Data for: Finding key classes in object-oriented software systems by techniques based on static analysis

Published: 26 Sep 2019 | Version 1 | DOI: 10.17632/7h57xwtrcb.1
Contributor(s):

Description of this data

This dataset accompanies the article:
Ioana Sora, Ciprian-Bogdan Chirila, "Finding key classes in object-oriented software systems by techniques based on static analysis".

In the article, we define different class attributes and investigate which attributes can be used best as a measure of class importance.
We experiment on 15 open source software systems.
This dataset contains:

A list of the systems used as case studies is given as a text file. For every system, we specify the number of the used version and URLs to code and documentation.

For every software system, the dataset further contains:

  • the reference solution as a set of known key classes, extracted from design documentation;
  • for all classes of the system, the values of all class attributes computed by us with the methods described in the article;
  • the positions on which the known key classes are ranked by the 3 approaches proposed in the article.

Also supplementary data behind the plots is given in the form of 3 tables containing the raw data behind the boxplots in Figures 3, 4 and 5 of the article.

Experiment data files

This data is associated with the following publication:

Finding key classes in object-oriented software systems by techniques based on static analysis

Published in: Information and Software Technology

Latest version

  • Version 1

    2019-09-26

    Published: 2019-09-26

    DOI: 10.17632/7h57xwtrcb.1

    Cite this dataset

    Sora, Ioana; Chirila, Ciprian-Bogdan (2019), “Data for: Finding key classes in object-oriented software systems by techniques based on static analysis”, Mendeley Data, v1 http://dx.doi.org/10.17632/7h57xwtrcb.1

Statistics

Views: 227
Downloads: 9

Categories

Software Engineering, Program Analysis

Licence

CC BY 4.0 Learn more

The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.

What does this mean?
You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.

Report