Madeyski15SQJ.NDC data

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Description

If you use this data set please cite: Lech Madeyski and Marian Jureczko, "Which Process Metrics Can Significantly Improve Defect Prediction Models? An Empirical Study," Software Quality Journal, vol. 23, no. 3, pp.393-422, 2015. DOI: 10.1007/s11219-014-9241-7

Usage

1

Format

A data frame with variables:

Project

In case of open source projects this field includes the name of the project as well as its version. In case of industrial projects this field includes the string "properietary" (we were not allowed to disclose the names of the analyzed industrial software projects developed by Capgemini Polska).

simple

The percentage of classes that must be tested in order to find 80% of defects in case of simple defect prediction models, i.e., using only software product metrics as predictors.

advanced

The percentage of classes that must be tested in order to find 80% of defects in case of advanced defect prediction models, using not only software product metrics but also the NDC (Number of distinct committers) process metric.

Details

"This paper presents an empirical evaluation in which several process metrics were investigated in order to identify the ones which significantly improve the defect prediction models based on product metrics. Data from a wide range of software projects (both, industrial and open source) were collected. The predictions of the models that use only product metrics (simple models) were compared with the predictions of the models which used product metrics, as well as one of the process metrics under scrutiny (advanced models). To decide whether the improvements were significant or not, statistical tests were performed and effect sizes were calculated. The advanced defect prediction models trained on a data set containing product metrics and additionally Number of Distinct Committers (NDC) were significantly better than the simple models without NDC, while the effect size was medium and the probability of superiority (PS) of the advanced models over simple ones was high (p=.016, r=-.29, PS=.76), which is a substantial finding useful in defect prediction. A similar result with slightly smaller PS was achieved by the advanced models trained on a data set containing product metrics and additionally all of the investigated process metrics (p=.038, r=-.29, PS=.68). The advanced models trained on a data set containing product metrics and additionally Number of Modified Lines (NML) were significantly better than the simple models without NML, but the effect size was small (p=.038, r=.06). Hence, it is reasonable to recommend the NDC process metric in building the defect prediction models." [http://dx.doi.org/10.1007/s11219-014-9241-7]

Source

http://madeyski.e-informatyka.pl/reproducible-research/

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