In-process inspections of workproducts at AT&T
AT&T Technical Journal
An Experiment to Assess the Cost-Benefits of Code Inspections in Large Scale Software Development
IEEE Transactions on Software Engineering
An encompassing life cycle centric survey of software inspection
Journal of Systems and Software
A Discipline for Software Engineering
A Discipline for Software Engineering
Software Inspection
Quantitative Modeling of Software Reviews in an Industrial Setting
METRICS '99 Proceedings of the 6th International Symposium on Software Metrics
METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
Peer Reviews in Real Life - Motivators and Demotivators
QSIC '05 Proceedings of the Fifth International Conference on Quality Software
Predicting object-oriented software maintainability using multivariate adaptive regression splines
Journal of Systems and Software
Managing software quality through a hybrid defect content and effectiveness model
Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement
Empirical Software Engineering
Location-based team recommendation in computer gaming scenarios
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data
Information Resources Management Journal
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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 are the factors that affect inspection effectiveness (the rate of detected defects) in a given environment, based on project data. In this paper we collected data from over 230 code inspections and performed 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 show how it can help understand the complex trends and interactions in the data, without requiring the analyst to rely on strong assumptions. Results are reported and discussed in light of existing studies.