Metrics for requirements engineering
Selected papers of the sixth annual Oregon workshop on Software metrics
Software process improvement with CMM
Software process improvement with CMM
Applying use cases: a practical guide
Applying use cases: a practical guide
An examination of the effects of requirements changes on software maintenance releases
Journal of Software Maintenance: Research and Practice
Exploring the relationship between design measures and software quality in object-oriented systems
Journal of Systems and Software
Software Engineering Economics
Software Engineering Economics
Experience With the Accuracy of Software Maintenance Task Effort Prediction Models
IEEE Transactions on Software Engineering
Process Metrics for Requirements Analysis
EWSPT '00 Proceedings of the 7th European Workshop on Software Process Technology
Metrics for Database Systems: An Empirical Study
METRICS '97 Proceedings of the 4th International Symposium on Software Metrics
Building UML Class Diagram Maintainability Prediction Models Based on Early Metrics
METRICS '03 Proceedings of the 9th International Symposium on Software Metrics
A study to investigate the impact of requirements instability on software defects
ACM SIGSOFT Software Engineering Notes
Analysis of Requirements Volatility during Software Development Life Cycle
ASWEC '04 Proceedings of the 2004 Australian Software Engineering Conference
An Industrial Case Study on Requirements Volatility Measures
APSEC '05 Proceedings of the 12th Asia-Pacific Software Engineering Conference
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Requirements volatility is an important risk factor for software projects. Software measures can help in quantifying and predicting this risk. In this paper, we present the results of a correlational study with the goal of predicting requirements volatility for a medium size software project. Based on the data collected from two industrial software projects for four measures of size of requirements (number of actors, use cases, words, and lines), we have evaluated prediction models for requirements volatility. These models can help project managers to estimate the volatility of requirements and minimize the risks caused by volatile requirements, like schedule and cost overruns. In cross systems validation our best model showed a mean magnitude of relative error (MMRE) of 0.25, which can be considered reliable. In an earlier study, we showed that decisions solely based on developers perception of requirements volatility are, instead unreliable. Predictions models, like the ones presented here, can therefore help taking more reliable decisions.