WEB METRICS SELECTION THROUGH A PRACTITIONERS’

SURVEY

Julian Ruiz, Coral Calero, Mario Piattini

Alarcos Research Group, Information Systems and Technologies Department, UCLM-Soluziona Research and

Development Institute. University of Castilla-La Mancha, Paseo de la Universidad, 4, 13071 - Ciudad Real, Spain

Keywords: Web Metrics, Quality.

Abstract: There are a lot of web metrics proposals. However, most previous work does

not include their practical

application. The risk of doing so, is to limit all the effort made just to an academic exercise. In order to

eliminate this gap as well as to be able to apply the work developed, it is necessary to involve the different

stakeholders related to web technologies as an essential part of web metrics definition. So, it is crucial to

know the perception they have about web metrics, especially those related to the development and

maintenance of web sites and applications. In this paper, we present the work we have done to find out

which web metrics are considered useful by web developers and maintainers. This study has been

performed on the basis of the 385 web metrics classified in WQM, a Web Quality Model defined in a

previous work, using as validation tool, a survey made by professionals of web technologies. As a result, we

have found out that the most weighted metrics were related to usability. That means that web professionals

give more importance to the user of metrics than to their own effort.

1 INTRODUCTION

The spectacular development of the web has led to

an increasing importance of related technologies in

the functioning of organizations as well as in

people’s lives. It is compulsory that developed

products, both complex applications or simple web

sites, satisfy a minimum quality standard (Cutter

Consortium, 2000).

In the field of web metrics, a large research effort

has b

een made with proposals from very diverse

perspectives.

In spite of the fact that some of the proposed

etrics have not been formally defined or

theoretically or empirically validated, fortunately, in

the last years, the tendency is changing and

justification, formalization and validation are also

taken into account (Abrahão et al., 2003).

However, most of the work is academic and

doe

sn’t take into account industrial concerns. There

are some exceptions. Among them, we can cite the

works (Reifer, 2000, 2002) and (Mendes et al., 2003,

2005).

To eliminate the gap between practical

application and academ

ic world, it is necessary to

better involve the different actors related to web

technologies. To do so, it is essential to know the

perception of web metrics that these actors have.

With this objective, we have performed a survey

ong web technologies professionals to select the

metrics that are considered interesting or useful by

them. Once we have the set of metrics, we could

measure the web sites or the web applications and

obtain corresponding quality indicators.

Our starting point is to consider the metrics that

we collected

from the literature and were used in our

previous study (Calero et al., 2005) in which we

classified a total of 385 web metrics, using the

WQM quality model.

In the next section, we will expose the criteria

llowed in the metrics selection that we have used

in our survey. In the third section, we will present

the survey, its results and conclusions. Finally, in the

fourth section, future work will be stated.

2 INTERNAL METRIC

SELECTION

As it is not possible to prepare a survey including

the 385 metrics classified in WQM, we performed a

238

Ruiz J., Calero C. and Piattini M. (2006).

WEB METRICS SELECTION THROUGH A PRACTITIONERS’ SURVEY.

In Proceedings of the First International Conference on Software and Data Technologies, pages 238-244

DOI: 10.5220/0001320502380244

 SciTePress

first selection with the objective of restricting them

into a manageable and representative set of web

metrics to be included in our survey.

2.1 Selection Criteria

The selection was made taking into account the

following considerations:

a) The number of metrics must be as limited as

possible.

b) The selection must cover the different

perspectives to be considered.

c) The most relevant works must be examined in a

detailed way, especially those having an

experimental component.

d) Works based on a concrete methodology must

not be refused at the beginning but we will have

to bear in mind the possibility of an easy

generalization.

e) Given that many aspects such as usability have a

great number of metrics, we will have to make an

even bigger synthesis effort than with other

aspects in which it is clear the lack of metrics.

f) It is not necessary that the selected number of

metrics must be proportional to the number of

collected metrics per aspect.

According to these considerations, we will

establish the following criteria for the selection

process:

1. To select those metrics that are proposed in

several works.

2. To select those metrics that represent simple

concepts.

3. To avoid duplicities, eliminating as much as

possible metrics that could be assimilated into

others, with respect to meaning, even not

representing the same concept.

