Advances in software inspections
IEEE Transactions on Software Engineering
Orthogonal Defect Classification-A Concept for In-Process Measurements
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
Estimating software fault content before coding
ICSE '92 Proceedings of the 14th international conference on Software engineering
Does every inspection need a meeting?
SIGSOFT '93 Proceedings of the 1st ACM SIGSOFT symposium on Foundations of software engineering
Assessing Software Designs Using Capture-Recapture Methods
IEEE Transactions on Software Engineering - Special issue on software reliability
An experiment to assess the cost-benefits of code inspections in large scale software development
SIGSOFT '95 Proceedings of the 3rd ACM SIGSOFT symposium on Foundations of software engineering
IEEE Transactions on Software Engineering
Defect content estimations from review data
Proceedings of the 20th international conference on Software engineering
A Discipline for Software Engineering
A Discipline for Software Engineering
Software Inspection
Does Every Inspection Really Need a Meeting?
Empirical Software Engineering
Quantitative Evaluation of Capture-Recapture Models to Control Software Inspections
ISSRE '97 Proceedings of the Eighth International Symposium on Software Reliability Engineering
A Comparison and Integration of Capture-Recapture Models and the Detection Profile Method
ISSRE '98 Proceedings of the The Ninth International Symposium on Software Reliability Engineering
Design and Analysis of Experiments
Design and Analysis of Experiments
Defect content estimations from review data
Proceedings of the 20th international conference on Software engineering
A Comprehensive Evaluation of Capture-Recapture Models for Estimating Software Defect Content
IEEE Transactions on Software Engineering
Evaluating Capture-Recapture Models with Two Inspectors
IEEE Transactions on Software Engineering
Empirical interval estimates for the defect content after an inspection
Proceedings of the 24th International Conference on Software Engineering
Using a Reliability Growth Model to Control Software Inspection
Empirical Software Engineering
An Empirical Method for Selecting Software Reliability Growth Models
Empirical Software Engineering
Applying Machine Learning to Solve an Estimation Problem in Software Inspections
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Evaluating defect estimation models with major defects
Journal of Systems and Software
An Empirical Study of Experience-Based Software Defect Content Estimation Methods
ISSRE '99 Proceedings of the 10th International Symposium on Software Reliability Engineering
Combining data from reading experiments in software inspections: a feasibility study
Lecture notes on empirical software engineering
Using Machine Learning for Estimating the Defect Content After an Inspection
IEEE Transactions on Software Engineering
Software Defect Association Mining and Defect Correction Effort Prediction
IEEE Transactions on Software Engineering
The effect of the number of inspectors on the defect estimates produced by capture-recapture models
Proceedings of the 30th international conference on Software engineering
Evaluation of capture-recapture models for estimating the abundance of naturally-occurring defects
Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement
Performing and analyzing non-formal inspections of entity relationship diagram (ERD)
Journal of Systems and Software
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In order to improvethe efficiency of inspections, quantitative data on defect contenthave to be the basis for decisions in the inspection process.An experience-based capture-recapture method is proposed, whichovercomes some problems with the basic pre-requisites of theoriginal method. A C-code inspection experiment is conductedto evaluate the enhanced method and its applicability to softwarecode inspections. It is concluded that the experience-based estimationprocedure gives significantly better estimates than the maximum-likelihoodmethod and the estimates are not very sensitive to changes inthe inspection data.