Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Constructing an accurate effort prediction model is a challenge in Software Engineering.  presents three Bayesian statistical software effort prediction models for database-oriented software systems, which are developed using a specific 4GL toolsuite. The models consist of  specification-based  software size metrics and development team's productivity metric. The models are constructed based on the subjective knowledge of human expert and calibrated using empirical data collected from 17 software systems developed in the target environment. The models' predictive accuracy is evaluated using subsets of the same data, which re not used for the models' calibration. The results show that the models have achieved very good predictive accuracy in terms of MMRE and pred measures. Hence, it is confirmed that the Bayesian statistical models can predict effort successfully in the target environment. In comparison with commonly used multiple linear regression models, the Bayesian statistical models'predictive accuracy is equivalent in general. Hover, when the number of software systems used for the models' calibration becomes smaller than five, the predictive accuracy of the best Bayesian statistical models are significantly better than the multiple linear regression model. This result suggests that the Bayesian statistical models would be a better choice when software organizations/practitioners do not posses sufficient empirical data for the models' calibration. The authors expect these findings to encourage more researchers to investigate the use of Bayesian statistical models for predicting software effort.
['C. van Koten', 'A. Gray']
I am writing a research paper on a idea Bayesian statistical effort prediction models for data-centred 4GL software development
[{"from": "human", "value": "I am writing a research paper on a idea Bayesian statistical effort prediction models for data-centred 4GL software development"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Constructing an accurate effort prediction model is a challenge in Software Engineering.  presents three Bayesian statistical software effort prediction models for database-oriented software systems, which are developed using a specific 4GL toolsuite. The models consist of  specification-based  software size metrics and development team's productivity metric. The models are constructed based on the subjective knowledge of human expert and calibrated using empirical data collected from 17 software systems developed in the target environment. The models' predictive accuracy is evaluated using subsets of the same data, which re not used for the models' calibration. The results show that the models have achieved very good predictive accuracy in terms of MMRE and pred measures. Hence, it is confirmed that the Bayesian statistical models can predict effort successfully in the target environment. In comparison with commonly used multiple linear regression models, the Bayesian statistical models'predictive accuracy is equivalent in general. Hover, when the number of software systems used for the models' calibration becomes smaller than five, the predictive accuracy of the best Bayesian statistical models are significantly better than the multiple linear regression model. This result suggests that the Bayesian statistical models would be a better choice when software organizations/practitioners do not posses sufficient empirical data for the models' calibration. The authors expect these findings to encourage more researchers to investigate the use of Bayesian statistical models for predicting software effort."}]