Software engineering metrics and models
Software engineering metrics and models
Scale Economies in New Software Development
IEEE Transactions on Software Engineering
Robust regression for developing software estimation models
Journal of Systems and Software
A Controlled Experiment to Assess the Benefits of Estimating with Analogy and Regression Models
IEEE Transactions on Software Engineering
Software Engineering Economics
Software Engineering Economics
A Simulation Study of the Model Evaluation Criterion MMRE
IEEE Transactions on Software Engineering
Reliability and Validity in Comparative Studies of Software Prediction Models
IEEE Transactions on Software Engineering
A Systematic Review of Software Development Cost Estimation Studies
IEEE Transactions on Software Engineering
Confidence in software cost estimation results based on MMRE and PRED
Proceedings of the 4th international workshop on Predictor models in software engineering
Why comparative effort prediction studies may be invalid
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Special issue on repeatable results in software engineering prediction
Empirical Software Engineering
A systematic review of systematic review process research in software engineering
Information and Software Technology
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We have in previous studies reported our findings and concern about the reliability and validity of the evaluation procedures used in comparative studies on competing effort prediction models. In particular, we have raised concerns about the use of accuracy statistics to rank and select models. Our concern is strengthened by the observed lack of consistent findings. This study offers more insights into the causes of conclusion instability by elaborating on the findings of our previous work concerning the reliability and validity of the evaluation procedures. We show that model selection based on the accuracy statistics MMRE, MMER, MBRE, and MIBRE contribute to conclusion instability as well as selection of inferior models. We argue and show that the evaluation procedure must include an evaluation of whether the functional form of the prediction model makes sense to better prevent selection of inferior models.