ImpactScale: Quantifying change impact to predict faults in large software systems

摘要

In software maintenance, both product metrics and process metrics are required to predict faults effectively. However, process metrics cannot be always collected in practical situations. To enable accurate fault prediction without process metrics, we define a new metric, ImpactScale. ImpactScale is the quantified value of change impact, and the change propagation model for ImpactScale is characterized by probabilistic propagation and relation-sensitive propagation. To evaluate ImpactScale, we predicted faults in two large enterprise systems using the effort-aware models and Poisson regression. The results showed that adding ImpactScale to existing product metrics increased the number of detected faults at 10% effort (LOC) by over 50%. ImpactScale also improved the predicting model using existing product metrics and dependency network measures.