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Building software inspection effectiveness prediction models using data mining

A replicated case study
: Freimut, B.

Fulltext urn:nbn:de:0011-n-436608 (220 KByte PDF)
MD5 Fingerprint: 0cdd5f3f32eb16b21166bd672c48b30e
Created on: 13.06.2006

Kaiserslautern, 2006, VII, 25 pp. : Ill., Lit.
IESE-Report, 049.06/E
Reportnr.: 049.06/E
Report, Electronic Publication
Fraunhofer IESE ()
software inspection; prediction models; MARS

Inspections have been shown to be an effective means of detecting defects early on in the software development life cycle. However, they are not always successful or beneficial as they are affected by a number of technical and managerial factors. To make inspections successful, one important aspect is to understand what the factors are that affect inspection effectiveness in a given environment, based on project data. In this paper we collected data from over 1600 code inspections and replicated a multivariate statistical analysis in order to look at how management factors, such as the effort assigned and the inspection rate, affect inspection effectiveness. Because the functional form of effectiveness models is a priori unknown, we use a novel exploratory analysis technique: Multiple Adaptive Regression Splines (MARS). We compare the MARS model with more classical regression models and compare the results of this study with previous studies.