Communications of the ACM
Software Cost Estimation with Incomplete Data
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
A Critical Analysis of PSP Data Quality: Results from aCase Study
Empirical Software Engineering
A Comparison of Noise Handling Techniques
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
An analysis of data sets used to train and validate cost prediction systems
PROMISE '05 Proceedings of the 2005 workshop on Predictor models in software engineering
Discovering the software process by means of stochastic workflow analysis
Journal of Systems Architecture: the EUROMICRO Journal - Special issue: AGILE methodologies for software production
The pairwise attribute noise detection algorithm
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Filtering, Robust Filtering, Polishing: Techniques for Addressing Quality in Software Data
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
Data sets and data quality in software engineering
Proceedings of the 4th international workshop on Predictor models in software engineering
Systematic literature reviews in software engineering - A systematic literature review
Information and Software Technology
Proceedings of the joint international and annual ERCIM workshops on Principles of software evolution (IWPSE) and software evolution (Evol) workshops
A pattern-based outlier detection method identifying abnormal attributes in software project data
Information and Software Technology
Nothing else matters: what predictive model should I use?
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
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In this keynote I explore what exactly do we mean by data quality, techniques to assess data quality and the very significant challenges that poor data quality can pose. I believe we neglect data quality at our peril since - whether we like it or not - our research results are founded upon data and our assumptions that data quality issues do not confound our results. A systematic review of the literature suggests that it is a minority practice to even explicitly discuss data quality. I therefore suggest that this topic should become a higher priority amongst empirical software engineering researchers.