In software development, the rapid expansion of code often leads to increased complexity and higher maintenance costs, particularly when unused or redundant code accumulates over time. This study introduces a novel approach to analyzing and visualizing code reachability using probabilistic model checking, enabling efficient identification of components that may no longer be necessary. By leveraging probabilistic reachability analysis, our method evaluates the usage trends of packages, classes and methods throughout the system’s lifecycle. We implemented a prototype tool and conducted case studies on real-world projects to validate its effectiveness. The results demonstrate the tool’s potential to support strategic code management by identifying and visualizing frequently used, rarely used and unused code components, thereby facilitating system optimization and reducing maintenance overhead.

This study addresses software bloat, a prevalent issue in modern software development, causing excessive size and complexity due to feature additions and unnecessary functions. Such bloat leads to decreased efficiency, performance degradation, and increased vulnerability. To combat this issue, the concept of software 3R (reduce, reuse, recycle) is proposed; however, accurately reproducing the internal state of black-box software for 3R requires both source code and execution log data, posing practical challenges. In this paper, we conduct a software execution status estimation using limited execution log. Graph Neural Networks (GNNs) are employed for analysis, offering effective processing of graph data. The task is framed as link prediction and node classification, comparing traditional deep learning methods with GNNs using Apache OFBiz ERP software logs. Preliminary results validate GNN applicability.

Software system developments generally involve writing codes.As code reduction is not considered, with accelerated software development, the number of code increases, which in turn increases the system management load.In this study, we pursue an analysis process to determine the necessity of each code present in the software.To handle a large amount of code, we utilize a probabilistic model checking technique.The analysis process identifies the trends in code usage by estimating probabilistic reachability.We implemented a prototype tool for the analysis.Results of two case studies in the real world demonstrate that the tool set has a possibility of extracting components that can be eliminated in the projects.