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|Title:||Software cost modelling and estimation using artificial neural networks enhanced by input sensitivity analysis||Authors:||Papatheocharous, Efi
Andreou, Andreas S.
|Keywords:||Prediction systems;Regression-models;Validation;Accuracy;Software cost estimation;Artificial neural networks;Input sensitivity analysis||Category:||Electrical Engineering, Electronic Engineering, Information Engineering||Field:||Engineering and Technology||Issue Date:||2012||Publisher:||Verlag der Technischen Universität Graz||Source:||Journal of Universal Computer Science, 2012, Volume 18, Issue 14, Pages 2041-2070||Abstract:||This paper addresses the issue of Software Cost Estimation (SCE) providing an alternative approach to modelling and prediction using Artificial Neural Networks (ANN) and Input Sensitivity Analysis (ISA). The overall aim is to identify and investigate the effect of the leading factors in SCE, through ISA. The factors identified decisively influence software effort in the models examined and their ability to provide sufficiently accurate SCEs is examined. ANN of variable topologies are trained to predict effort devoted to software development based on past (finished) projects recorded in two publicly available historical datasets. The main difference with relevant studies is that the proposed approach extracts the most influential cost drivers that describe best the effort devoted to development activities using the weights of the network connections. The approach is validated on known software cost data and the results obtained are assessed and compared. The ANN constructed generalise efficiently the knowledge acquired during training providing accurate effort predictions. The validation process included predictions with only the most highly ranked attributes among the original cost attributes of the datasets and revealed that accuracy performance was maintained at same levels. The results showed that the combination of ANN and ISA is an effective method for evaluating the contribution of cost factors, whereas the subsets of factors selected did not compromise the accuracy of the prediction results.||URI:||http://ktisis.cut.ac.cy/handle/10488/3676||Other Identifiers:||0948-695X||DOI:||10.3217/jucs-018-14-2041||Rights:||© J.UCS||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
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