  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.
