Towards the Quality Improvement of Web Applications by

Neuroscience Techniques

F. J. Domínguez-Mayo

, G. Kubryk

, M. J. Escalona

, G. Denhière

, M. Mejias

and C. Tijus

Computer Languages and Systems department, University of Seville, Seville, Spain

Equipe CHArt (Cognitions Humaine et Artificielle), LUTIN, Université Paris 8, Paris, France

Keywords: User-Centered Design, Web Applications, Neuroscience, Requirements Engineering, Quality.

Abstract: User-centered design not only requires designers to analyse and anticipate how users are likely to use a Web

application, but also to validate their assumptions with regard to user behaviour in real environments.

Cognitive neuroscience, for its part, addresses the questions of how psychological functions are produced by

neural circuitry. The emergence of powerful new measurement techniques

allows neuroscientists and psychologists to address abstract questions such as how human cognition and

emotion are mapped to specific neural substrates. This paper focus on the validation of user-centered

designs and requirements of Web applications by neuroscience techniques and suggest the use of these

techniques to achieve efficient and effectiveness validated designs by real behavior of potential users.

1 INTRODUCTION

Neuroscience is a heterogeneous field, consisting of

many and various sub-disciplines (e.g., Cognitive

Psychology, Behavioral Neuroscience, and

Behavioral Genetics). In order for our understanding

of the brain to continue to deepen, it is necessary

that these sub-disciplines are able to share data and

findings in a meaningful way;

Neuroeconomics (Karmarkar, 2011) is an

interdisciplinary field that seeks to explain

human decision making, the ability to process

multiple alternatives and to choose an optimal

course of action. It studies how economic behavior

can shape our understanding of the brain, and how

neuroscientific discoveries can constrain and guide

models of economics. Behavioral economics

(Karmarkar, 2011) emerged to account for these

anomalies by integrating social, cognitive, and

emotional factors in understanding economic

decisions. Neuroeconomics adds another layer by

using neuroscientific methods in understanding the

interplay between economic behavior and neural

mechanisms. By using tools from various fields,

some scholars claim that neuroeconomics offers a

more integrative way of understanding decision

making. More specific for our purposes is

Neuroinformatics (Adee and Sally, 2008) which is a

research field concerned with the organization of

neuroscience data by the application of

computational models and analytical tools. These

areas of research are important for the integration

and analysis of increasingly large-volume, high-

dimensional, and fine-grain experimental data.

Neuroinformaticians provide computational tools,

mathematical models, and create interoperable

databases for clinicians and research scientists.

There are three main directions where

neuroinformatics has to be applied (INCF, 2013):

 The development of tools and databases for

management and sharing of neuroscience data at

all levels of analysis,

 The development of tools for analyzing and

modeling neuroscience data,

 The development of computational models of the

nervous system and neural processes.

Neuromarketing is a new field of marketing

research that studies customers' sensorimotor,

cognitive, and affective response to marketing

stimuli. In fact, marketing field is related to quality

with the strategic idea that we have to assure that the

software product is accepted by the customer. So,

we can use all these techniques and cross them with

neuroinformatics to achieve the quality improvement

of Web applictions and Web applications

development process. Actually, neuromarketing

research raised interest for both academic and

business side. In fact, certain companies, particularly

337

Domínguez-Mayo F., Kubryk G., Escalona M., Denhière G., Mejías M. and Tijus C..

Towards the Quality Improvement of Web Applications by Neuroscience Techniques.

DOI: 10.5220/0005040303370344

In Proceedings of the 9th International Conference on Software Engineering and Applications (ICSOFT-EA-2014), pages 337-344

ISBN: 978-989-758-036-9

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

those with large-scale goals, have invested in their

own laboratories, science personnel and / or

partnerships with academia (Karmarkar, 2011).

Then, Neuroscience is currently an interdisciplinary

science that collaborates with other fields like

economics, marketing or informatics. This science

could be useful to be applied to quality improvement

of Web applications and Web applications

development process. Regarding quality, we mean

that the Web application must fulfill all requirements

that customers really demand. In addition, it is very

important to control that the software development

process is the most adequate for software developers

to design the software product that we are looking

for our customers. Thus, neuroscience applies to

achieve quality improvement in Web applications

and Web applications development processes.

