Identifying Error-Prone Software An Empirical Study
IEEE Transactions on Software Engineering
Statistical techniques for modelling software quality in the ESPIRIT REQUEST project
Software Engineering Journal
An introduction to L1-norm based statistical data analysis
Computational Statistics & Data Analysis - Special issue on statistical data analysis based on the L:0I1:0E norm and relate
Metrics, outlier analysis and the software design process
Information and Software Technology
IEEE Transactions on Software Engineering
Regression modelling of software quality: empirical investigation
Journal of Electronic Materials
Elements of Software Science (Operating and programming systems series)
Elements of Software Science (Operating and programming systems series)
Software Reliability Model with Optimal Selection of Failure Data
IEEE Transactions on Software Engineering - Special issue on software reliability
A Comparative Study of Ordering and Classification of Fault-ProneSoftware Modules
Empirical Software Engineering
Rough Neural Network for Software Change Prediction
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Fault Prediction Modeling for Software Quality Estimation: Comparing Commonly Used Techniques
Empirical Software Engineering
The effects of development team skill on software product quality
ACM SIGSOFT Software Engineering Notes
Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods
IEEE Transactions on Software Engineering
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
A novel composite model approach to improve software quality prediction
Information and Software Technology
Assessing the maintainability of software product line feature models using structural metrics
Software Quality Control
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The objective in the construction of models of software quality is to use measures that may be obtained relatively early in the software development life cycle to provide reasonable initial estimates of the quality of an evolving software system. Measures of software quality and software complexity to be used in this modeling process exhibit systematic departures of the normality assumptions of regression modeling. Two new estimation procedures are introduced, and their performances in the modeling of software quality from software complexity in terms of the predictive quality and the quality of fit are compared with those of the more traditional least squares and least absolute value estimation techniques. The two new estimation techniques did produce regression models with better quality of fit and predictive quality when applied to data obtained from two software development projects.