TeleDoCTR: Domain-Specific and Contextual Troubleshooting for Telecommunications

TL;DR

TeleDoCTR is a specialized system for telecom ticket troubleshooting that automates routing, retrieval, and report generation, significantly improving accuracy and efficiency in resolving telecom issues.

Contribution

It introduces a novel, domain-specific, and contextual troubleshooting system that combines ranking and generative models for end-to-end telecom ticket resolution.

Findings

Achieves superior performance over existing methods

Enhances troubleshooting accuracy and efficiency

Successfully applied to real-world telecom data

Abstract

Ticket troubleshooting refers to the process of analyzing and resolving problems that are reported through a ticketing system. In large organizations offering a wide range of services, this task is highly complex due to the diversity of submitted tickets and the need for specialized domain knowledge. In particular, troubleshooting in telecommunications (telecom) is a very time-consuming task as it requires experts to interpret ticket content, consult documentation, and search historical records to identify appropriate resolutions. This human-intensive approach not only delays issue resolution but also hinders overall operational efficiency. To enhance the effectiveness and efficiency of ticket troubleshooting in telecom, we propose TeleDoCTR, a novel telecom-related, domain-specific, and contextual troubleshooting system tailored for end-to-end ticket resolution in telecom. TeleDoCTR integrates both domain-specific ranking and generative models to automate key steps of the troubleshooting workflow which are: routing tickets to the appropriate expert team responsible for resolving the ticket (classification task), retrieving contextually and semantically similar historical tickets (retrieval task), and generating a detailed fault analysis report outlining the issue, root cause, and potential solutions (generation task). We evaluate TeleDoCTR on a real-world dataset from a telecom infrastructure and demonstrate that it achieves superior performance over existing state-of-the-art methods, significantly enhancing the accuracy and efficiency of the troubleshooting process.