• Journal of KIISE:Software and Applications
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• v.35 no.10
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• pp.589-599
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• 2008

Accurate software effort estimation has always been a challenge for the software industrial and academic software engineering communities. Many studies have focused on effort estimation methods to improve the estimation accuracy of software effort. Although data quality is one of important factors for accurate effort estimation, most of the work has not considered it. In this paper, we investigate the influence of outlier elimination on the accuracy of software effort estimation through empirical studies applying two outlier elimination methods(Least trimmed square regression and K-means clustering) and three effort estimation methods(Least squares regression, Neural network and Bayesian network) associatively. The empirical studies are performed using two industry data sets(the ISBSG Release 9 and the Bank data set which consists of the project data collected from a bank in Korea) with or without outlier elimination.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.59-73
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• 2016

The demand for subsurface transport is increasing. The users and the operators of road tunnels are exposed to risks with different causes. One main cause, however, is the traffic situation in the event of accidents. The importance of a Quantified Risk Assessment is increasing to quantify the safety of road tunnels and to balance the requirements (capacity, reliability, availability, maintainability and safety) of various stakeholders. Although there are classical methods for risk assessments, such as ETA and FTA. These methods are used for relatively simple cases because it could not relevantly reflect the diversity and relationship of the parameters. Therefore, a quantitative risk assessment based on Bayesian Probabilistic Networks considering interdependence between the parameters of a complex underground system as a double deck tunnel is provided.