Mining Variable-Method Correlation for Change Impact Analysis

作     者：Hu, Chunling Li, Bixin Sun, Xiaobing 

作者机构：Hefei Univ Dept Comp Sci & Technol Hefei 230026 Anhui Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Jiangsu Peoples R China Yangzhou Univ Sch Informat Engn Yangzhou 225009 Jiangsu Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2018年第6卷

页      面：77581-77595页

核心收录：

基　　金：National Natural Science Foundation of China [61806068, 61672204] Project of Universities of Anhui Province [gxfxZD2016209] Key Technologies R&D Program of Anhui Province [1804a09020058] Major Program for Scientific and Technological of Anhui Province Talent Research Foundation Project of Hefei University [16-17RC23] 

主　　题：Change impact analysis variable-method correlation variable and method triple impact propagation call graph impact set abstract syntax tree 

摘      要：Software change impact analysis (CIA) is a key technique to identify the potential ripple effects of the changes to software. Coarse-grained CIA techniques such as file, class and method level techniques often gain less precise change impacts, which are difficult for practical use. Fine-grained CIA techniques, such as slicing, can be used to gain more precise change impacts, but need more time and space cost. In this paper, by combining the features of the coarse-grained technique and the fine-grained technique, a variable-method (VM) correlation-based CIA technique called VM-CIA is proposed to improve the precision of static CIA. First, the VM-CIA technique uses the abstract syntax tree (AST) of program to construct a novel intermediate representation called variable and method triple (VMT), which is used to analyze the correlation between the variables and methods. Second, the VM-CIA technique proposes the single-change impact analysis algorithm and multi-change impact analysis algorithm to compute the impact set based on the VMT representation. In addition, the VM-CIA technique can get a sorted impact set which is more accurate than the existing CIA techniques. The empirical results show that the VM-CIA technique can greatly improve the precision (19%) over traditional the CIA techniques, while at the cost of a little recall (5%). Moreover, the empirical studies also show that the VM-CIA technique predicts a ranked list of potential impact results according to the distance measure, which can greatly facilitate the practical use.