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      Assessing the Quality and Cleaning of a Software Project Dataset: An Experience Report

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      10th International Conference on Evaluation and Assessment in Software Engineering (EASE) (EASE)

      Evaluation and Assessment in Software Engineering (EASE)

      10 - 11 April 2006

      Software Engineering, Data Quality, Filtering, Polishing, Robust Algorithms

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            Abstract

            OBJECTIVE – The aim is to report upon an assessment of the impact noise has on the predictive accuracy by comparing noise handling techniques. METHOD – We describe the process of cleaning a large software management dataset comprising initially of more than 10,000 projects. The data quality is mainly assessed through feedback from the data provider and manual inspection of the data. Three methods of noise correction (polishing, noise elimination and robust algorithms) are compared with each other assessing their accuracy. The noise detection was undertaken by using a regression tree model. RESULTS – Three noise correction methods are compared and different results in their accuracy where noted. CONCLUSIONS – The results demonstrated that polishing improves classification accuracy compared to noise elimination and robust algorithms approaches.

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            Author and article information

            Contributors
            Conference
            April 2006
            April 2006
            : 1-7
            Affiliations
            [0001]Brunel University, UK
            Article
            10.14236/ewic/EASE2006.14
            cb43d8b3-795d-4694-ba82-6e47505ff686
            © Gernot Liebchen et al. Published by BCS Learning and Development Ltd. 10th International Conference on Evaluation and Assessment in Software Engineering (EASE), Keele University, UK

            This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

            10th International Conference on Evaluation and Assessment in Software Engineering (EASE)
            EASE
            10
            Keele University, UK
            10 - 11 April 2006
            Electronic Workshops in Computing (eWiC)
            Evaluation and Assessment in Software Engineering (EASE)
            Product
            Product Information: 1477-9358BCS Learning & Development
            Self URI (journal page): https://ewic.bcs.org/
            Categories
            Electronic Workshops in Computing

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