Applying AspectJ and Test Cases to Expose the Impact of Exception Handling Bad Smells

• Main Authors: Yen-Lin Liu, 劉彥麟
• Other Authors: 謝金雲
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/9d8x46

碩士 === 國立臺北科技大學 === 資訊工程系 === 106 === Bad smells in exception handling code may decrease the robustness of a program. Robusta is a static analysis tool which has been shown to be very useful in detecting exception handling bad smells in Java programs. The impact of a bad smell needs to be exposed so that we can know if it is a real bug. In this thesis, we propose a method for exposing the impact of a bad smell by using AspectJ, that is, to dynamically inject a code to make an exception be thrown at a desired place so that the effect of the exception to the program can be revealed. In this study, an empirical study has also been conducted by applying the proposed method to two open source software - JFreeChart and Tomighty. The result shows the proposed method can successfully expose the impact of exception handling bad smells detected by Robusta.