CGCI: Cross-granularity Causal Inference framework for engineering Change Propagation Analysis

In the dynamic landscape of large-scale and intricate product development, the constant generation and accumulation of configuration data, influenced by factors such as evolving demands and version alterations, exhibit inter-domain and inter-level characteristics. This complexity presents formidable challenges to the management of controlled changes. Central to effective change management is Change Propagation Analysis (CPA), particularly in accurately predicting the potential impacts on affected items. However, conventional CPA methods are insufficient for addressing the challenge of cross-domain, cross-level inference. Therefore, we propose a Cross-granularity Causal Inference Framework (CGCI) tailored for CPA. This framework leverages the diffusion and attenuation of influence, enabling efficient identification of potential configuration items. To assess the feasibility of CGCI, a dataset is constructed using raw industrial configuration data and conducted a comprehensive case study on aircraft configuration change control. The results of our comparative analysis show that CGCI is effective in addressing multi-granularity and multi-hop inference problems, with more comprehensive consideration and less inference overhead in the multi-granularity case.