SOFTWARE ENGINEERING GLOBAL INTERACTION LAB

Automated Solutions for Support Analysts in Large Software Companies

Project Details

  • Project Overview
    Our work provides a step towards simplifying the job of support analysts and managers, particularly in predicting the risk of escalating support tickets.
  • SEGAL Members Involved
    Lloyd Montgomery, Daniela Damian


Project Description

Understanding and keeping the customer happy is a central tenet of requirements engineering. Strategies to gather, analyze, and negotiate requirements are complemented by efforts to manage customer input after products have been deployed. For the latter, support tickets are key in allowing customers to submit their issues, bug reports, and feature requests. Whenever insufficient attention is given to support issues, however, their escalation to management is time-consuming and expensive, especially for large organizations managing hundreds of customers and thousands of support tickets. Our work provides a step towards simplifying the job of support analysts and managers, particularly in predicting the risk of escalating support tickets.

Project Process and Results

In a field study at our large industrial partner, IBM, we used a design science methodology to characterize the support process and data available to IBM analysts in managing escalations. Through iterative cycles of design and evaluation, we translated our understanding of support analysts’ expert knowledge of their customers into features of a support ticket model to be implemented into a Machine Learning model to predict support ticket escalations. We trained and evaluated our Machine Learning model on over 2.5 million support tickets and 10,000 escalations, obtaining a recall of 79.9% and an 80.8% reduction in the workload for support analysts looking to identify support tickets at risk of escalation. Further on-site evaluations, through a prototype tool we developed to implement our Machine Learning techniques in practice, showed more efficient weekly support-ticket-management meetings. The features we developed in the Support Ticket Model are designed to serve as a starting place for organizations interested in implementing our model to predict support ticket escalations, and for future researchers to build on to advance research in escalation prediction. Questions from our analysis can be found Here.

Lloyd Montgomery, and Daniela Damian. What do Support Analysts Know about Their Customers? On the Study and Prediction of Support Ticket Escalations in Large Software Organizations, in Proc. of IEEE International Conference on Requirements Engineering. August 2017, to appear.

Lloyd Montgomery, Emma Reading, and Daniela Damian. ECrits – Visualizing Support Ticket Escalation Risk, in Proc. of IEEE International Conference on Requirements Engineering. August 2017, to appear.

IBM Centre for Advanced Studies & CGSM NSERC

Related Posts

26Jan

REACH Awards (Excellence in teaching awards)

0 Comments
Daniela Damian is a critically reflective educator and leader in software engineering, who has created innovative cross-cultural experiential learning practices... Read More →
16May

MSc and PhD positions in SEGAL!

0 Comments
Our lab  is seeking applications for MSc and Doctoral research positions in the area of software ecosystems. The research pertains to... Read More →
13Apr

Supporting the Adaptation of Contextual Requirements at Runtime

0 Comments
Today’s complex operating environments require systems to capture and document context together with requirements. Such contextual requirements are used by... Read More →
30Jan

Paper accepted at ICSE 2015!

0 Comments
Our paper “Learning Global Agile Software Engineering Using Same-Site and Cross-Site Teams” has been accepted for JSEET track at ICSE 2015.... Read More →
11Jan

Congratulations!

0 Comments
Our paper “Open Source-Style Collaborative Development Practices in Commercial Projects Using GitHub” has been accepted at ICSE 2015. Congratulations Eirini,... Read More →
Back to Top