4. To eliminate metrics coming from the

specialization of other metrics. Although they

can allow us to measure certain characteristics in

a more precise way, they can also made us lose a

more general vision.

5. To incorporate some metrics that are not very

common with the purpose of introducing

variability.

6. To incorporate metrics specific for a

methodology but able to be adapted to others.

Furthermore, if at any time there is a

contradiction between the criteria, we will prioritize

the simplest one.

2.2 Metrics Selection

As we have already indicated, our starting point has

been the 385 metrics.

According to criterion 1, we have a set of metrics

proposed by a large number of authors and that have

also a very simple meaning (criterion 2) such as

Number of Web Pages, Depth, Breadth, Number IN

Links, Total Number of Links, Number of Broken

Links, %Broken Links, Total Number of Images,

Images per Page, Images with ALT Text, all of them

with respect to the Website or Web Application, and

Download Time (of a page), Links of a Page and

Images of a Page.

We have also included others such as

Compactness, Stratum, and Cyclomatic Complexity,

based on criterion 5.

Following criteria 1, 2 and 5, we have included

the following: Quick Access Pages, Site Map,

Global Help, Scoped Search, Stability, Link Colour

Style Uniformity, Global Style Uniformity, Foreign

Language Support, Contact Address. And we have

selected other generic metrics (criteria 2) like

Suitable Information and Updated Information.

Concerning usability, as most of metrics are the

result of a specialization (criteria 4), we have

extracted the following (remember that other metrics

have been already included from other works):

Display Colour Count, Text Positioning Count, Text

Cluster Count, Font Count and Reading Complexity,

all of them with respect to a web page.

Regarding works related to the development and

maintenance of web applications, we have selected

(we have not included those included above),

following criterion 2: Media Count, Program Count,

Total Page Allocation, Total Media Allocation,

Total Code Length, Page Allocation, Media

Duration, Media Allocation, Code Length (LOC),

Code Comment Length, Reused Media Count,

Reused Program Count, Total Reused Media

Allocation, Total Reused Code Length, Reused Code

Length, Reused Comment Length, Total Page

Complexity, Page Complexity, Audio Complexity,

Video Complexity, Animation Complexity, Scanned

Image Complexity, Total Effort (Design&Auth),

Total Page Effort, Total Media Effort, Program

Effort, Experience, and Tool Type. And others like

Total Number Flash Animations, Total Number of

Icons/Buttons, Average Length Audio Clips, Average

Length Video Clips, Reused Web Pages, and Reused

Docs.

We include, according to criterion 6, (all of them

with respect to the web application): Web Building

Blocks, Number of COTS Components, Number of

WEB METRICS SELECTION THROUGH A PRACTITIONERS’ SURVEY

239

Object or Application Points, Number of XML,

SGML, HTML and Query Language Lines, Number

of Web Components, Number of Scripts (Visual

Language, Audio, Motion) and Number of Web

Objects. And based on criteria 5, we have taken the

metric Peak Staff.

Besides, due to the application of criterion 4, we

take into account the model efforts and the total

effort: Total Design Effort, Information Effort,

Navigation Effort and Presentation Effort.

Following criterion 2, we select Server Scripts,

Client Scripts, Web Page Scripts, Web Page

WebObjects, Total Languages and Page Languages.

We have not considered other metrics that are

not relevant as compared to the selected ones

because they mean an excessive specialization

(criterion 4).

With this selection we have obtained 85 metrics,

to be included in the survey (see appendix).

3 SURVEY

In this section, we will deal with aspects related to

the survey, its objective, design, obtained results and

its discussion.

3.1 Definition of Objectives

We have focused on the following objectives:

• To determine the importance given by web

professionals to the considered web metrics.

• To study the impact of participant experience on

the importance of metrics.

• To identify other aspects not taken into account

in our work and that are considered important by

web practitioners.

• To identify the concordances/discordances with

the metrics proposed by researchers in the

literature.

3.2 Survey Participants

An important aspect to be considered is who the

survey target since any community has its own

characteristics. For our purposes, our survey is

addressed to practitioners involved in tasks of

developing or maintaining applications and web

systems with diverse degree of experience.

