Problem Statement

In this section, we present how users navigate web sites and describe our adaptation of our composition model to this problem.

1_{The content of this chapter is the subject of a publication at the 12th IEEE International Symposium}

7.1.1 Finding Information on a Web Site

A user typically has two options to find information on a web site. He can either explore the site, going from page to page by following links or, if available, he can use other features like a search engine to access pages directly. Consequently, the general navigability of a web site needs to take into consideration both ways to navigate the site. First, it needs to evaluate the impact of visiting every page on the navigability of the web site. This means that the model should evaluate how easy it is to find the appropriate link to follow on every page and combine this to the probability that a user will be on that page. Thus two models are involved: a page quality model and a navigation model. In addition, our method must take into account the alternate navigation mechanisms that are provided by the web site.

From an exploration perspective, a web site can be viewed as a directed graph; for- mally, G ⇒ hV, Ei where V and E are respectively the set of vertices representing the pages and the set of directed edges representing links between pages. An edge (u, v) represents a link in the page u to the page v. Vertex u, is called the head of the link and v, the tail. For vertex u, the out-links is the set of links with u as the head, representing the links to the other pages, and in-links is the set with u as the tail, representing the links to

ufrom the other pages.

A user requiring information located at page pdestneeds to find a path (p1, p2, ..., pdest)

in G that takes him from his origin p1to his destination pdest. In terms of the graph, this

is a greedy path-finding problem where at any given page a user needs to figure out which out-link leads him closer to his destination. A site with good navigability should ensure that few steps are required to reach any destination. Some potential navigation difficulties arise due to pages with inadequate link identification (e.g., no titles and bad anchor text) or to pages that overwhelm him with too much information (e.g., the user needs to scroll down to find the correct link). There are consequently two sources of information that influence this view of navigability: the quality of individual pages, and the presence of these pages on an exploration path.

119 engine, the user jumps directly to another page. Exploration is pertinent even if the site has been indexed by a search engine because, lacking the correct keywords, a user may not find the page he needs from the index. The two methods of navigation are thus complementary.

7.1.2 Assessing Site Navigability

Figure 7.1 shows our composition model adapted for the evaluation of web site nav- igability. The three sub-models handle respectively three kinds of decisions: the naviga- bility at individual pages (white boxes), the importance of each page in the site to weight the contribution of its navigability (light-gray box), and the navigability at the site level (dark-gray box).

Figure 7.1: Navigability Evaluation Process

The different models describe the following aspects of navigability:

• The Page-level model describes the ability of a user to find relevant navigation information on a given web page. This information can either be to find the correct link to follow, or whether or not the page follows standard navigation practices that allow users to go to the site’s home page or simply go back.

• The Composition model describes how likely a user will land up on a given web page, and need to interact with the page.

• The Site-level model uses both the result of the composition model and a set of site-level metrics. These site-level metrics describe the navigation mechanisms available site-wide. For example, the presence of a search engine is a site-wide mechanism.

7.2 Related Work

There is an abundance of information describing how to build usable Web sites (e.g., http://usability.gov). This information is typically provided by practitioners. However, unlike usability, the problem of building navigable sites is mostly the subject of research articles. Zhang et al. [ZZG04] proposed complexity metrics to evaluate nav- igability. Newman and Landay considered it as one of three aspects affecting the quality of the interface design of Web applications [NL00]. Olsina et al. [OLR01] decompose quality hierarchically and navigability is a factor affecting the suitability quality sub- characteristics. Zhou et al. [ZLW07] proposed a navigation model that abstracts the user Web surfing behaviour as a Markov model. This model is used to quantify the naviga- bility. Cachero et al.

[CCMG+_{07] used a model-driven approach to define a model for the measurement of}

navigability and a process for evolving this model. Finally, Ricca and Tonella [RT01] propose using the UML to represent Web pages. Using this representation, they present TestWeb, a tool to generate test cases.

Other methodologies consider additional characteristics to assess quality of web ap- plications. For instance, Olsina et al. [OLR01] define WebQEM (Web Quality Eval- uation Methodology). Albuquerque et al. [AB02] suggest FMSQE (Fuzzy Model for Software Quality Evaluation) model. The model uses fuzzy logic and presents a quality tree for e-commerce applications. It takes into account problems related to uncertainty during quality evaluation. Shubert et al. [Sch03a] develop EWAM (Extended Web As- sessment Method). The method is based on Fishbein’s behavioural model and Davis’ technology acceptance model. It is applied to e-commerce web sites and is supported by a tool. Recently, Mavromoustakos et al. [MA07] use importance-based criteria for eval-

121 uating requirements in their quality model WAQE (Web Application Quality Evaluation model). Regarding the use of probabilistic approaches for quality assessment, Malak

et al. [MSBB06] propose a method for building web application quality models using

Bayesian networks. The approach of Malak et al. was used by Caro et al. [CCdSP07] for the particular case of web portal data quality.

Finally, there is work done to model the behaviour of a user navigating a web site to find specific information [CRS+_{03]. This work is very similar to the use of call graphs}

(described in Section 6.3) that are used to identify what methods can be invoked for a given execution. In our quality models, we are not interested in understanding the behaviour of users conducting specific activities (e.g., finding information X located on page Y ), but rather we want an estimate of the general acceptability of a site.