Portrait de Jin Guo

Jin Guo

Membre académique associé
Professeur adjoint, McGill University, École d'informatique
Sujets de recherche
IA centrée sur l'humain
IA responsable
Interaction humain-IA
Interaction humain-machine (IHM)
Vie privée
Site web

Biographie

Jin L.C. Guo a obtenu son doctorat à l'Université de Notre Dame. Elle s'intéresse à l'utilisation des techniques d'intelligence artificielle pour résoudre des problèmes de génie logiciel. Ses recherches récentes portent sur la connaissance du domaine minier à partir des données de traçabilité logicielle et sur l'utilisation de ces connaissances pour faciliter les tâches automatisées de génie logiciel telles que la recherche de traces et les questions et réponses sur les projets. Avant son doctorat, elle a travaillé au laboratoire de recherche de Fuji Xerox dans les domaines du traitement de l'image et de la vision par ordinateur.

Étudiants actuels

Avinash Avinash
Doctorat - McGill
Site web
Github
Google Scholar
Considine Breandan
Postdoctorat - McGill
Co-superviseur⋅e :
Xujie Si
Site web
Github
Google Scholar
Billy Exarhakos
Maîtrise recherche - McGill
Github
Lyu Fuyuan
Doctorat - McGill
Superviseur⋅e principal⋅e :
Xue (Steve) Liu
Site web
Github
Google Scholar
Jazlyn Jazlyn
Doctorat - McGill
jazlynhellman@gmail.com
Github
Tamara Paris Paris
Maîtrise recherche - McGill
Co-superviseur⋅e :
AJung Moon
tamaraprs21@gmail.com
Sara Tavakoli
Maîtrise recherche - McGill
Site web
Github
Veronica Xia
Maîtrise recherche - McGill

