In software engineering, impact analysis consists in predicting the software elements (e.g. modules, classes, methods) potentially impacted by a change in the source code. Impact analysis is required to optimize the testing effort. In this paper, we propose a framework to predict error propagation. Based on 10 open-source Java projects and 5 classical mutation operators, we create 17000 mutants and study how the error they introduce propagates. This framework enables us to analyze impact prediction based on four types of call graph. Our results show that the sophistication indeed increases completeness of impact prediction. However, and surprisingly to us, the most basic call graph gives the highest trade-off between precision and recall for impact prediction.
Bohner and Arnold @cite_24 and Li al @cite_1 list the notable graph-based approaches. Different types or variants of software graphs have been used to perform change impact analysis, a common example is the program dependence graphs ( PDG) @cite_11 . In the present paper, we focus on the call graph.
@cite_24 @cite_1 @cite_11
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From the Publisher: As software systems become increasingly large and complex, the need increases to predict and control the effects of software changes. Software Change Impact Analysis captures the latest information on the science and art of determining what software parts affect each other. It provides a battery of ideas for doing impact analysis better, presents a framework for the field, and focuses attention on important results. You will gain a healthy respect for the strengths and limitations of impact analysis technology and a solid background that will prove valuable for years to come. The book identifies key impact analysis definitions and themes and illustrates the important themes to give you a solid understanding for tackling impact analysis problems. It includes reports on software source code dependency analysis and software traceability analysis and shows how results from both areas can more effectively support impact analysis in software engineering repositories. It also describes why impact representation and determination techniques are at the heart of both source dependency analysis and traceability analysis. SUMMARY Software change impact analysis (CIA) is a technique for identifying the effects of a change, or estimating what needs to be modified to accomplish a change. Since the 1980s, there have been many investigations on CIA, especially for code-based CIA techniques. However, there have been very few surveys on this topic. This article tries to fill this gap. And 30 papers that provide empirical evaluation on 23 code-based CIA techniques are identified. Then, data was synthesized against four research questions. The study presents a comparative framework including seven properties, which characterize the CIA techniques, and identifies key applications of CIA techniques in software maintenance. In addition, the need for further research is also presented in the following areas: evaluating existing CIA techniques and proposing new CIA techniques under the proposed framework, developing more mature tools to support CIA, comparing current CIA techniques empirically with unified metrics and common benchmarks, and applying the CIA more extensively and effectively in the software maintenance phase. Copyright © 2012 John Wiley & Sons, Ltd. Dependence analysis is useful for software maintenance because it indicates the possible effects of a software modification on the rest of a program. This helps the software maintainer evaluate the appropriateness of a software modification, drive regression testing, and determine the vulnerability of critical sections of code. A definition of interprocedural dependence analysis is given, and its implementation in a prototype tool that supports software maintenance is described. >