Lloyd M
MSc > >
Personal
Details
Demographics
  • Citizenship
    Canadian
  • Childhood SES
    Middle Class
  • Sex
    Male
  • Pronouns
    He/Him/His
  • Gender Expression
    Come meet me!
  • Gender Identity & Attraction
    Private
Degrees
  • BSc in Computer Science
    2010 - 2015
    University of Victoria
    GPA - 4.0
  • MSc in Computer Science
    2015 - 2017
    University of Victoria (Dr. Damian)
    GPA - 4.0
  • PhD in Computer Science
    2018 - Present
    University of Hamburg (Prof. Dr. Maalej)
Hobbies / Interests
  • Fitness
    Running, Squash, Badminton, Soccer
  • Gaming
    Factorio, Stardew Valley, Minecraft
  • Languages
    English (Native), Deutsch (A2)
  • Music
    Saxophone, Piano, Spotify
Development
  • Machine Learning
    Python, Jupyter Notebooks
  • Web Dev
    HTML, CSS, JS, JQuery
  • Web Frameworks
    Statamic, Vue, React
  • Databases
    MySQL, MongoDB, Firebase
  • Web 3.0
    APIs, Tool Reuse, Server-Based Controllers
Career
Research
Interests
  • Requirements Quality
    Defining, improving, and evaluating the quality of requirements are important facets of Requirements Engineering, both for researchers and for industry.
  • Recommender Systems
    Leveraging Big Data requires systems that can reliably interpret and recommend data to the persons involved.
Projects
  • Requirements Quality
    2021 - 2021
  • OpenReq
    2017 - 2020
  • Automated Solutions for Support Analysts at IBM
    2015 - 2017
Publications See all 12
  • Renovating Requirements Engineering: First Thoughts to Shape Requirements Engineering as a Profession
    Y Pham, L Montgomery, W Maalej
    3rd International Workshop on Learning from other Disciplines for RE (D4RE)
    Jeju Island, South Korea
    Workshop Paper
    DOI
    Legacy software systems typically include vital data for organizations that use them and should thus to be regularly maintained. Ideally, organizations should rely on Requirements Engineers to understand and manage changes of stakeholder needs and system constraints. However, due to time and cost pressure, and with a heavy focus on implementation, organizations often choose to forgo Requirements Engineers and rather focus on ad-hoc bug fixing and maintenance. This position paper discusses what Requirements Engineers could possibly learn from other similar roles to become crucial for the evolution of legacy systems. Particularly, we compare the roles of Requirements Engineers (according to IREB), Building Architects (according to the German regulations), and Product Owners (according to "The Scrum-Guide"). We discuss overlaps along four dimensions: liability, self-portrayal, core activities, and artifacts. Finally we draw insights from these related fields to foster the concept of a Requirements Engineer as a distinguished profession.
  • Customer Support Ticket Escalation Prediction using Feature Engineering
    L Montgomery, D Damian, T Bulmer, S Quader
    Springer Requirements Engineering Journal (REJ)
    Journal Paper
    DOI
    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. If insufficient attention is given to support issues, however, their escalation to management becomes time-consuming and expensive, especially for large organizations managing hundreds of customers and thousands of support tickets. Our work provides a step toward 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 research methodology to characterize the support process and data available to IBM analysts in managing escalations. In a design science methodology, we used feature engineering to translate our understanding of support analysts’ expert knowledge of their customers into features of a support ticket model. We then implemented these features 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 87.36% and an 88.23% 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. Finally, in addition to these research evaluation activities, we compared the performance of our support ticket model with that of a model developed with no feature engineering; the support ticket model features outperformed the non-engineered model. The artifacts created in this research are designed to serve as a starting place for organizations interested in predicting support ticket escalations, and for future researchers to build on to advance research in escalation prediction.
  • Predicting Developers' IDE Commands with Machine Learning
    T Bulmer, L Montgomery, D Damian
    ACM 15th Mining Software Repositories (MSR)
    Gothenburg, Sweden
    Research Paper
    DOI
    When a developer is writing code they are usually focused and in a state-of-mind which some refer to as flow. Breaking out of this flow can cause the developer to lose their train of thought and have to start their thought process from the beginning. This loss of thought can be caused by interruptions and sometimes slow IDE interactions. Predictive functionality has been harnessed in user applications to speed up load times, such as in Google Chrome's browser which has a feature called "Predicting Network Actions". This will pre-load web-pages that the user is most likely to click through. This mitigates the interruption that load times can introduce. In this paper we seek to make the first step towards predicting user commands in the IDE. Using the MSR 2018 Challenge Data of over 3000 developer session and over 10 million recorded events, we analyze and cleanse the data to be parsed into event series, which can then be used to train a variety of machine learning models, including a neural network, to predict user induced commands. Our highest performing model is able to obtain a 5 cross-fold validation prediction accuracy of 64%.
