Testing & Troubleshooting challenges

  • Testing and release of new features takes a long time, thus causing a delayed time to market for new technologies, products, and services 

  • Lack of cross-platform and domain expertise required for root cause analysis(RCA) across multiple platforms. 

  • Too many handovers as operational troubleshooting involves numerous teams and resources. 

  • Network topology for end-to-end (E2E) troubleshooting is not always accurate. 

  • Constant network changes, revisions, and updates are expensive to maintain, operators will experience outages due to network upgrades. 

  • Long time to troubleshoot and resolve operational issues. 

 

B-Yond’s AI + Automation Solution

SaaS-based end-to-end (E2E) testing and root cause analysis (RCA) using a combination of B-Yond INFINITY and Services Solutions. Our deep knowledge and domain expertise in RCA involving all telco network platforms such as; EPC, IMS, Transport, and RAN, provides algorithms delivering high accuracy in failure classification and RCA in short reinforcement cycles. aRCA has the ability to automatically decipher underlying call flow patterns, root causes and reinforcement of the detected pattern using supervised feedback.

Key features include:

  • Automated RCA of E2E call flows spanning multiple platforms and signaling protocols 

  • Platform-specific testing eg: PGW, SGW, MME, MCPTT AS, etc. 

  • Capture of Test IQ on a continuous basis 

  • Automated creation of a learning repository or Test IQ database (DB)

  • Graph theory + Meta data + Platform and E2E trace logs = Automated data ingestion to to capture network topology and perform network RCA without custom parsers

  • Cloud native implementation 

  • Integrate with any and multiple OSS/BSS  

  • Support bare metal & cloud deployments

Business benefits

Increase RCA velocity exponentially.
Case Study Results: B-Yond demonstrated 35x–84x increase in RCA velocity for a Tier 1 operator.

Reduce human effort for RCA.
Case Study Results: B-Yond demonstrated ability to reduce level of effort by at least 25% with additional gains projected through reinforced learning. 

Double overall quality assurance (QA) velocity in regression phase.

Reduce time to integrate new tests.

Accelerate time to market for new features and reduce time to new revenue.