Portrait of Jin Guo

Jin Guo

Associate Academic Member
Assistant Professor, McGill University, School of Computer Science

Biography

Jin L.C. Guo is an assistant professor at the School of Computer Science, McGill University.

She is interested in using AI techniques to solve software engineering problems. Her recent research focuses on mining domain knowledge from software traceability data and using such knowledge to facilitate automated SE tasks, such as trace retrieval and project Q&A.

Guo completed her PhD at the University of Notre Dame. Prior to that, she worked on image processing and computer vision in Fuji Xerox’s research lab.

Current Students

Postdoctorate - McGill University
Co-supervisor :
Master's Research - McGill University
Co-supervisor :
PhD - McGill University
Principal supervisor :
Master's Research - McGill University
Co-supervisor :
Master's Research - McGill University
Master's Research - McGill University
Co-supervisor :
Master's Research - McGill University

Publications

Communicating Study Design Trade-offs in Software Engineering
Martin P. Robillard
Deeksha M. Arya
Neil A. Ernst
Maxime Lamothe
Mathieu Nassif
Nicole Novielli
Alexander Serebrenik
Igor Steinmacher
Klaas-Jan Stol
Properties and Styles of Software Technology Tutorials
Deeksha M. Arya
Martin P. Robillard
A large number of tutorials for popular software development technologies are available online, and those about the same technology vary wid… (see more)ely in their presentation. We studied the design of tutorials in the software documentation landscape for five popular programming languages: Java, C#, Python, Javascript, and Typescript. We investigated the extent to which tutorial pages, i.e. resources, differ and report statistics of variations in resource properties. We developed a framework for characterizing resources based on their distinguishing attributes, i.e. properties that vary widely for the resource, relative to other resources. Additionally, we propose that a resource can be represented by its resource style, i.e. the combination of its distinguishing attributes. We discuss three techniques for characterizing resources based on our framework, to capture notable and relevant content and presentation properties of tutorial pages. We apply these techniques on a data set of 2551 resources to validate that our framework identifies valid and interpretable styles. We contribute this framework for reasoning about the design of resources in the online software documentation landscape.
SUMMIT: Scaffolding Open Source Software Issue Discussion Through Summarization
Saskia Gilmer
Avinash Bhat
Shuvam Shah
Kevin Cherry
Jinghui Cheng
Aspirations and Practice of ML Model Documentation: Moving the Needle with Nudging and Traceability
Avinash Bhat
Austin Coursey
Grace Hu
Sixian Li
Nadia Nahar
Shurui Zhou
Christian Kästner
The documentation practice for machine-learned (ML) models often falls short of established practices for traditional software, which impede… (see more)s model accountability and inadvertently abets inappropriate or misuse of models. Recently, model cards, a proposal for model documentation, have attracted notable attention, but their impact on the actual practice is unclear. In this work, we systematically study the model documentation in the field and investigate how to encourage more responsible and accountable documentation practice. Our analysis of publicly available model cards reveals a substantial gap between the proposal and the practice. We then design a tool named DocML aiming to (1) nudge the data scientists to comply with the model cards proposal during the model development, especially the sections related to ethics, and (2) assess and manage the documentation quality. A lab study reveals the benefit of our tool towards long-term documentation quality and accountability.
Approach Intelligent Writing Assistants Usability with Seven Stages of Action
Avinash Bhat
Disha Shrivastava
GUILGET: GUI Layout GEneration with Transformer
Andrey Sobolevsky
Guillaume-Alexandre Bilodeau
Jinghui Cheng
SUMMIT: Scaffolding OSS Issue Discussion Through Summarization
Saskia Gilmer
Avinash Bhat
Shuvam Shah
Kevin Cherry
Jinghui Cheng
SUMMIT: Scaffolding OSS Issue Discussion Through Summarization
Saskia Gilmer
Avinash Bhat
Shuvam Shah
Kevin Cherry
Jinghui Cheng
How programmers find online learning resources
Deeksha M. Arya
M. Robillard
Machine learning-based incremental learning in interactive domain modelling
Rijul Saini
Gunter Mussbacher
Jörg Kienzle
Characterizing User Behaviors in Open-Source Software User Forums: An Empirical Study
Jazlyn Hellman
Jiahao Chen
Md. Sami Uddin
Jinghui Cheng
User forums of Open Source Software (OSS) enable end-users to collaboratively discuss problems concerning the OSS applications. Despite deca… (see more)des of research on OSS, we know very little about how end-users engage with OSS communities on these forums, in particular, the challenges that hinder their continuous and meaningful participation in the OSS community. Many previous works are developer-centric and overlook the importance of end-user forums. As a result, end-users' expectations are seldom reflected in OSS development. To better understand user behaviors in OSS user forums, we carried out an empirical study analyzing about 1.3 million posts from user forums of four popular OSS applications: Zotero, Audacity, VLC, and RStudio. Through analyzing the contribution patterns of three common user types (end-users, developers, and organizers), we observed that end-users not only initiated most of the threads (above 96% of threads in three projects, 86% in the other), but also acted as the significant contributors for responding to other users' posts, even though they tended to lack confidence in their activities as indicated by psycho-linguistic analyses. Moreover, we found end-users more open, reflecting a more positive emotion in communication than organizers and developers in the forums. Our work contributes new knowledge about end-users' activities and behaviors in OSS user forums that the vital OSS stakeholders can leverage to improve end-user engagement in the OSS development process.
GANSpiration: Balancing Targeted and Serendipitous Inspiration in User Interface Design with Style-Based Generative Adversarial Network
Mohammad Amin Mozaffari
Xinyuan Zhang
Jinghui Cheng
Inspiration from design examples plays a crucial role in the creative process of user interface design. However, current tools and technique… (see more)s that support inspiration usually only focus on example browsing with limited user control or similarity-based example retrieval, leading to undesirable design outcomes such as focus drift and design fixation. To address these issues, we propose the GANSpiration approach that suggests design examples for both targeted and serendipitous inspiration, leveraging a style-based Generative Adversarial Network. A quantitative evaluation revealed that the outputs of GANSpiration-based example suggestion approaches are relevant to the input design, and at the same time include diverse instances. A user study with professional UI/UX practitioners showed that the examples suggested by our approach serve as viable sources of inspiration for overall design concepts and specific design elements. Overall, our work paves the road of using advanced generative machine learning techniques in supporting the creative design practice.