ABSTRACT Websites are an important and efficient means of communication for companies wishing to interact with their clients. Therefore, research has focused on evaluating how websites should be structured to ensure their quality. The majority of this research has focused on evaluating the quality of individual pages or that of a site as a whole. In this article, we propose the use of two‐level models that combine evaluations at the page level with evaluations at the site level, and applied them to the problem of evaluating the navigability of websites. To test our models, we conducted a study with 21 subjects who had to complete navigation tasks on several websites, and compared their quality judgments to those produced by single and two‐level quality models. We found that two‐level models are better predictors of navigability. Finally, we show how two‐level models are able to suggest modifications to improve site navigability. Copyright © 2012 John Wiley & Sons, Ltd.

We propose an approach for the automatic detection of potential design defects in code. The detection is based on the notion that the more code deviates from good practices, the more likely it is bad. Taking inspiration from artificial immune systems, we generated a set of detectors that characterize different ways that a code can diverge from good practices. We then used these detectors to measure how far code in assessed systems deviates from normality.

Coupling metrics play an important role in empirical software engineering research as well as in industrial measurement programs. The existing coupling metrics have usually been defined in a way that they can be computed from a static analysis of the source code. However, modern programs extensively use dynamic language features such as polymorphism and dynamic class loading that are difficult to capture by static analysis. Consequently, the derived metric values might not accurately reflect the state of a program. In this paper, we express existing definitions of coupling metrics using call graphs. We then compare the results of four different call graph construction algorithms with standard tool implementations of these metrics in an empirical study. Our results show important variations in coupling between standard and call graph-based calculations due to the support of dynamic features.