  • A Simple NLP-Based Approach to Support Onboarding and Retention in Open Source Communities.
    C Stanik, L Montgomery, D Martens, D Fucci, W Maalej
    IEEE 34th International Conference on Software Maintenance and Evoluation (ICSME)
    Madrid, Spain
    Workshop Paper
    DOI
    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.
  • What do Support Analysts Know About Their Customers? On the Study and Prediction of Support Ticket Escalations in Large Software Organizations
    Best Paper Award
    L Montgomery, D Damian
    IEEE 25th International Requirements Engineering Conference (RE)
    Lisbon, Portugal
    Research Paper
    DOI
    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 escalation prediction.
Funding
Funding & Awards See all 13
  • NSERC Postgraduate Scholarships - Doctoral
    $ 63,000
    - Natural Sciences and Engineering Research Council of Canada
    The NSERC Postgraduate Scholarships – Doctoral (PGS D) program provides financial support to high-calibre scholars who are engaged in an eligible doctoral program in the natural sciences or engineering. This support allows these scholars to fully concentrate on their studies and seek out the best research mentors in their chosen fields.
  • NSERC Canada Graduate Scholarships - Masters
    $ 17,500
    - Natural Sciences and Engineering Research Council of Canada
    The CGSM Program provides financial support to high-calibre scholars who are engaged in eligible master’s programs in Canada. This support allows these scholars to fully concentrate on their studies in their chosen fields.
  • Fellowship
    $ 26,000
    - IBM Center for Advanced Studies
    The IBM PhD Fellowship Program advances this collaboration by recognizing and supporting exceptional PhD students who want to make their mark in promising and disruptive technologies.
  • Outstanding Graduate Entrance Award
    $ 5,000
    - University of Victoria
    The Faculty of Graduate Studies provides awards to graduate students of high academic standing.
  • NSERC Undergraduate Student Research Award
    $ 4,500
    - Natural Sciences and Engineering Research Council of Canada
    The Natural Sciences and Engineering Research Council of Canada (NSERC) subsidizes eligible professors to hire students to work on their research projects. The program creates interesting research-related jobs and gives you the opportunity to gain valuable work experience.
Teaching
Current Courses
  • M-Lab - Innovation Laboratory for App Development
    2018, 2019, 2020
    Teaching Assistant
    In this teaching and innovation laboratory students develop innovative Apps in small teams, under real conditions and tight project deadlines, for real customers from industry, society and the public sector.
  • EMSE - Empirical Software Engineering
    2018, 2019, 2020
    Teaching Assistant
    We teach students the basics of empirical research methods, and offer them the opportunity to identify, extract, and critique the use of these methods in existing research papers in Software Engineering.
Past Courses
  • CSC 375 - Introduction to Systems Analysis
    2015, 2016, 2017
    Teaching Assistant
  • SENG 321 - Requirements Engineering
    2015, 2016, 2017
    Teaching Assistant
  • CSC 105 - Computers and Information Processing
    2016
    Teaching Assistant
  • SENG 299 - Software Architecture and Design
    2015
    Teaching Assistant
Service
Conferences Attended
  • ICSE
    2016, 2020
  • RE
    2017, 2020
  • PROFES
    2019
  • REFSQ
    2017, 2018
  • CASCON
    2016
  • WCCCE
    2015
Review Committees
  • ESEC/FSE Artefact Track PC
    2019
  • ICSE Artefact Track PC
    2020, 2021
  • NLP4RE PC
    2020, 2021
  • RE Artefact Track PC
    2019, 2020
Community
Commitments
Responsibilities and Accomplishments
  • Pint of Science Germany Co-Director
    Jul 2020 - Present
    • > Manage a central team of volunteers across Germany
    • > Manage ~150 city volunteers across Germany
    • > Conceptualise and orchestrated online conference alternative for Pint of Science Germany 2020
  • Pint of Science Germany Cities Coordinator
    Oct 2020 - Present
    • > Coordinate with all Pint of Science cities across Germany
    • > Onboard new cities to Pint of Science Germany
    • > Inform cities of updates and discuss next steps
    • > Established 18 new cities for Pint of Science Germany 2020 (originally 8)
    • > Created workshops and documents to communicate critical information
  • Pint of Science Hamburg Event Manager
    Sep 2018 - Present
    • > Host evenings of science communication at local pubs
    • > Filled 2 local venues with participants interested in learning about science
  • Hour of Code Organiser
    Oct 2015 - Dec 2015
    • > Communicate the joy of programming through simple exercises
    • > Delivered three Hour of Code workshops to students in Grades 1, 8, & 11
  • Learn2Solve Founder
    Jan 2011 - Aug 2013
  • Salvation Army Summer Camp Volunteer
    Jul 2009 - Aug 2010
  • Veterinarian Assistant
    Dec 2008 - Dec 2008
  • Chiropractic Assistant
    Oct 2008 - Oct 2008
  • Romania Missions Trip
    Jul 2007 - Aug 2007
Family
Details
Description

I choose not to participate in social media because I believe in the power of face-to-face human connection. As a result, I live a fairly "private" life with respect to the amount of information that exists on the internet. However, I am very open to discussing my family life with anyone who is interested!

I have a lovely mother, father, and sister. They are back in Canada, living their best lives. I am a cat person, and miss them dearly.

I have an amazing partner, Lisa, who keeps me honest, and balanced. We have travelled the world together, and are looking to what the future holds.