Software reliability: measurement, prediction, application (professional ed.)
Software reliability: measurement, prediction, application (professional ed.)
Reliability Analysis of Large Software Systems: Defect Data Modeling
IEEE Transactions on Software Engineering
Software modeling and measurement: the Goal/Question/Metric paradigm
Software modeling and measurement: the Goal/Question/Metric paradigm
Handbook of software reliability engineering
A Comparative Study of Predictive Models for Program Changes During System Testing and Maintenance
ICSM '93 Proceedings of the Conference on Software Maintenance
Modeling software maintenance requests: a case study
ICSM '97 Proceedings of the International Conference on Software Maintenance
Experience from Replicating Empirical Studies on Prediction Models
METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
Implications of Evolution Metrics on Software Maintenance
ICSM '98 Proceedings of the International Conference on Software Maintenance
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
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
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
Performance and reliability prediction for evolving service-oriented software systems
Empirical Software Engineering
| Hi-index | 0.00 |
Corrective maintenance activities are a common cause of schedule delays in software development projects. Organizations frequently fail to properly plan the effort required to fix field defects. This study aims to provide relevant guidance to software development organizations on planning for these corrective maintenance activities by correlating metrics that are available prior to release with parameters of the selected software reliability model that has historically best fit the product's field defect data. Many organizations do not have adequate historical data, especially historical deployment and field usage information. The study identifies a set of metrics calculable from available data to approximate these missing predictor categories. Two key metrics estimable prior to release surfaced with potentially useful correlations, (1) the number of periods until the next release and (2) the peak deployment percentage. Finally, these metrics were used in a case study to plan corrective maintenance efforts on current development releases.