Code smells are defined as poor implementation and coding practices, and as a result decrease the overall quality of a source code. A number of code smell detection tools are available to automatically detect poor implementation choices, i.e., code smells. The detection of code smells is essential in order to improve the quality of the source code. This report aims to evaluate the accuracy and quality of seven different open-source code smell detection tools, with the purpose of establishing their level of trustworthiness.To assess the trustworthiness of a tool, we utilize a controlled experiment in which several versions of each tool are scrutinized using the most recent version of the same tool. In particular, we wanted to verify to what extent the code smell detection tools that reveal code smells in other systems, contain smells themselves. We further study the evolution of code smells in the tools in terms of number, types of code smells and code smell density.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:lnu-97558 |
| Date | January 2020 |
| Creators | Bampovits, Stefanos, Löwe, Amelie |
| Publisher | Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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