Thus, the technical concepts should not represent

any problem. However, to fulfil our purpose, we

have to take into account other aspects. For example,

if the survey comes from the academic field can be

seen by web technologies professionals as it does

not fulfil their needs and they can refuse to fill it out.

Subjects were not involved only in a passive way. In

addition to theirs answers we tried to involve them

in the project by soliciting suggestions from them.

For this survey our objective population is the

web professionals. For the sample, we have

considered professionals that previously we had

maintained some contact in the past (or with their

companies). Choosing them to conserve the diversity

in the applications developed (scope of work), its

experience degree, and the companies for whom

work.

3.3 Survey Design

To fulfil the fixed objectives, we have structured our

survey into three parts:

A. Data of the Subject.

B. Web Metrics.

C. Suggestions.

We have to take into consideration that the

survey design is conditioned by the high number of

metrics to be included in it. Answering a survey with

questions about 85 metrics carries out certain

reticences regarding the necessary time to fill it out.

Now, we will deal with each part separately.

3.3.1 PART A: Data of the Subject

There are a great variety of web professionals

depending on their experience, their job and the

technologies that they use. For these reasons, and

following the recommendations of (Pfleeger and

Kitchenham, 2001) and (Kitchenham and Pfleeger,

2002a-d, 2003) we have included generic questions

about personal data. Thus, part A, Data of the

Subject, is composed of three questions:

1) Job (Developer, Maintenance Manager, Others)

2) Years of Experience

3) Category of the developed product:

a) Web site with static pages

b) Web sites with dynamic generation of pages,

working with jsp, php, asp, within a

centralized environment (e. g. applications

for small or medium size enterprise)

c) Web sites using Content Management

Systems (such as CMS of Microsoft, Zope,

Tipo3,…) in a distributed environment (e.g.

applications for a corporation)

ICSOFT 2006 - INTERNATIONAL CONFERENCE ON SOFTWARE AND DATA TECHNOLOGIES

240

3.3.2 PART B: Web Metrics

To make part B as simple as possible, we have

decided to use close questions, one for each metric,

quantifying the importance of each metric by using a

Likert scale with an interval from 0 (not important)

to 9 (very important).

To avoid fatigue and motivation loss we decided

to start the questioner by simple metrics.

For providing a common background to the

subjects, we included in the documentation a

minitutorial about the metrics of the survey.

3.3.3 PART C: Suggestions

This part has two open questions. The first is to

include suggestions of other metrics that subjects

consider interesting, and the second is to include

suggestions about the survey. The objective of this

part is, on the one hand, to detect metrics that we

have not considered, and on the other hand, to

validate our survey.

3.4 Results

The survey was sent to the subjects by personalized

email, avoiding as much as possible to give the

impression of being like a circular to avoid the

rejection rate. The survey could be sent back once

filled out in electronic or paper format.

66 surveys were sent and we obtained 42

answers (63.6%), during the ten days deadline.

Two participants classified themselves as web

users and the rest as professionals with different

degrees of experience and different development

environment. Then, the sample of web technologies

is formed by 40 subjects. From them, we have

centred our study as follows.

In the tables 1-4, we show the obtained results

for the different subjects categories. In each one of

the tables, column Score shows the arithmetic

average given by subjects of each considered group,

and Deviat. (or Dev) its standard deviation. As we

have noted above, each metric has been scored into a

scale from 0 (not important) to 9 (very important).

The interpretation of the results shown here will be

analysed in the following section.

For the set of the 40 subjects, the most accepted

metrics are shown in table 1. For those related to

technologies of static web pages (Category a, with

only 6 subjects), we obtain table 2 (we have

included this table in spite of the reduced sample

size). Regarding the most valued metrics for

categories b and c of subjects (34 subjects), we

obtain table 3.

In the table of the appendix, we can see the

complete relation of all the metrics studied in the

survey ordered according the importance given by

these 34 subjects of group b-c. Column Score is the

average score by the metrics, and Dev. its standard

deviation, for each of the group considered.

Table 1: Metrics rank for all the subjects (40 subjects).

Metric Score Deviat.

