Fit Refactoring-Improving the Quality of Fit Acceptance Test

by Liu, Xu

Abstract (Summary)
Acceptance tests are formal testing conducted to determine whether a system satisfies its acceptance criteria or not and whether the acquirer should accept the system or not. A suite of acceptance tests for large projects might include a large number of test cases; therefore, automation of acceptance test is in great demand. Framework for Integrated Tests (FIT) is a popular tool employed in Agile Software Development to automate acceptance tests. Its most attractive feature is that it uses customer readable tables as test cases so that customers can write test cases. Refactoring is the process of restructuring or rewriting code without changing its interface and functionality. Refactoring make the code easier to read, understand and maintain, and sometime helps to improve the performance of the system. In a typical project that uses FIT as an acceptance test tool, the size of FIT acceptance tests grows as the size of system code grows, and the acceptance design may go far away from the original design (this may happen in any project, not restricted in a project using FIT). At this stage, it would be difficult to read and maintain the FIT acceptance test, and it is time to improve the quality of the acceptance test. In this research, we introduce the concept and reveal the importance of FIT Refactoring. Several FIT Refactoring methods are introduced as examples to show the needs of FIT Refactoring and the methods how it can be accomplished. Of course, the methods given here are some obvious refactoring methods, and new methods can be discovered in further research. We also discuss the proper time to do FIT refactoring and proper efforts that should be devoted in it. The similarities and differences between system code refactoring and FIT acceptance test refactoring are also one part of the research. During the research, there are some unexpected findings. One of them is that sometimes, the bad code in FIT acceptance test indicates bad code in the system code.
Bibliographical Information:


School:Bowling Green State University

School Location:USA - Ohio

Source Type:Master's Thesis



Date of Publication:01/01/2007

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