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dc.contributor.authorLiebchen, G-
dc.contributor.authorShepperd, M J-
dc.identifier.citationData Sets and Data Quality in Software Engineering. PROMISE 2008, Leipzig, ACM Press, May 2008en
dc.description.abstractOBJECTIVE - to assess the extent and types of techniques used to manage quality within software engineering data sets. We consider this a particularly interesting question in the context of initiatives to promote sharing and secondary analysis of data sets. METHOD - we perform a systematic review of available empirical software engineering studies. RESULTS - only 23 out of the many hundreds of studies assessed, explicitly considered data quality. CONCLUSIONS - first, the community needs to consider the quality and appropriateness of the data set being utilised; not all data sets are equal. Second, we need more research into means of identifying, and ideally repairing, noisy cases. Third, it should become routine to use sensitivity analysis to assess conclusion stability with respect to the assumptions that must be made concerning noise levels.en
dc.format.extent150051 bytes-
dc.publisherACM Pressen
dc.subjectData qualityen
dc.subjectEmpirical software engineeringen
dc.subjectSystematic reviewen
dc.titleData sets and data quality in software engineeringen
dc.typeConference Paperen
Appears in Collections:Computer Science
Dept of Computer Science Research Papers
Software Engineering (B-SERC)

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