
doi: 10.1007/11774129_8
The variety of code smells deserves a numerous set of detectors capable of sensing them. There exist several sources of data that may be examined: code metrics, existence of particular elements in an abstract syntax tree, specific code behavior or subsequent changes in the code. Another factor that can be used for this purpose is the knowledge of other, already detected or rejected smells. In the paper we define and analyze different relations that exist among smells and provide tips how they could be exploited to alleviate detection of other smells.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 29 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