Publications

Automated, interactive, and traceable domain modelling empowered by artificial intelligence
Rijul Saini
Gunter Mussbacher
Jin Guo
Jörg Kienzle
2022-01-08
Software and Systems Modeling (publié)
doi.org
Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability
Avinash Bhat
Austin Coursey
Grace Hu
Sixian Li
Nadia Nahar
Shurui Zhou
Christian Kästner
Jin Guo
2022-01-01
(publié)
doi.org
Deposited in DRO : 17 January 2022 Version of attached le : Accepted Version Peer-review status of attached
Nelly Bencomo
Jin Guo
Rachel Harrison
Hans-Martin Heyn
Tim Menzies
Much has been written about the algorithmic role that AI plays for automation in SE. But what about the role of AI, augmented by human knowl… (voir plus)edge? Can we make a profound advance by combining human and artificial intelligence? Researchers in requirements engineering think so, arguing that requirement engineering is the secret weapon for better AI and better software. Much has been written about the algorithmic role that AI plays for automation in SE. But what about the role of AI, augmented by human knowledge? Can we make a profound advance by combining human and artificial intelligence? Researchers in requirements engineering think so, arguing that requirement engineering is the secret weapon for better AI and better software1. To begin, we first need a definition. What is requirements engineering or RE? RE used to be viewed as an early lifecycle activity that proceeded analysis, design, coding and testing. For safety critical applications there is certainly a pressing need to create those requirements before the coding starts (we will return to this point, later in the paper). However, in this age of DevOps and Autonomous and Self-adaptive systems, requirements can happen at many other times in a software project[15], [14]. We say that: Requirements engineering is any discussion about what to build and how to trade-off competing cost/benefits. It can happen before, during, or after runtime. 1This paper is based on the Panel “Artificial Intelligence and Requirement Engineering: Challenges and Opportunities”, which took place at the Eighth International Workshop on Artificial Intelligence and Requirements Engineering (AIRE). As shown in Table 1 and Table 2, there are many ways AI can help RE, across a broad range of SE activities. But, what about the other way around? If we add more requirements into AI, and use RE methods to get truly desired requirements, can we make better software by combining human and artificial intelligence? In our view, when integrating AI into software engineering is a co-design problem between humans, the AI model, the data required to train and validate the desired behaviour, and the hardware running the AI model, in addition to the classical software components. This means that when integrating AI, you need to know and understand the context of the system in which you want to apply your AI model to derive the necessary model requirements [17]. For example, in the arena of safety critical systems, model construction must be guided by safety requirements. one challenge for AI in RE are safety standards that base on the EN-IEC 61508 standard2. These safety standards assume that for software only systematic faults exists. Therefore, they emphasise correct processes and the creation of lifecycle artifacts to minimise systematic mistakes during both the 2Functional Safety of Electrical/Electronic/Programmable Electronic Safety-related Systems; for example ISO 26262 for the automotive sector or IEC 61511 for the process industry. IEEE Software (submitted) Published by the IEEE Computer Society © 2021 IEEE 1
2022-01-01
(publié)
www.semanticscholar.org
GANSpiration: Balancing Targeted and Serendipitous Inspiration in User Interface Design with Style-Based Generative Adversarial Network
Mohammad Amin Mozaffari
Xinyuan Zhang
Jinghui Cheng
Jin Guo
Inspiration from design examples plays a crucial role in the creative process of user interface design. However, current tools and technique… (voir plus)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.
2022-01-01
CHI (publié)
doi.org
arxiv.org
GitHub repositories with links to academic papers: Public access, traceability, and evolution
Supatsara Wattanakriengkrai
Bodin Chinthanet
Hideaki Hata
Raula Gaikovina Kula
Christoph Treude
Jin Guo
Kenichi Matsumoto
2022-01-01
Journal of Systems and Software (publié)
doi.org
arxiv.org
The Secret to Better AI and Better Software (Is Requirements Engineering)
Nelly Bencomo
Jin Guo
Rachel. Harrison
Hans-Martin Heyn
Tim J Menzies
Recently, practitioners and researchers met to discuss the role of requirements, and AI and SE. We offer here notes on that fascinating disc… (voir plus)ussion. Also, have you considered writing for this column? This “SE for AI” column publishes commentaries on the growing field of SE for AI. Submissions are welcomed and encouraged (1,000–2,400 words, each figure and table counts as 250 words, try to use fewer than 12 references, and keep the discussion practitioner focused). Please submit your ideas to me at timm@ieee.org.—Tim Menzies
2022-01-01
IEEE Software (published)
doi.org
The Secret to Better AI and Better Software (Is Requirements Engineering)
Nelly Bencomo
Jin Guo
Rachel Harrison
Hans-Martin Heyn
Tim Menzies
Recently, practitioners and researchers met to discuss the role of requirements, and AI and SE. We offer here notes on that fascinating disc… (voir plus)ussion. Also, have you considered writing for this column? This “SE for AI” column publishes commentaries on the growing field of SE for AI. Submissions are welcomed and encouraged (1,000–2,400 words, each figure and table counts as 250 words, try to use fewer than 12 references, and keep the discussion practitioner focused). Please submit your ideas to me at timm@ieee.org.—Tim Menzies
2022-01-01
IEEE Software (publié)
doi.org
Splitting, Renaming, Removing: A Study of Common Cleaning Activities in Jupyter Notebooks
Helen Dong
Shurui Zhou
Jin Guo
Christian Kästner
