Towards Log-based Execution Status Estimation Using Graph Neural Networks

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.