3 research outputs found
ESSMArT Way to Manage User Requests
Quality and market acceptance of software products is strongly influenced by
responsiveness to user requests. Once a request is received from a customer,
decisions need to be made if the request should be escalated to the development
team. Once escalated, the ticket must be formulated as a development task and
be assigned to a developer. To make the process more efficient and reduce the
time between receiving and escalating the user request, we aim to automate of
the complete user request management process. We propose a holistic method
called ESSMArT. The methods performs text summarization, predicts ticket
escalation, creates the title and content of the ticket used by developers, and
assigns the ticket to an available developer. We internally evaluated the
method by 4,114 user tickets from Brightsquid and their secure health care
communication plat- form Secure-Mail. We also perform an external evaluation on
the usefulness of the approach. We found that supervised learning based on
context specific data performs best for extractive summarization. For
predicting escalation of tickets, Random Forest trained on a combination of
conversation and extractive summarization is best with highest precision (of
0.9) and recall (of 0.55). From external evaluation we found that ESSMArT
provides suggestions that are 71% aligned with human ones. Applying the
prototype implementation to 315 user requests resulted in an average time
reduction of 9.2 minutes per request. ESSMArT helps to make ticket management
faster and with reduced effort for human experts. ESSMArT can help Brightsquid
to (i) minimize the impact of staff turnover and (ii) shorten the cycle from an
issue being reported to an assignment to a developer to fix it.Comment: This is a preprint of the submission to Empirical Software
Engineering journal, 201
What do Support Analysts Know about Their Customers? On the Study and Prediction of Support Ticket Escalations in Large Software Organizations
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. 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 ...Comment: Accepted for publication to the 25th International Requirements
Engineering Conference (RE'17
Predicting Software Escalations with Maximum ROI
Enterprise software venders often have to release software products before all reported defects are corrected, and a small number of these reported defects will be escalated by customers whose businesses are seriously impacted. Escalated defects must be quickly resolved at a high cost by the software vendors. The total costs can be even greater, including loss of reputation, satisfaction, loyalty, and repeat revenue. In this paper, we develop an Escalation Prediction (EP) system to mine historic defect report data and predict the escalation risk of current defect reports for maximum ROI (Return On Investment). More specifically, we first describe a simple and general framework to convert the maximum ROI problem to cost-sensitive learning. We then apply and compare several best-known cost-sensitive learning approaches for EP. The EP system has produced promising results, and has been deployed in the product group of an enterprise software vendor. Conclusions drawn from this study also provide guidelines for mining imbalanced datasets and costsensitive learning. 1