Information and Software Technology

the results of prediction models trained on oversampled datasets

the dataset contains survey responses over cloud monitoring practices in industrial scenarios

The file lists papers found in the two updated SLRs analysed in the paper.

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.

Controlled experiment, i*, requirements engineering, Pareto efficiency

Data of the results obtained during the experimental evaluation

Dataset used in experiments and all extracted association rules.

A total of 12 software defect data sets from NASA were used in this study, where five data sets (part I) including CM1, JM1, KC1, KC2, and PC1 are obtained from PROMISE software engineering repository (http://promise.site.uottawa.ca/SERepository/), the other seven data sets (part II) are obtained from tera-PROMISE Repository (http://openscience.us/repo/defect/mccabehalsted/).

The video shows step-by-step how to use MEdit4CEP-Gam.

Access restricted due to collaboration with priave sector.