Jin Guo

Maxime Lamothe

Mathieu Nassif

Nicole Novielli

Alexander Serebrenik

Igor Steinmacher

Klaas-Jan Stol

2024-03-02

ACM Transactions on Software Engineering and Methodology (published)

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.

2024-02-01

IEEE Transactions on Software Engineering (published)

SUMMIT: Scaffolding Open Source Software Issue Discussion Through Summarization

Saskia Gilmer

Avinash Bhat

Shuvam Shah

Kevin Cherry

Jinghui Cheng

2023-10-04

Proceedings of the ACM on Human-Computer Interaction (published)

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.

2023-04-19

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (published)

Approach Intelligent Writing Assistants Usability with Seven Stages of Action

Avinash Bhat

Disha Shrivastava

2023-04-06

ArXiv (preprint)

GUILGET: GUI Layout GEneration with Transformer

Andrey Sobolevsky

Guillaume-Alexandre Bilodeau

Jinghui Cheng

2023-01-01

Canadian AI (published)

SUMMIT: Scaffolding OSS Issue Discussion Through Summarization

Saskia Gilmer

Avinash Bhat

Shuvam Shah

Kevin Cherry

Jinghui Cheng

2023-01-01

arXiv.org (preprint)

SUMMIT: Scaffolding OSS Issue Discussion Through Summarization

Saskia Gilmer

Avinash Bhat

Shuvam Shah

Kevin Cherry

Jinghui Cheng

2023-01-01

arXiv.org (preprint)

Machine learning-based incremental learning in interactive domain modelling

Rijul Saini

Gunter Mussbacher

Jörg Kienzle

2022-10-24

Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems (published)

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.

2022-04-13

ArXiv (preprint)

The Secret to Better AI and Better Software (Is Requirements Engineering)

Nelly Bencomo

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… (see more)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)

The Secret to Better AI and Better Software (Is Requirements Engineering)

Nelly Bencomo

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… (see more)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)