A Software Engineering View of Data Quality
Daniel Yankelevich
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Abstract
Thirty years ago, software was not considered a concrete value. Everyone agreed on its importance, but it was not considered as a good or possession. Nowadays, software is part of the balance of an organization. Data is slowly following the same process. The information owned by an organization is an important part of its assets. Information can be used as a competitive advantage. However, data has long been underestimated by the software community. Usually, methods and techniques apply to software (including data schemata), but the data itself has often been considered as an external problem. Validation and verification techniques usually assume that data is provided by an external agent and concentrate only on software.
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2009 Ninth International Conference on Quality Software, 2009
Inadequate levels of Data Quality (DQ) in Information Systems (IS) suppose a very important problem for organizations. In any case, they look for to assure data quality from earlier stages on information system developments. This paper proposes to incorporate mechanisms into software development methodologies, in order to integrate users DQ requirements aimed at assuring the data quality from the beginning of development. It brings a framework consisting of processes, activities and tasks, well defined, which would be incorporated in existent software development methodology, as METRICA V3; and therefore, to assure software product data quality created according to this methodology. The extension presented, is a guideline, and this can be extended and applied to other development methodologies like Unified Development Process.
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Data collection and analysis are key artifacts in any software engineering experiment. However, these data might contain errors. We propose a Data Quality model specific to data obtained from software engineering experiments, which provides a framework for analyzing and improving these data. We apply the model to two controlled experiments, which results in the discovery of data quality problems that need to be addressed. We conclude that data quality issues have to be considered before obtaining the experimental results.
Can We Trust Our Results? A Mapping Study on Data Quality
2013 20th Asia-Pacific Software Engineering Conference (APSEC), 2013
Background: The quality of data sets used in software engineering research is of the utmost importance. To ensure credibility of results obtained from use of data sets, the quality of the data must be examined. Objective: This study provides an overview of recent research (2008)(2009)(2010)(2011)(2012) involving data quality in software engineering data sets, with the goal of generally understanding what research there is that addresses data quality, and in particular to determine to what degree researchers have addressed any data quality issues in order to evaluate the trustworthiness of their results. Method: We performed a systematic mapping study to investigate treatment of data quality issues in software engineering research. A total of 64 papers published from 2008 to 2012 explicitly address issues with the quality of data and use software engineering data sets. These studies were classified according to the data quality topic, data set and data quality problem. Results: We found only 31 studies gave serious consideration for how the quality of the data affected their results. We observed that there is a lack of clear and consistent terminology regarding data quality, especially with respect to the kinds of quality problems a data set might have. As a first step to address this problem, we propose a model that describes the lifecycle that research data goes through when used in research. Conclusions: The results suggest that researchers should give more attention to the quality of data sets in order to produce trustworthy data for reliable empirical research, and that the research community needs to better understand and communicate issues with data quality.
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Data quality has significance to companies, but is an issue that can be challenging to approach and operationalise. This study focuses on data quality from the perspective of operationalisation by analysing the practices of a company that is a world leader in its business. A model is proposed for managing data quality to enable evaluation and operationalisation. The results indicate that data quality is best ensured when organisation specific aspects are taken into account. The model acknowledges the needs of different data domains, particularly those that have master data characteristics. The proposed model can provide a starting point for operationalising data quality assessment and improvement. The consequent appreciation of data quality improves data maintenance processes, IT solutions, data quality and relevant expertise, all of which form the basis for handling the origins of products.
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Riga , Latvia Executable Data Quality Models
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