As regards quality, it is a relevant aspect to

consider in the software engineering context. There

are several different definitions in the literature like,

for example, conformance to user expectations,

which is often described as the “fitness for purpose”

of a piece of software. Another definition of quality

related to software quality measures concerns the

high quality of software design (quality of design)

and the high level software conforms to that design

(quality of conformance). In fact, regarding quality,

we basically focus on quality of the software product

or quality of the software development process. On

the one hand, quality of software product really

means that the software product meets all

requirements and needs that customers demand. On

the other hand, it is very important to control the

software development process to perform the

software product effectively and complete all

customers’ needs. Then, to implement customer’s

requirements is a key aspect for customers to accept

software products.

Normally, good references from satisfied

customers enable business growth in most

companies. A software development company that is

responsive to requesting and demonstrating a "can

do" attitude will gain competitive advantages. In

general, these benefits are obtained from medium to

long-term periods. Internal benefits, including cost

reductions from improved quality levels, are often

achieved much faster. Production costs can be

reduced when production processes are streamlined

or when their effectiveness increases. This can be

achieved through an improved process control that

reduces the undesirable production of unable parts.

Shortened machine setup times and immediate

availability of complete production information can

further improve productivity. Quality professionals

have studied valuable improvement techniques that

lead to reduce production costs through quality

improvements.

This paper comprises the following sections.

After this introduction, Section II analyzes some

related works and concepts found in the literature.

Then, Section III proposes the NDT methodology to

capture and define Web application requirements and

psychological/emotional experiences to be expected

by users. NDT is a Model-Driven Web development

approach for the development of Web applications

which is mainly focused on requirements. Section IV

proposes QuEF for the definition of a Quality Model

from the requirements and psychological/emotional

experiences defined by the NDT methodology. QuEF

provides templates and methods to define the Quality

Model and defines a life cycle for the Quality Model

that ensures the quality continual improvement of the

model. Then, Section V explains how this Quality

Model can be validated by neuroscience techniques.

Concluding the paper is Section VI by stating some

learned lessons and ongoing work.

2 RELATED WORKS AND

CONCEPTS

As far as quality in Web applications based on

neuroscience is concerned, lots of papers describe

the necessity of assuring quality and controlling the

development process of these Web applications or

software products.

Barsalou (Barsalou, 2012) explains that the

human conceptual system contains people's

knowledge of the world. The conceptual system

represents components of experience, such as

knowledge about settings, objects, people, actions,

events, mental states, properties and relations, rather

than containing holistic images of experience.

Componential knowledge in the conceptual system

supports a wide variety of simple cognitive

operations including categorization, inference,

representation of propositions and productive

creation of novel conceptualizations.

Wang and Patel (Wang and Patel, 2009) explore

the basic properties of software and look for the

cognitive computer foundations of software

engineering. They explain that the nature of software

is characterized by computer, behavioral,

mathematical and cognitive properties. The authors

identify a set of fundamental cognitive constraints of

software engineering, such as intangibility,

complexity, indeterminacy, diversity,

ICSOFT-EA2014-9thInternationalConferenceonSoftwareEngineeringandApplications

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polymorphism, inexpressiveness, inexplicit

embodiment and unquantifiable quality measures.

Hofman (Hofman, 2009) examines non-technical

aspects of software quality perception and proposes

further research activities on this subject. Cognitive

science, psychology, microeconomics and other

human-oriented sciences do analyze human

behavior, cognition and decision-making processes.

Therefore, this paper recommends that the

professional product perception should be analyzed

as a software product.

Jean-Michel Hoc reviews the state-of-the-art of

cognitive cooperation in Hoc (Hoc, 2009) to extend

an individual cognitive architecture and handle these

situations, by combining private and cooperative

activities that are highly task-oriented. In Hoc (Hoc,

2009), cooperation is tackled as the management of

interference between individual activities to

facilitate the team members' sub-tasks and the team's

common task, if any. This review of the literature is

a step towards finding out a theoretical approach that

could be relevant to evaluate cooperation and design

assistance in diverse domains.

Zaytsev et al. (Zaytsev and Morrison, 2012)

identify multiple areas where continuous integration

can be employed to further increase the quality of

neuroinformatics projects by improving

development practices and incorporating appropriate

development tools. Finally, they discuss what

measures can be taken to lower the barrier for

developers of neuroinformatics applications to adopt

this useful technique.

