A Software Maintainability Dataset

Abstract

This dataset contains 304 manual evaluations of class-level software maintainability. It can be used to develop and evaluate automated quality prediction tools.<br>This archive was created along the work described in detail in<em>M. Schnappinger, A. Fietzke, and A. Pretschner, "</em><i>Defining a Software Maintainability Dataset: Collecting, Aggregating and Analysing Expert Evaluations of Software Maintainability</i><em>", International Conference on Software Maintenance and Evolution (ICSME), 2020</em><em><br></em>If you use this dataset in your research, please cite both this dataset and the corresponding paper. This archive containsA readme with all relevant information aboutStudy objectsLabel definitionThreats to validityHints for using the datasetList of metrics used to prioritize the samplesThe code of the study objectsA .csv file containing the readability, understandability, complexity, adequate size, and overall maintainability labelsFigshare uses a strict character limit for this description. Please refer to the `Readme.md` for further information.<br>

Cite this paper

@misc{schnappinger2020software,
  title = {{A Software Maintainability Dataset}},
  author = {Schnappinger, Markus and Fietzke, Arnaud and Pretschner, Alexander},
  journal = {Figshare},
  year = {2020},
  doi = {10.6084/m9.figshare.12801215.v2}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.

Related papers