Accuracy of software quality models over multiple releases
Annals of Software Engineering
Controlling Overfitting in Classification-Tree Models ofSoftware Quality
Empirical Software Engineering
Uncertain Classification of Fault-Prone Software Modules
Empirical Software Engineering
Balancing Misclassification Rates in Classification-TreeModels of Software Quality
Empirical Software Engineering
Data Mining of Software Development Databases
Software Quality Control
Fault Prediction Modeling for Software Quality Estimation: Comparing Commonly Used Techniques
Empirical Software Engineering
Classification Tree Models of Software Quality Over Multiple Releases
ISSRE '99 Proceedings of the 10th International Symposium on Software Reliability Engineering
Improving Tree-Based Models of Software Quality with Principal Components Analysis
ISSRE '00 Proceedings of the 11th International Symposium on Software Reliability Engineering
Empirical evaluation of defect projection models for widely-deployed production software systems
Proceedings of the 12th ACM SIGSOFT twelfth international symposium on Foundations of software engineering
Predictors of customer perceived software quality
Proceedings of the 27th international conference on Software engineering
Proceedings of the 28th international conference on Software engineering
Modeling software evolution defects: a time series approach
Journal of Software Maintenance and Evolution: Research and Practice
System regression test planning with a fuzzy expert system
Information Sciences: an International Journal
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Faults discovered by customers are an important aspect of software quality. The working hypothesis of this paper is that variables derived from an execution profile can be useful in software quality models. An execution profile of a software system consists of the probability of execution of each module during operations. Execution represents opportunities for customers to discover faults. However, an execution profile over an entire customer-base can be difficult to measure directly.Deployment records of past releases can be a valuable source of data for calculating an approximation to the probability of execution. In this paper, we analyze a metric derived from deployment records which is a practical surrogate for an execution profile in the context of a software quality model. We define "usage" as the proportion of systems in the field which have a module deployed.This paper presents a case study of a very large legacy telecommunications system. We developed models using a standard statistical technique to predict whether software modules will have any faults discovered by customers on systems in the field. Static software product metrics and usage were independent variables. The significance levels of variables in logistic regression models were analyzed, and models with and without usage as an independent variable were compared. The case study was empirical evidence that usage can be a significant contributor to a software quality model.