Paper Info
Title
Software Development Effort And Quality Prediction Using Bayesian Nets And Small Local Qualitative Data
Abstract
Large and homogeneous datasets are typically required to predict software development effort and quality accurately. Also, many statistical methods can only be applied when meeting various constraints. This study focuses on developing Bayesian nets (BNs) automatically from a small local dataset. Predictive accuracy of generated BNs keeps the level of other published results but the procedure of building the models is simpler The accuracy can be improved by incorporating domain expert knowledge.
Year
Venue
Keywords
2010
22ND INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING & KNOWLEDGE ENGINEERING (SEKE 2010)
effort prediction, quality prediction, Bayesian nets, local data, qualitative factors
Field
DocType
Citations 
Data mining,Systems engineering,Qualitative property,Computer science,Software development,Bayesian probability
Conference
1
PageRank 
References 
Authors
0.35
0
1
Name
Order
Citations
PageRank
Łukasz Radliński1895.34