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Research Paper Analysis Written By: Emilia Mendes and Nile Mosley IEEE Transactions on Software Engineering, vol. 34, issue 6, pp. 723-737, Nov.-Dec. 2008 Presented By: Matt Catron EEL6883 Spring 2009 University of Central Florida This paper focuses on estimating the effort involved in Web projects Web effort estimation is complex, and differs from traditional software development Much uncertainty in causal relationships in the management process Bayesian Networks are proposed models for this Causal models and probabilistic reasoning already used for software effort estimation Current models cannot be applied to the web development environment One recent Bayesian Network Model has produced superior results to other methods Compare several Bayesian Network Models for web project estimation Across multiple companies across the globe Total of 8 models are studied All models were trained with the same 2 data sets, each containing details on 130 web projects P(A|B)=P(B|A)*P(A)/P(B) Called the “posterior probability” Determines the likelihood of ‘A’ occurring given that ‘B’ has already occurred Bayesian Networks graphically depict this posterior probability over many events Node Probability Table (NPT) is produced for each variable CAUSE EFFECT Team size Involvement in a process improvement program Total number of web pages Use of a documented process Use of metrics throughout project Total amount effort required CAUSE EFFECT Level of team experience Level of team experience Team size Use of a documented process Implementation of process adaptations Number of languages used Not all variables account for, need more categories to model (ex: use std dev of team age to model generational gaps) Static, not random, set of data was used 6 percent of effort values provided were “guesstimates” Authors wish to repeat study with different data and larger set of categories Compare different automated BN modeling tools, as study indicate they can have varying results Study inconclusive trends more in depth GOOD Very thorough Some trends were identified Raised plenty of questions for future research BAD Studied too many models for one paper No clear winner (conclusion was very analytical) Difficult to model all variables in such detailed analysis Study a smaller set of models Offer more concise conclusions