Generating GitHub Repository Descriptions: A Comparison of Manual and Automated Approaches

Jazlyn, Hellman,; Jang, Eunbee; Huang, Chenzhun; Guo, Jin

doi:10.48550/arxiv.2110.13283

Cited by 2 publications

(2 citation statements)

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“…The study of software documentation concentrates mostly on the aspects of documentation property and quality [2,5,32], documentation search and discovery [40,41], content augmentation [36,43], and documentation creation support [20,21,29]. Among them, our work is most relevant to the previous inquiry on documentation quality and interactive documentation creation support.…”

Section: Software Documentationmentioning

confidence: 99%

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Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability

Bhat¹,

Coursey²,

Hu³

et al. 2022

Preprint

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Machine learning models have been widely developed, released, and adopted in numerous applications. Meanwhile, the documentation practice for machine learning models often falls short of established practices for traditional software components, which impedes model accountability, inadvertently abets inappropriate or misuse of models, and may trigger negative social impact. Recently, model cards, a template for documenting machine learning models, have attracted notable attention, but their impact on the practice of model documentation is unclear. In this work, we examine publicly available model cards and other similar documentation. Our analysis reveals a substantial gap between the suggestions made in the original model card work and the content in actual documentation.Motivated by this observation and literature on fields such as software documentation, interaction design, and traceability, we further propose a set of design guidelines that aim to support the documentation practice for machine learning models including (1) the collocation of documentation environment with the coding environment, (2) nudging the consideration of model card sections during model development, and (3) documentation derived from and traced to the source. We designed a prototype tool named DocML following those guidelines to support model development in computational notebooks. A lab study reveals the benefit of our tool to shift the behavior of data scientists towards documentation quality and accountability.

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Section: Software Documentationmentioning

confidence: 99%

“…or even the whole project [21]. This work normally relies on heuristic or machine learning methods to extract or synthesize content from the input artifacts and inevitably introduce both errors and biases.…”

Section: Software Documentationmentioning

confidence: 99%