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The MLCQ data set with nearly 15000 code samples was created by software developers with professional experience who reviewed industry-relevant, contemporary Java open source projects. We expect that this data set should stay relevant for a longer time than data sets that base on code released years ago and, additionally, will enable researchers to investigate the relationship between developers' background and code smells' perception. If you use this data set please cite the following paper: Lech Madeyski and Tomasz Lewowski. MLCQ: Industry-relevant code smell data set. In Evaluation and Assessment in Software Engineering (EASE2020), April 15–17, 2020, Trondheim, Norway.ACM, New York, NY, USA, 6 pages, DOI: 3383219.3383264 URL: https://doi.org/10.1145/3383219.3383264 Note: Pre-print should be available soon from http://madeyski.e-informatyka.pl
This work has been supported by the National Centre for Research and Development (NCBR) project POIR.01.01.01-00-0792/16
software development, code smells, data set, software quality, bad code smells
software development, code smells, data set, software quality, bad code 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). | 0 | |
| 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. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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| downloads | 240 |

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