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
Empirical 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
Using Inspection Data for Defect Estimation
IEEE Software
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
Using Machine Learning for Estimating the Defect Content After an Inspection
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
Web software traffic characteristics and failure prediction model selection
Journal of Computational Methods in Sciences and Engineering
Simulation of experiments for data collection: a replicated study
EASE'06 Proceedings of the 10th international conference on Evaluation and Assessment in Software Engineering
Application of kusumoto cost-metric to evaluate the cost effectiveness of software inspections
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
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In order to control inspections, the number of remaining defects in software artifacts after their inspection should be estimated. This would allow, for example, deciding whether a reinspection of supposedly faulty artifacts is necessary. Several studies in software engineering have considered capture-recapture models for performing such estimations. These models were initially developed for estimating animal abundance in wildlife research. In addition to these models, researchers in software engineering have recently proposed an alternative approach, namely the Detection Profile Method (DPM), that makes less restrictive assumptions than some capture-recapture models and that show promise in terms of estimation accuracy. In this study, we investigate how to select between these two approaches for defect content estimation. As a result of this investigation we present a selection procedure taking into account the strength and weaknesses of the two methods. A weakness known for capture-recapture models is that they tend to provide extreme under/over estimation. The existence of such extreme outliers can discourage their use because their consequences in terms of wasted effort or defect slippage can be substantial, and therefore it is not clear whether a particular estimate can be trusted. The evaluation of our selection procedure with actual inspection data indicates that this selection procedure provides the same accuracy as capture-recapture models alone and DPM alone, and most importantly does not exhibit extreme over/under estimation. Thus, this selection procedure can be used in practice with a high degree of confidence since its estimates are not likely to exhibit extreme estimation error.