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Research Article Open Access
Multi-organizational repositories, in particular those based on voluntary data contributions such as the repository of the International Software Benchmarking Standards Group (ISBSG), may be missing a large number of values for many of their data fields, as well as including some outliers. This paper suggests a number of data quality issues associated with the ISBSG repository which can compromise the outcomes for users exploiting it for benchmarking purposes or for building estimation models. We propose a number of criteria and techniques for preprocessing the data in order to improve the quality of the samples identified for detailed statistical analysis, and present a multiple imputation (MI) strategy for dealing with datasets with missing values.
Multi-imputation technique, ISBSG data preparation, Identification of outliers, Analysis effort estimation, Evaluation criteria, Android Technology, Cloud, Computer Hardware, Cryptography, Development Process, Information Systems, Information Technology, Internet Communication Technology, IT Management, Project development, Real Time, Wireless Technology, Sensor Technology, Software Component, Software Architecture, Software Quality, Web Service