One Documentation Does Not Fit All: Case Study of TensorFlow Documentation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910927397322752 |
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| author | Thirimanne, Sharuka Promodya Lemango, Elim Yoseph Antoniol, Giulio Nayebi, Maleknaz |
| author_facet | Thirimanne, Sharuka Promodya Lemango, Elim Yoseph Antoniol, Giulio Nayebi, Maleknaz |
| contents | Software documentation guides the proper use of tools or services. With the rapid growth of machine learning libraries, individuals from various fields are incorporating machine learning into their workflows through programming. However, many of these users lack software engineering experience, affecting the usability of the documentation. Traditionally, software developers have created documentation primarily for their peers, making it challenging for others to interpret and effectively use these resources. Moreover, no study has specifically focused on machine learning software documentation or analyzing the backgrounds of developers who rely on such documentation, highlighting a critical gap in understanding how to make these resources more accessible. This study examined customization trends in TensorFlow tutorials and compared these artifacts to analyze content and design differences. We also analyzed Stack Overflow questions related to TensorFlow documentation to understand the types of questions and the backgrounds of the developers asking them. Further, we developed two taxonomies based on the nature and triggers of the questions for machine learning software. Our findings showed no significant differences in the content or the nature of the questions across different tutorials. Our results show that 24.9% of the questions concern errors and exceptions, while 64.3% relate to inadequate and non-generalizable examples in the documentation. Despite efforts to create customized documentation, our analysis indicates that current TensorFlow documentation does not effectively support its target users. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01939 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | One Documentation Does Not Fit All: Case Study of TensorFlow Documentation Thirimanne, Sharuka Promodya Lemango, Elim Yoseph Antoniol, Giulio Nayebi, Maleknaz Software Engineering Software documentation guides the proper use of tools or services. With the rapid growth of machine learning libraries, individuals from various fields are incorporating machine learning into their workflows through programming. However, many of these users lack software engineering experience, affecting the usability of the documentation. Traditionally, software developers have created documentation primarily for their peers, making it challenging for others to interpret and effectively use these resources. Moreover, no study has specifically focused on machine learning software documentation or analyzing the backgrounds of developers who rely on such documentation, highlighting a critical gap in understanding how to make these resources more accessible. This study examined customization trends in TensorFlow tutorials and compared these artifacts to analyze content and design differences. We also analyzed Stack Overflow questions related to TensorFlow documentation to understand the types of questions and the backgrounds of the developers asking them. Further, we developed two taxonomies based on the nature and triggers of the questions for machine learning software. Our findings showed no significant differences in the content or the nature of the questions across different tutorials. Our results show that 24.9% of the questions concern errors and exceptions, while 64.3% relate to inadequate and non-generalizable examples in the documentation. Despite efforts to create customized documentation, our analysis indicates that current TensorFlow documentation does not effectively support its target users. |
| title | One Documentation Does Not Fit All: Case Study of TensorFlow Documentation |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2505.01939 |