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Defect Data Analysis Based on Extended Association Rule Mining

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dc.contributor.author Morisaki, Shuji en
dc.contributor.author Monden, Akito en
dc.contributor.author Matsumura, Tomoko en
dc.contributor.author Tamada, Haruaki en
dc.contributor.author Matsumoto, Ken-ichi en
dc.date.accessioned 2018-10-30T04:57:16Z en
dc.date.available 2018-10-30T04:57:16Z en
dc.date.issued 2007 en
dc.identifier.isbn 076952950X en
dc.identifier.issn 2160-1852 en
dc.identifier.uri http://hdl.handle.net/10061/12760 en
dc.description MSR'07:ICSE Workshops 2007 : Fourth International Workshop on Mining Software Repositories, 20-26 May 2007, Minneapolis, MN, USA en
dc.description.abstract This paper describes an empirical study to reveal rules associated with defect correction effort. We defined defect correction effort as a quantitative (ratio scale) variable, and extended conventional (nominal scale based) association rule mining to directly handle such quantitative variables. An extended rule describes the statistical characteristic of a ratio or interval scale variable in the consequent part of the rule by its mean value and standard deviation so that conditions producing distinctive statistics can be discovered As an analysis target, we collected various attributes of about 1,200 defects found in a typical medium-scale, multi-vendor (distance development) information system development project in Japan. Our findings based on extracted rules include: (l)Defects detected in coding/unit testing were easily corrected (less than 7% of mean effort) when they are related to data output or validation of input data. (2)Nevertheless, they sometimes required much more effort (lift of standard deviation was 5.845) in case of low reproducibility, (i)Defects introduced in coding/unit testing often required large correction effort (mean was 12.596 staff-hours and standard deviation was 25.716) when they were related to data handing. From these findings, we confirmed that we need to pay attention to types of defects having large mean effort as well as those having large standard deviation of effort since such defects sometimes cause excess effort. en
dc.language.iso en en
dc.publisher IEEE en
dc.rights c Copyright IEEE 2007 en
dc.subject data analysis en
dc.subject data mining en
dc.subject program debugging en
dc.subject program testing en
dc.subject statistical analysis en
dc.subject defect data analysis en
dc.subject association rule mining en
dc.subject statistical characteristics en
dc.subject standard deviation en
dc.subject information system development project en
dc.subject unit testing en
dc.subject data handing en
dc.subject Data analysis en
dc.subject Association rules en
dc.subject Data mining en
dc.subject Information systems en
dc.subject Statistics en
dc.subject Testing en
dc.subject Software engineering en
dc.subject Risk management en
dc.subject Information science en
dc.subject Information analysis en
dc.title Defect Data Analysis Based on Extended Association Rule Mining en
dc.type.nii Conference Paper en
dc.textversion author en
dc.identifier.spage 3 en
dc.identifier.epage 3 en
dc.relation.doi 10.1109/MSR.2007.5 en
dc.identifier.NAIST-ID 73292310 en

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