Software Space Analytics: Towards Visualization and Statistics of Internal Software Execution
This addresses a specific need for software architects and programmers in software engineering, but it appears incremental as it adapts existing spatial statistics methods to a new domain.
The paper tackles the problem of identifying modules needing modification or deletion in software maintenance by applying spatial statistics to internal software execution data, viewing the software structure as a space based on module call relationships.
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).