Updated Information 8.35 1.05

Suitable Information 8.13 1.26

ownload Time 8.05 1.11

Global Style Uniformity 7.88 1.11

Scoped Search 7.65 1.69

ink Colour Style Uniformity 7.58 1.32

Navigation Effor

7.55 1.50

nformation Effor

7.48 1.55

Total Effort (Design) 7.40 1.58

Presentation Effor

7.38 1.56

evelope

’s Experience 7.38 1.58

Quick Access Pages 7.38 1.50

Table 2: Metric rank for static web page developers.

Metric Score Dev

Suitable Information 8.33 0.82

Updated Information 8.33 0.82

oreign Language Suppor

8.00 0.89

% Broken Links 7.83 1.60

Global Style Uniformity 7.6

1.03

ink Colour Style Uniformity 7.50 0.84

Number of IN Links 7.1

0.98

Number of Broken Links 7.1

1.83

ownload Time 7.1

1.4

Global Help 7.00 1.10

Contact Address (e-mail, phone, mail) 7.00 0.63

Scoped Search 7.00 1.10

Table 3: Metric rank for b-c subjects category.

Metric Score Deviat.

Updated Information 8.35 1.10

ownload Time 8.21 0.98

Suitable Information 8.09 1.33

Global Style Uniformity 7.91 1.14

Scoped Search 7.76 1.76

Navigation Effor

7.74 1.26

nformation Effor

7.68 1.36

Total Effort (Design) 7.62 1.30

ink Colour Style Uniformity 7.59 1.40

Presentation Effor

7.56 1.33

eveloper’s Experience 7.53 1.54

WEB METRICS SELECTION THROUGH A PRACTITIONERS’ SURVEY

241

We have divided the group b-c in two subgroups:

the first one is formed by subjects with at least three

years’ experience (19 subjects), and, the other one is

formed by those subjects with less than three years’

experience (15 subjects). In the rest of the section

we will refer to them as subgroups I) and II),

respectively.

We have decided to perform this division since

we think that from three year’s experience there is a

qualitative leap in developer maturity as well as in

their knowledge of the technologies they work with.

For the sake of clarity, in table 4, we have

extracted the results corresponding to the best scored

metrics (score upon 7.50) according to subgroup I

(three or more years experience). As we can see

results do not differ very much with respect to those

obtained by complete category b-c (see table 3).

Table 4: Metrics rank for practitioners with 3 or more

years of experience in categories b-c.

Metric Score Deviat.

Updated Information 8.37 1.12

Download Time 8.32 0.95

Suitable Information 8.21 1.18

Scoped Search 8.16 1.01

Total Effort (Design) 8.11 0.94

Quick Access Pages 8.05 0.97

Information Effort 7.95 1.13

Total Effort (Design&Auth) 7.95 1.03

Global Style Uniformity 7.89 0.94

Navigation Effort 7.89 1.20

Presentation Effort 7.79 1.13

Number of Broken Links 7.79 1.96

Developer’s Experience 7.74 1.05

Page Allocation 7.53 2.06

Program Effort 7.53 1.47

Total Page Effort 7.53 1.43

Furthermore, if we compare the 22 most

highlighted metrics by subgroups I and II, we obtain

that there are more or less the same metrics except

for only four metrics for each subgroup that they do

not appear until positions 16

and 15

respectively

(see appendix).

3.5 Discussion and Interpretation of

Results

We notice that considerations about table 2 –

category a), static web page developers– will be only

indications, because of the reduced sample size.

The first conclusion we can extract is that

Usability is very important. This result was

foreseeable taking into account the importance of

usability in Web Applications (Calero et al., 2005).

Information quality is very important, Suitable

Information and Updated Information. We also note

the coincidence of groups a) and b-c) in other four

metrics among the most valued also related to

usability (Download Time, Scoped Search, Global

Style Uniformity, Link Colour Style Uniformity).

The rest of the metrics in table 3 are related to

effort (Information Effort, Navigation Effort, Total

Effort, Presentation Effort and Developer’s

Experience). It is paradoxical that developers

prioritize the user vs. their effort.

In opposition to the general perception, our

survey shows that importance granted in Literature

to the number of pages and the number of images

does not correspond to the perception that

developers have of them.

Nevertheless, we do not mean that the number of

pages is not important, since precisely the access to

information is made from a page (or a data entry if it

is carried out by other system). But these access

pages would be the important ones and not those

dynamically generated because the most important

aspects are the programs that generate pages, the

information they contain, how information is

presented, and not the number of such pages that can

be generated.

