Authors : G. Saranya, H. Khanna Nehemiah, A. Kannan and S. Vimala
Abstract: Bad smells are the symptoms of code decay which leads to the severe maintenance problem. Shotgun surgery is a smell where a change in a class may cause many small changes to other different classes. There are several approaches that identify bad smells based upon the definition of rules and change history information. These rules are the combination of software metrics and threshold values which sometimes not able to detect the code decay such as shotgun surgery. Since, it is difficult to find the best threshold value for rule based detection and also finding the best combination of metrics from the historical information seems to be difficult. To detect the shotgun surgery, the co-change should be feasible. In that case, the need for having sufficient history of observable co-changes, without which the approach of change history is not possible. Therefore, these techniques cannot report the accurate instance of smells to detect the shotgun surgery bad smell. So as an alternate, in this study, a framework similarity measure distribution modelfor detecting shotgun surgery bad smell for object oriented program without the need for change history information is proposed. The framework is experimented on HSQLDB, TYRANT, XERCES-J and JFREE CHART open source software. To enable the detection of Shotgun surgery certain class files of the software under experimentation are modified. The results obtained through this framework are compared with the results obtained from the bad smell detection tools namely, inFusion and iPlasma in terms of precision and recall.From the results it is inferred that the shotgun surgery can be detected more accurately using this proposed approach.The proposed framework improves the maintainability by detecting the bad smell shotgun surgery.
G. Saranya, H. Khanna Nehemiah, A. Kannan and S. Vimala, 2016. Detecting Shotgun Surgery Bad Smell Using Similarity Measure Distribution Model. Asian Journal of Information Technology, 15: 3205-3216.