Empirical Evaluation of Mimic Software Project Data Sets for Software Effort Estimation

Maohua GAN  Zeynep YÜCEL  Akito MONDEN  Kentaro SASAKI  

IEICE TRANSACTIONS on Information and Systems   Vol.E103-D   No.10   pp.2094-2103
Publication Date: 2020/10/01
Publicized: 2020/07/03
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019EDP7150
Type of Manuscript: PAPER
Category: Software Engineering
empirical software engineering,  data confidentiality,  data mining,  

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To conduct empirical research on industry software development, it is necessary to obtain data of real software projects from industry. However, only few such industry data sets are publicly available; and unfortunately, most of them are very old. In addition, most of today's software companies cannot make their data open, because software development involves many stakeholders, and thus, its data confidentiality must be strongly preserved. To that end, this study proposes a method for artificially generating a “mimic” software project data set, whose characteristics (such as average, standard deviation and correlation coefficients) are very similar to a given confidential data set. Instead of using the original (confidential) data set, researchers are expected to use the mimic data set to produce similar results as the original data set. The proposed method uses the Box-Muller transform for generating normally distributed random numbers; and exponential transformation and number reordering for data mimicry. To evaluate the efficacy of the proposed method, effort estimation is considered as potential application domain for employing mimic data. Estimation models are built from 8 reference data sets and their concerning mimic data. Our experiments confirmed that models built from mimic data sets show similar effort estimation performance as the models built from original data sets, which indicate the capability of the proposed method in generating representative samples.