期刊名称：International Journal of Soft Computing and Software Engineering
电子版ISSN：2251-7545
出版年度：2015
卷号：5
期号：4
页码：64-84
DOI：10.7321/jscse.v5.n4.1
出版社：Advance Academic Publisher
摘要：Regression testing is a part of the software testing activity, which is an important activity of the software development life cycle and the maintenance process. It is carried out to ensure that changes made in the fixes or any enhancement changes are not influencing the previously working functionality. Regression testing is mostly done by re-running existing test cases against the modified code to determine whether the changes affect anything. This requires a lot of cost and time, which increases as the size and the complexity of the software increases. Instead of re-running all the test cases, a number of different approaches were studied to solve regression-testing problems. There has been an explosion in the use of data mining techniques in the exploration and analysis of large quantities of data in order to discover meaningful patterns and rules. Data mining models were introduced for software testing to design a minimal set of regression tests. This helps solving regression testing problems with large-scale systems that are usually accompanied by thousands set of test cases, where it is considered impossible to re-run all of them each time a system update is applied. Therefore, data mining is investigated to handle such cases. In this paper, we investigate the different techniques proposed to solve the regression testing problems, where a comprehensive study is conducted for analysis and evaluation. We also discuss the tools presented in market for the regression testing. Finally, we present our proposed approach for regression testing using data mining techniques. The main advantage of this new approach is that it can be applied on large-scale systems having thousands of test cases. The proposed regression-testing algorithm considers time and cost constraints with no human intervention.
关键词：Software Testing ; Regression Testing ; Large-scale Systems ; Test Cases Prioritization & Selection ; Data Mining