Data scientists commonly use computational notebooks because they provide a good environment for testing multiple models. However, once the … (voir plus)scientist completes the code and finds the ideal model, he or she will have to dedicate time to clean up the code in order for others to easily understand it. In this paper, we perform a qualitative study on how scientists clean their code in hopes of being able to suggest a tool to automate this process. Our end goal is for tool builders to address possible gaps and provide additional aid to data scientists, who then can focus more on their actual work rather than the routine and tedious cleaning work. By sampling notebooks from GitHub and analyzing changes between subsequent commits, we identified common cleaning activities, such as changes to markdown (e.g., adding headers sections or descriptions) or comments (both deleting dead code and adding descriptions) as well as reordering cells. We also find that common cleaning activities differ depending on the intended purpose of the notebook. Our results provide a valuable foundation for tool builders and notebook users, as many identified cleaning activities could benefit from codification of best practices and dedicated tool support, possibly tailored depending on intended use.
2021-11-15
2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) (publié)
doi.org
Subtle Bugs Everywhere: Generating Documentation for Data Wrangling Code
Chenyang Yang
Shurui Zhou
Jin Guo
Christian Kästner
Data scientists reportedly spend a significant amount of their time in their daily routines on data wrangling, i.e. cleaning data and extrac… (voir plus)ting features. However, data wrangling code is often repetitive and error-prone to write. Moreover, it is easy to introduce subtle bugs when reusing and adopting existing code, which results in reduced model quality. To support data scientists with data wrangling, we present a technique to generate documentation for data wrangling code. We use (1) program synthesis techniques to automatically summarize data transformations and (2) test case selection techniques to purposefully select representative examples from the data based on execution information collected with tailored dynamic program analysis. We demonstrate that a JupyterLab extension with our technique can provide on-demand documentation for many cells in popular notebooks and find in a user study that users with our plugin are faster and more effective at finding realistic bugs in data wrangling code.
2021-11-15
2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) (publié)
doi.org
Generating GitHub Repository Descriptions: A Comparison of Manual and Automated Approaches
Jazlyn Hellman
Eunbee Jang
Christoph Treude
Chenzhun Huang
Jin Guo
Given the vast number of repositories hosted on GitHub, project discovery and retrieval have become increasingly important for GitHub users.… (voir plus) Repository descriptions serve as one of the first points of contact for users who are accessing a repository. However, repository owners often fail to provide a high-quality description; instead, they use vague terms, the purpose of the repository is poorly explained, or the description is omitted entirely. In this work, we examine the current practice of writing GitHub repository descriptions. Our investigation leads to the proposal of the LSP (Language, Software technology, and Purpose) template to formulate good descriptions for GitHub repositories that are clear, concise, and informative. To understand the extent to which current automated techniques can support generating repository descriptions, we compare the performance of state-of-the-art text summarization methods on this task. Finally, our user study with GitHub users reveals that automated summarization can adequately be used for default description generation for GitHub repositories, while the descriptions which follow the LSP template offer the most effective instrument for communicating with GitHub users.
2021-10-25
ArXiv (prépublication)
arxiv.org
arxiv.org
DoMoBOT: An AI-Empowered Bot for Automated and Interactive Domain Modelling
Rijul Saini
Gunter Mussbacher
Jin Guo
Jörg Kienzle
Domain modelling transforms informal requirements written in natural language in the form of problem descriptions into concise and analyzabl… (voir plus)e domain models. As the manual construction of these domain models is often time-consuming, error-prone, and labor-intensive, several approaches already exist to automate domain modelling. However, the current approaches suffer from lower accuracy of extracted domain models and the lack of support for system-modeller interactions. To better assist modellers, we introduce DoMoBOT, a web-based Domain Modelling BOT. Our proposed bot combines artificial intelligence techniques such as natural language processing and machine learning to extract domain models with higher accuracy. More importantly, our bot incorporates a set of features to bring synergy between automated model extraction and bot-modeller interactions. During these interactions, the bot presents multiple possible solutions to a modeller for modelling scenarios present in a given problem description. The bot further enables modellers to switch to a particular solution and updates the other parts of the domain model proactively. In this tool demo paper, we demonstrate how the implementation and architecture of DoMoBOT support the paradigm of automated and interactive domain modelling for assisting modellers.
2021-10-10
2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) (publié)
doi.org
Automated Traceability for Domain Modelling Decisions Empowered by Artificial Intelligence
Rijul Saini
Gunter Mussbacher
Jin Guo
Jörg Kienzle
Domain modelling abstracts real-world entities and their relationships in the form of class diagrams for a given domain problem space. Model… (voir plus)lers often perform domain modelling to reduce the gap between understanding the problem description which expresses requirements in natural language and the concise interpretation of these requirements. However, the manual practice of domain modelling is both time-consuming and error-prone. These issues are further aggravated when problem descriptions are long, which makes it hard to trace modelling decisions from domain models to problem descriptions or vice-versa leading to completeness and conciseness issues. Automated support for tracing domain modelling decisions in both directions is thus advantageous. In this paper, we propose an automated approach that uses artificial intelligence techniques to extract domain models along with their trace links. We present a traceability information model to enable traceability of modelling decisions in both directions and provide its proof-of-concept in the form of a tool. The evaluation on a set of unseen problem descriptions shows that our approach is promising with an overall median F2 score of 82.04%. We conduct an exploratory user study to assess the benefits and limitations of our approach and present the lessons learned from this study.
2021-09-01
IEEE International Requirements Engineering Conference (publié)
doi.org