Dataset on Code Smells Surveys

DESCRIPTION This dataset contains the structure, collected data and descriptive statistics of responses to 3 surveys on code smells detection and visualization aimed at validating the conclusions of [1]. These surveys were administered stepwise: pre-test for identifying unclear questions and collecting suggestions for improvement; the 27 respondents were Portuguese researchers in the area of Software Engineering; improved survey (based on pre-test improvement suggestions) sent to the 193 authors of the primary papers on code smells detection covered in [1]; further improved/customized survey sent to authors of published work on software visualization in at least one of its two major conferences (VisSoft and/or SoftVis), taken from the SLR of Merino et al. [2], complemented by answers obtained through an announcement on this survey publicized in the SoftVis blog (https://softvis.wordpress.com/). AVAILABLE FILES PreTest_Questionnaire.pdf describes the structure of the pre-test; PreTest_Results.csv contains the responses collected during the pre-test (CVS format); PreTest_Responses.pdf contains descriptive statistics on the responses collected during the pre-test; DetectionExperts_Questionnaire.pdf describes the structure of the survey targeted to code smells detection experts; DetectionExperts_Results.csv contains the responses collected from the code smells detection experts (CVS format); DetectionExperts_Responses.pdf contains descriptive statistics on the responses produced by the code smells detection experts; VisualizationExperts_Questionnaire.pdf describes the structure of the survey targeted to software visualization experts; VisualizationExperts_Results.csv contains the responses collected from the software visualization experts (CVS format); VisualizationExperts_Responses.pdf contains descriptive statistics of the responses produced by the software visualization experts. REFERENCES [1] Pereira dos Reis J, Brito e Abreu F, Figueiredo Carneiro G, Anslow C (2020) Code smells detection and visualization: a systematic literature review. Archives of Computational Methods in Engineering, Springer (submitted) [2] Merino L, Ghafari M, Anslow C, Nierstrasz O (2018) A systematic literature review of software visualization evaluation. Journal of Systems and Software, 144:165 –180, Elsevier, DOI https://doi.org/10.1016/j.jss.2018.06.027