Hybrid Labels are the New Measure!

Nayebi, Maleknaz; Kabeer, Shaikh Jeeshan; Ruhe, Guenther; Carlson, Chris; Chew, Francis

doi:10.1109/ms.2018.110164344

IEEE Softw.

2018

DOI: 10.1109/ms.2018.110164344

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Hybrid Labels are the New Measure!

Maleknaz Nayebi

Shaikh Jeeshan Kabeer

Guenther Ruhe

et al.

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“…As a case study, we evaluated ESSMArT using data from the development of a Health Communication system offered by a company called BrightSquid. Analyzing customer change requests is an important part of their development process [33]. In particular, from analyzing a set of 4,114 former user requests, we answered the following research questions: RQ1: Automated condensing of user requests -Among existing state-of-the art techniques for condensing user requests by extractive summarization, which one works best in terms of F1 accuracy?…”

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ESSMArT way to manage customer requests

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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 platform 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

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ESSMArT way to manage customer requests

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Hybrid Labels are the New Measure!

Cited by 1 publication

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ESSMArT way to manage customer requests

ESSMArT way to manage customer requests

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