Software Impact Radar: A Lightweight Change-Impact Analysis and Test Prioritization Framework for Java Applications

Modern software systems evolve through continuous and interdependent code changes, making it difficult to determine how a localized modification may propagate across complex software architectures.A seemingly small source-code change can affect dependent classes, services, APIs, database interactions, and regression tests, thereby increasing software-maintenance risk and testing cost.This paper presents Software Impact Radar, a lightweight change-impact analysis and test-prioritization framework designed for Java applications.The proposed framework integrates Git-based change detection, Java source-code parsing, dependency-graph construction, impact propagation, interpretable impact scoring, regression-test mapping, and visual analytics into a unified workflow.The framework identifies changed software entities, estimates their potential downstream impact, and generates an ordered regression-test sequence.Unlike approaches that focus exclusively on test reduction, the proposed framework emphasizes interpretable impact information and test prioritization so that high-risk tests can be executed earlier.The proposed evaluation considers precision, recall, F1-score, test-suite reduction, regression-fault detection, Average Percentage of Faults Detected (APFD), prioritization effectiveness, and analysis time.Experimental values are intentionally not fabricated and will be reported only after implementation and controlled empirical evaluation.