Generalizing evolutionary coupling with stochastic dependencies

Researchers have leveraged evolutionary coupling derived from revision history to conduct various software analyses, such as software change impact analysis (IA). The problem is that the validity of historical data depends on the recency of changes and varies with different evolution paths-thus, influencing the accuracy of analysis results. In this paper, we formalize evolutionary coupling as a stochastic process using a Markov chain model. By varying the parameters of this model, we define a family of stochastic dependencies that accounts for different types of evolution paths. Each member of this family weighs historical data differently according to their recency and frequency. To assess the utility of this model, we conduct IA on 78 releases of five open source systems, using 16 stochastic dependency types, and compare with the results of several existing approaches. The results show that our stochastic-based IA technique can provide more accurate results than these existing techniques.