Replicated Case Studies for Investigating Quality Factorsin Object-Oriented Designs
Empirical Software Engineering
Coupling and cohesion metrics for knowledge-based systems using frames and rules
ACM Transactions on Software Engineering and Methodology (TOSEM)
IEEE Software
Using industry based data sets in software engineering research
Proceedings of the 2006 international workshop on Summit on software engineering education
Experimental evaluation of an object-oriented function point measurement procedure
Information and Software Technology
Metrics to study symptoms of bad software designs
ACM SIGSOFT Software Engineering Notes
Quality Factors and Coding Standards -- a Comparison Between Open Source Forges
Electronic Notes in Theoretical Computer Science (ENTCS)
Measurement Analysis and Fault Proneness Indication in Product Line Applications (PLA)
Proceedings of the 2007 conference on New Trends in Software Methodologies, Tools and Techniques: Proceedings of the sixth SoMeT_07
An application of Bayesian network for predicting object-oriented software maintainability
Information and Software Technology
Assessment of usability metrics for object-oriented software system
ACM SIGSOFT Software Engineering Notes
Usability Estimation of Software System by using Object-Oriented Metrics
ACM SIGSOFT Software Engineering Notes
Assessment of maintainability metrics for object-oriented software system
ACM SIGSOFT Software Engineering Notes
Assessing software product maintainability based on class-level structural measures
PROFES'06 Proceedings of the 7th international conference on Product-Focused Software Process Improvement
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In the context of software cost estimation, system size is widely taken as a main driver of system development effort. But other structural design properties, such as coupling, cohesion, complexity have been suggested as additional cost factors. In this paper, we empirically investigate the relationship between class size and the development effort for a class, and what additional impact structural properties such as class coupling have on effort. We use Poisson regression and regression trees to build cost prediction models from size and design measures, and use these models to predict system development effort. We also investigate a recently suggested technique to combine regression trees with regression analysis, which aims at building more accurate models. Results indicate that fairly accurate predictions of class effort can be made based on simple measures of the class interface size alone (mean MREs below 30%). Effort predictions at the system level are even more accurate as, using Bootstrapping, the estimate 95% confidence interval for MREs is 3%-23%. But more sophisticated coupling and cohesion measures do not help to improve these predictions to a degree that would be practically significant. However, the use of hybrid models, combining Poisson regression and CART regression trees clearly improves the accuracy of the models, as compared to using Poisson regression alone.