A program slicing-based Bayesian network model for change impact analysis
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Change impact analysis plays an important role in identifying potential affected areas that are caused by changes that are made in a software. Most of the existing change impact analysis techniques are based on architectural design and change history. However, source code-based change impact analysis studies are very few and they have shown higher precision in their results. In this study, a static method-granularity level change impact analysis, that uses program slicing and Bayesian Network technique has been proposed. The technique proposes a directed graph model that also represents the call dependencies between methods. In this study, an open source Java project with 8999 to 9445 lines of code and from 505 to 528 methods have been analyzed through 32 commits it went. Recall and f-measure metrics have been used for evaluation of the precision of the proposed method, where each software commit has been analyzed separately.