arXiv subjects

Lloyd Montgomery

Publications and source records attributed to Lloyd Montgomery.

21 records · Page 2Linked to original sources

arXiv2019

Towards a Live Anonymous Question Queue To Address Student Apprehension

In today's university climate many first and second year classes have over a hundred students. Large classrooms make some students apprehensive about asking questions. An anonymous method of submitting questions to an instructor would allow students to ask their questions without feeling apprehensive. In this paper we propose a Live Anonymous Question Queue (LAQQ), a system that facilitates anonymous question submissions in real time to mitigate student apprehension, increase student participation, and provide real-time feedback to the instructor. To study the necessary features of an LAQQ, we conducted a study of a system, namely Google Moderator, which best approached our concept of an LAQQ. We deployed Google moderator in large lectures and studied its support of a number of features that we envisioned for an LAQQ. Through our class observations, interviews with instructors, and surveys with the students, our results suggest that an LAQQ system must provide support for: notification of question submission to provide awareness for the instructor, and context for questions to allow an instructor to easily answer a question. Additionally our results suggest that an LAQQ system must be accessible and usable on multiple platforms. Finally our results suggest that in order to be successful in the classroom an LAQQ system must be fully adopted by the instructor and the classroom organizational structure must change to accommodate the use of the LAQQ.

cs.CY↗

arXiv2019

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 ...

cs.SE↗

arXiv2018

A Simple NLP-based Approach to Support Onboarding and Retention in Open Source Communities

Successful open source communities are constantly looking for new members and helping them become active developers. A common approach for developer onboarding in open source projects is to let newcomers focus on relevant yet easy-to-solve issues to familiarize themselves with the code and the community. The goal of this research is twofold. First, we aim at automatically identifying issues that newcomers can resolve by analyzing the history of resolved issues by simply using the title and description of issues. Second, we aim at automatically identifying issues, that can be resolved by newcomers who later become active developers. We mined the issue trackers of three large open source projects and extracted natural language features from the title and description of resolved issues. In a series of experiments, we optimized and compared the accuracy of four supervised classifiers to address our research goals. Random Forest, achieved up to 91% precision (F1-score 72%) towards the first goal while for the second goal, Decision Tree achieved a precision of 92% (F1-score 91%). A qualitative evaluation gave insights on what information in the issue description is helpful for newcomers. Our approach can be used to automatically identify, label, and recommend issues for newcomers in open source software projects based only on the text of the issues.

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