Software Space Analytics: Towards Visualization and Statistics of Internal Software Execution

2026-02-08

AI Summary

This study addresses the challenge in software maintenance of effectively quantifying the execution status of internal modules to identify redundant or critical components requiring modification or removal. To this end, it introduces spatial statistics theory into software engineering for the first time, proposing the concept of “software space.” By modeling execution data through a module call graph, the approach enables structured analysis of module-level execution behavior via spatial clustering visualization and statistical hypothesis testing. Experimental results demonstrate that the method successfully identifies both critical and redundant modules, thereby offering data-driven support for informed maintenance decisions.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• software maintenance
• module execution
• software space
• spatial statistics
• software analytics

Innovation

• software space
• spatial statistics
• module execution
• visualization
• software maintenance