ArchJIT: Just-in-Time Warning of Architecture Smells via Incremental Dependency Graph Evolution and Learning-Based Prioritization

Software architecture tends to degrade during evolution, yet most studies analyze architectural decay offline at the version level. This limits early intervention when developers submit changes. We propose ArchJIT, a just-in-time warning framework for architecture smells that combines incremental dependency graph evolution with learning-based warning prioritization. ArchJIT updates a package-level dependency graph from changed source files, detects commit-induced smell events, generates warning candidates, and uses a lightweight binary retention model to select final warnings. We evaluate ArchJIT on multiple open-source Java repositories for detection effectiveness, incremental efficiency, structural discriminability, warning usefulness, and retention learnability. ArchJIT identifies smell-inducing commits at commit granularity, remains highly consistent with full reconstruction while reducing analysis cost, and emits concise warnings with interpretable evidence. Using calibrated retention labels, the retention model achieves 0.91 accuracy and a 0.89 F1-score on held-out candidates, and a small-scale expert study shows that final warnings are understandable and often comment-worthy. These results suggest that ArchJIT is a practical AI-for-software-engineering method for commit-time architecture warning.