A similar reasoning can be made for the number

of images. In a small website (and normally static),

possibly image processing would be manual. But in

a system with thousands of items, they would be

provided in a digitalized format, probably in a

database. Consequently, system complexity should

be measured according to programs that use the

database, not to the database size.

As we have already mentioned, to achieve the

last cited objective, in section 3.1, we have

incorporated into the survey, a third section of

suggestions not only regarding metrics but also the

survey itself.

With relation to the suggestion of metrics, we

have found that almost all are also related to

Usability and in particular, to Accessibility and

adaptation to standards, compatibility with

navigators and, in a lower degree, others related to

performance and security.

3.6 Conclusions

In summary, the main conclusions we can extract

from the survey are:

¾ Developers prioritize usability instead of their

effort. By this, it is convenient to have tools that

ICSOFT 2006 - INTERNATIONAL CONFERENCE ON SOFTWARE AND DATA TECHNOLOGIES

242

from the first stages of development provide an

estimation of product usability.

¾ Some metrics frequently used in Literature, have

a relative importance for developers (e.g. number

of pages and number of images). This is because

the developed products are complex, with the use

of dynamic generation of pages, and the use of

Content Management Systems. There is a

necessity of metrics and frameworks for that.

4 FUTURE WORK

As we said in the introduction, our work has the

purpose of creating web applications quality

indicators. We consider it essential to take into

account the vision of quality of web professionals.

To do so, we have divided our task into two stages,

the first stage he consists in having a first

approximation to metrics considered relevant by

developers and sites and web applications

administrators. As a result of this, we have obtained

that the best valuated metrics are Updated

Information, Suitable Information, Download Time,

Global Style Uniformity and Scoped Search.

The second stage consists of determining the

importance of each metric with respect to each

quality characteristic and each phase of the life cycle

process. Therefore, we must obtain the metric

weights with the purpose of obtaining quality

indicators of a site or a web application.

However, before starting the second stage, we

aim at carrying out the survey again using a different

group of subjects to check the validity of the

obtained results. The indicators obtained in our work

must be able to be incorporated into web

development methodologies.

Other aspect to be considered (considering the

importance given to usability) is the incorporation of

metrics for estimating the end product usability

during the development. The same happens with

accessibility.

ACKNOWLEDGEMENTS

We would like to thank all participants in this survey

for their time and above all, for their suggestions and

especially to those that not only indicated

suggestions in the survey but also came to talk to the

authors about new approaches in our work. Thank

you all.

This work is part of the CALIPO project (TIC

2003-07804-C05-03) and the CALIPSO network

(TIN2005-24055-E) supported by the Spanish

Ministerio de Educación y Ciencia and by the

DIMENSIONS project (PBC-05-012-1) supported

by FEDER and Junta de Comunidades de Castilla-

La Mancha.

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17.

WEB METRICS SELECTION THROUGH A PRACTITIONERS’ SURVEY

243

APPENDIX

Metric

Score in group b-c

(34 subjects)

Score in group b-c

Subgroup

Experience>=3 years

(19 subjects)

Score in group b-c

Subgroup

Experience<3 years

(15 subject)

Score in group a

(6 subjects)

All Subjects

(40 subjects)

ver. Dev.

Updated

Inf

1.1

71.12

8,33

0,8

8,35

,05

wnl

oad

Tim

.21

,03

17 1

8,05

tab

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,53

8,33

0,8

oba

Sty

nif

rmi

11.14 7.

47.

,39

7 1

,03

,88

Scoped

Sea

h 7.7

1.7

17.272

,00

,65

,69

gat

n Eff

7.74 1.2

1.2

,36

6,50

,35

,55

,50

Inf

n Eff

1.1

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6,33

,55

ota

l Eff

(

)

21.

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47.

17 2

,58

Link

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1.4

7.47 1.2

7.7

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4 7

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ese

tat

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7.7

1.1

7.27 1

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6,33

42 7

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6,50

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6,83

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6,50

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7 1

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7 1

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5,50

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(

sua

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ICSOFT 2006 - INTERNATIONAL CONFERENCE ON SOFTWARE AND DATA TECHNOLOGIES

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