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
Sandrine Bédard
Maîtrise recherche - McGill
Co-superviseur⋅e :
AJung Moon
Considine Breandan
Postdoctorat - McGill
Co-superviseur⋅e :
Xujie Si
Site web
Github
Google Scholar
Lyu Fuyuan
Doctorat - McGill
Superviseur⋅e principal⋅e :
Xue (Steve) Liu
Site web
Github
Google Scholar
Justine Gehring
Maîtrise recherche - McGill
Co-superviseur⋅e :
Danny Tarlow
Site web
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

Toward Requirements Specification for Machine-Learned Components
Mona Rahimi
Jin Guo
Sahar Kokaly
Marsha Chechik
In current practice, the behavior of Machine-Learned Components (MLCs) is not sufficiently specified by the predefined requirements. Instead… (voir plus), they "learn" existing patterns from the available training data, and make predictions for unseen data when deployed. On the surface, their ability to extract patterns and to behave accordingly is specifically useful for hard-to-specify concepts in certain safety critical domains (e.g., the definition of a pedestrian in a pedestrian detection component in a vehicle). However, the lack of requirements specifications on their behaviors makes further software engineering tasks challenging for such components. This is especially concerning for tasks such as safety assessment and assurance. In this position paper, we call for more attention from the requirements engineering community on supporting the specification of requirements for MLCs in safety critical domains. Towards that end, we propose an approach to improve the process of requirements specification in which an MLC is developed and operates by explicitly specifying domain-related concepts. Our approach extracts a universally accepted benchmark for hard-to-specify concepts (e.g., "pedestrian") and can be used to identify gaps in the associated dataset and the constructed machine-learned model.
2019-01-01
2019 IEEE 27th International Requirements Engineering Conference Workshops (REW) (publié)
doi.org
Domain Knowledge Discovery Guided by Software Trace Links
Jin Guo
Natawut Monaikul
Jane Cleland-Huang
Software-intensive projects are specified and modeled using domain terminology. Knowledge of the domain terminology is necessary for perform… (voir plus)ing many Software Engineering tasks such as impact analysis, compliance verification, and safety certification. However, discovering domain terminology and reasoning about their interrelationships for highly technical software and system engineering domains is a complex task which requires significant domain expertise and human effort. In this paper, we present a novel approach for leveraging trace links in software intensive systems to guide the process of mining facts that contain domain knowledge. The trace links which drive our mining process, define relationships between artifacts such as regulations and requirements and enable a guided search through high-yield combinations of domain terms. Our proof-of-concept evaluation shows that our approach aids in the discovery of domain facts even in highly complex technical domains. These domain facts can provide support for a variety of Software Engineering activities. As a use case, we demonstrate how the mined facts can facilitate the task of project Q&A.
2018-08-21
2018 5th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE) (publié)
doi.org
arxiv.org
Traceability in the Wild: Automatically Augmenting Incomplete Trace Links
Michael Rath
Jacob Rendall
Jin Guo
Jane Cleland-Huang
Patrick Mäder
Software and systems traceability is widely accepted as an essential element for supporting many software development tasks. Today's version… (voir plus) control systems provide inbuilt features that allow developers to tag each commit with one or more issue ID, thereby providing the building blocks from which project-wide traceability can be established between feature requests, bug fixes, commits, source code, and specific developers. However, our analysis of six open source projects showed that on average only 60% of the commits were linked to specific issues. Without these fundamental links the entire set of project-wide links will be incomplete, and therefore not trustworthy. In this paper we address the fundamental problem of missing links between commits and issues. Our approach leverages a combination of process and text-related features characterizing issues and code changes to train a classifier to identify missing issue tags in commit messages, thereby generating the missing links. We conducted a series of experiments to evaluate our approach against six open source projects and showed that it was able to effectively recommend links for tagging issues at an average of 96% recall and 33% precision. In a related task for augmenting a set of existing trace links, the classifier returned precision at levels greater than 89% in all projects and recall of 50%.
2018-05-27
Proceedings of the 40th International Conference on Software Engineering (publié)
doi.org
arxiv.org
How Do the Open Source Communities Address Usability and UX Issues?: An Exploratory Study
Jinghui Cheng
Jin Guo
Usability and user experience (UX) issues are often not well emphasized and addressed in open source software (OSS) development. There is an… (voir plus) imperative need for supporting OSS communities to collaboratively identify, understand, and fix UX design issues in a distributed environment. In this paper, we provide an initial step towards this effort and report on an exploratory study that investigated how the OSS communities currently reported, discussed, negotiated, and eventually addressed usability and UX issues. We conducted in-depth qualitative analysis of selected issue tracking threads from three OSS projects hosted on GitHub. Our findings indicated that discussions about usability and UX issues in OSS communities were largely influenced by the personal opinions and experiences of the participants. Moreover, the characteristics of the community may have greatly affected the focus of such discussion.
2018-04-20
Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (publié)
doi.org
arxiv.org