A Proposal of Data Dependence-Based Metrics for Evaluating Fault Injection Risk.

Abstract

This paper proposes metrics for evaluating the risk of fault injection through a code change. The proposed metrics focus on the data dependence via variables and quantify the extent to which the code change would influence. The empirical study using 7 open source projects shows that the proposed metrics are useful explanatory variables together with conventional metrics in random forest models to predict fault injection commits.