The Software Documentor Mindset
- URL: http://arxiv.org/abs/2412.09422v1
- Date: Thu, 12 Dec 2024 16:28:08 GMT
- Title: The Software Documentor Mindset
- Authors: Deeksha M. Arya, Jin L. C. Guo, Martin P. Robillard,
- Abstract summary: We interviewed 26 volunteer documentation contributors, i.e. documentors, to understand why and how they create such documentation.
We identified sixteen considerations that documentors have during the documentation contribution process, along three dimensions, namely motivations, topic selection techniques, and styling objectives.
We propose a structure of mindsets, and their associated considerations across the three dimensions, as a framework for reasoning about the documentation contribution process.
- Score: 10.55519622264761
- License:
- Abstract: Software technologies are used by programmers with diverse backgrounds. To fulfill programmers' need for information, enthusiasts contribute numerous learning resources that vary in style and content, which act as documentation for the corresponding technology. We interviewed 26 volunteer documentation contributors, i.e. documentors, to understand why and how they create such documentation. From a qualitative analysis of our interviews, we identified a total of sixteen considerations that documentors have during the documentation contribution process, along three dimensions, namely motivations, topic selection techniques, and styling objectives. We grouped related considerations based on common underlying themes, to elicit five software documentor mindsets that occur during documentation contribution activities. We propose a structure of mindsets, and their associated considerations across the three dimensions, as a framework for reasoning about the documentation contribution process. This framework can inform information seeking as well as documentation creation tools about the context in which documentation was contributed.
Related papers
- DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models [63.466265039007816]
We present DocGenome, a structured document benchmark constructed by annotating 500K scientific documents from 153 disciplines in the arXiv open-access community.
We conduct extensive experiments to demonstrate the advantages of DocGenome and objectively evaluate the performance of large models on our benchmark.
arXiv Detail & Related papers (2024-06-17T15:13:52Z) - Does Documentation Matter? An Empirical Study of Practitioners'
Perspective on Open-Source Software Adoption [4.400274233826898]
Open-source software (OSS) has become increasingly prevalent in developing software products.
We conducted semi-structured interviews and an online survey to provide insight into this area.
We developed a topic model to collect relevant information from OSS documentation automatically.
We propose a novel information augmentation approach, DocMentor, by combining OSS documentation corpus-IDF scores and ChatGPT.
arXiv Detail & Related papers (2024-03-06T16:06:08Z) - Understanding Documentation Use Through Log Analysis: An Exploratory
Case Study of Four Cloud Services [14.104545948572836]
We analyze documentation page-view logs from four cloud-based industrial services.
By analyzing page-view logs for over 100,000 users, we find diverse patterns of documentation page visits.
We propose documentation page-view log analysis as a feasible technique for design audits of documentation.
arXiv Detail & Related papers (2023-10-16T20:37:29Z) - Workshop on Document Intelligence Understanding [3.2929609168290543]
This workshop aims to bring together researchers and industry developers in the field of document intelligence.
We also released a data challenge on the recently introduced document-level VQA dataset, PDFVQA.
arXiv Detail & Related papers (2023-07-31T02:14:25Z) - A Study of Documentation for Software Architecture [7.011803832284996]
We asked 65 participants to answer software architecture understanding questions.
Answers to questions that require applying and creating activities were statistically significantly associated with the use of the system's source code.
We conclude that, in the limited experimental context studied, our results contradict the hypothesis that the format of architectural documentation matters.
arXiv Detail & Related papers (2023-05-26T22:14:53Z) - Layout-Aware Information Extraction for Document-Grounded Dialogue:
Dataset, Method and Demonstration [75.47708732473586]
We propose a layout-aware document-level Information Extraction dataset, LIE, to facilitate the study of extracting both structural and semantic knowledge from visually rich documents.
LIE contains 62k annotations of three extraction tasks from 4,061 pages in product and official documents.
Empirical results show that layout is critical for VRD-based extraction, and system demonstration also verifies that the extracted knowledge can help locate the answers that users care about.
arXiv Detail & Related papers (2022-07-14T07:59:45Z) - Documenting Data Production Processes: A Participatory Approach for Data
Work [4.811554861191618]
opacity of machine learning data is a significant threat to ethical data work and intelligible systems.
Previous research has proposed standardized checklists to document datasets.
This paper proposes a shift of perspective: from documenting datasets toward documenting data production.
arXiv Detail & Related papers (2022-07-11T15:39:02Z) - Focused Attention Improves Document-Grounded Generation [111.42360617630669]
Document grounded generation is the task of using the information provided in a document to improve text generation.
This work focuses on two different document grounded generation tasks: Wikipedia Update Generation task and Dialogue response generation.
arXiv Detail & Related papers (2021-04-26T16:56:29Z) - A Survey of Deep Learning Approaches for OCR and Document Understanding [68.65995739708525]
We review different techniques for document understanding for documents written in English.
We consolidate methodologies present in literature to act as a jumping-off point for researchers exploring this area.
arXiv Detail & Related papers (2020-11-27T03:05:59Z) - Explaining Relationships Between Scientific Documents [55.23390424044378]
We address the task of explaining relationships between two scientific documents using natural language text.
In this paper we establish a dataset of 622K examples from 154K documents.
arXiv Detail & Related papers (2020-02-02T03:54:47Z) - Conversations with Documents. An Exploration of Document-Centered
Assistance [55.60379539074692]
Document-centered assistance, for example, to help an individual quickly review a document, has seen less significant progress.
We present a survey to understand the space of document-centered assistance and the capabilities people expect in this scenario.
We present a set of initial machine learned models that show that (a) we can accurately detect document-centered questions, and (b) we can build reasonably accurate models for answering such questions.
arXiv Detail & Related papers (2020-01-27T17:10:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.