially be automated. With their existing manual processes, they experienced waterfall effects including extra time spent on fixes.

Outcome

By implementing automated testing, the telco provider was able to achieve greater efficiencies within their business, drastically reducing time spent on manual processes and improving their approach to whitelisting.

As one of the largest global telecommunications providers, this client is a leader in its field, using innovative technologies to keep people connected. When testing their 5G and 6G networks, the provider discovered manual processes that could potentially be automated. 

Looking for specific expertise in machine learning and testing, the telecommunications provider found the right partner in Endava. Working together on a new testing system would help the provider create efficiencies, streamline operations and drastically improve whitelisting methods and time spent on test cases. 

Navigating the waterfall effects of manual processes

Every time a new feature, patch or other form of software update was produced, it would be subjected to thousands of test cases to ensure the update’s compatibility with the set of radio units being targeted.  

 

The client’s main challenges included: 

  • The waterfall effects of manual processes, including extra time spent on fixes  
  • Implementing automation at scale with machine learning 
  • Sourcing expertise in machine learning to assist with testing 

Creating a new plan for acceleration

After assembling a game plan with the client, we got to work in creating and implementing a new automated whitelist processing system to accelerate radio unit test cases (RUTC) execution velocity. 

 

Our approach included: 

  • Integrating a fully automated, machine learning-based system capable of intelligently selecting and executing a comprehensive suite of test cases 
  • Creating a series of design iterations to validate improvements, including manual whitelisting, stage 1 automation and stage 2 threaded automation  
  • Supporting the design, validation, and improvement of individual test cases  
  • Developing a streamlined testing procedure 

Building a path to efficiency

After six months, the new machine learning system ran usefully in their environment, giving the client a new path to greater efficiencies. 

 

The project’s results included: 

  • Successfully whitelisted 2,037 RUTCs, covering 306 unique test cases across 10 radio units 
  • Implemented a new methodology that relaxed criteria for similar radio unit tests, enabling a reduction in interactive testing time by preemptively identifying test cases that had already been interactively tested 
  • Developed and deployed a drastically faster whitelisting method, dramatically reducing RUTC whitelisting time 
  • Achieved full interactive testing between RUTCs for targeted radio units, ensuring thorough coverage and validation 

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