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| Main Author: | |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.07821 |
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| _version_ | 1866917356073123840 |
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| author | Saito, Shinobu |
| author_facet | Saito, Shinobu |
| contents | In software maintenance work, software architects and programmers need to identify modules that require modification or deletion. Whilst user requests and bug reports are utilised for this purpose, evaluating the execution status of modules within the software is also crucial. This paper, therefore, applies spatial statistics to assess internal software execution data. First, we define a software space dataset, viewing the software's internal structure as a space based on module call relationships. Then, using spatial statistics, we conduct the visualization of spatial clusters and the statistical testing using spatial measures. Finally, we consider the usefulness of spatial statistics in the software engineering domain and future challenges. (This paper has been published in the 14th International Conference on Model-Based Software and Systems Engineering (MODELSWARD 2016). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_07821 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Software Space Analytics: Towards Visualization and Statistics of Internal Software Execution Saito, Shinobu Software Engineering In software maintenance work, software architects and programmers need to identify modules that require modification or deletion. Whilst user requests and bug reports are utilised for this purpose, evaluating the execution status of modules within the software is also crucial. This paper, therefore, applies spatial statistics to assess internal software execution data. First, we define a software space dataset, viewing the software's internal structure as a space based on module call relationships. Then, using spatial statistics, we conduct the visualization of spatial clusters and the statistical testing using spatial measures. Finally, we consider the usefulness of spatial statistics in the software engineering domain and future challenges. (This paper has been published in the 14th International Conference on Model-Based Software and Systems Engineering (MODELSWARD 2016). |
| title | Software Space Analytics: Towards Visualization and Statistics of Internal Software Execution |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2602.07821 |