As regards international standards for software

products quality, ISO/IEC 25000:2005 (ISO/IEC

25000:2005, 2014) provides guidance on the use of

the new series of International Standards named

Software Product Quality Requirements and

Evaluation (SQuaRE). This guide aims to offer a

general overview of SQuaRE contents, common

reference models and definitions, as well as the

relationship among the documents, allowing users of

this guide to better understand these series of

International Standards, according to their purpose

of use.

3 A MODEL-DRIVEN WEB

DEVELOPMENT

METHODOLOGY BASED ON

WEB REQUIREMENTS

TREATMENT

NDT (Navigational Development Techniques)

(Escalona and Aragón, 2008), is a methodological

approach oriented to the Web Engineering. Web

Engineering is a specific line in the Software

Engineering that offers specific models and

techniques to deal with the special characteristics of

Web systems. In the last years, several web

approaches were defined: OOHDM, UWE, WebML

or OOHare only some examples. However,

comparative studies concluded that these approaches

are mainly focussed on analysis and design phases

and there is an important gap in Web requirements

treatment.

NDT is oriented to cover this gap. Thus, it is

mainly focussed on the requirements and the analysis

phases, although in its last versions it covers the hole

life cycle. It is an approach defined in the Model

Driven paradigm and it offers a suitable and easy

methodological environment. The most important

characteristics of this approach are:

 It offers a friendly interface for the final user in

the requirements phase.

 It is based on a set of MOF metamodels that are

transparent to the development team. These

metamodels are the base of NDT development

process.

 It follows the traceability of the requirements

from their definition until their analysis, offering

a systematic process based on formal

transformations defined by QVT that proceeds

until implementation.

 NDT is completely UML based, so it is

compatible with other approaches such as

Métrica.

NDT is being applied in several real projects. It

was a very applied methodology in real environment

with very good results. Although NDT was initially

supported by NDT-Tool, today it is not used and it is

not being reviewed. In any case, in NDT-

Tool section information about this tool can be

found.

Today, NDT has evolved to be used in practical

environments, and is now one of the best

methodological proposals addressing the

development of many software projects, specifically

projects aimed at the web. IWT2 offers a suite of

support tools that apply the NDT methodology to

your software project. This toolkit is distributed

under the name NDT-Suite (García-García et Al,

2012). Thus, with the NDT methodology not only is

necessary to specify user requirements but

psychological/emotional experiences to be expected

by users.

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4 A FRAMEWORK TO MANAGE

QUALITY OF WEB

APPLICATIONS

Once Web application requirements and

psychological/emotional experiences to be expected

by users is well defined, it is necessary to assure the

quality continual improvement of these concepts on

Web applications. Besides, it is necessary to define a

quality model based on these requirements that must

be validated afterwards by neuroscience techniques.

QuEF (Quality Evaluation Framework)

(Dominguez-Mayo et al., 2012a; Dominguez-Mayo

et al., 2012b) is a framework to manage quality of

any product or process, which aims to enforce

quality and continuous quality improvement of Web

applications and software development process by

means of defining a quality model. QuEF is a

framework to manage quality of entities (products,

processes, services, organizations, etc.) in any

context and domains. In previous works, this

framework was used to manage quality of Model-

Driven Web development methodologies

(Dominguez-Mayo et al., 2012b). QuEF has been

adapted for designers of any products and processes

to analyze, evaluate, control and increase the quality

and improve their design and results. In addition,

this framework can be also used for consumers to

identify the most suitable product or process for

them and decide which one will be used depend on

their project scope.

This framework describes templates and

methods to define a specific quality model for the

domain under study. It also offers a method in order

to instantiate the quality model, evaluate it and

calculate preferences of their elements. Besides, the

framework includes the definition of a set of phases

to enforce the quality continual improvement of the

quality model. This is the most important aspect that

all the quality management is centralized on the

quality model.

The Quality Model represents the core of the

framework and thequality management revolves

around it. We propose a Quality Model metamodel

consisting in a simplification and adaptation of ISO

standards. Particularly, ISO/IEC 15939:2007 defines

a measurement process applicable to system and

software engineering and management disciplines.

The process is described through a model that

defines the activities of the measurement process

that are required to adequately specify what

measurement information is required, how the

measures and analysis results are to be applied, and

how to determine if the analysis results are valid.

The measurement process is flexible, tailorable, and

adaptable to the needs of different users. ISO/IEC

15939 (ISO/IEC 15939:2007 2012) so that the

model instantiation can be more flexible and

practical. The main objective concludes that quality

management becomes strategically active.

Therefore, all the strategic assets have to be

identified and it is necessary to carry out capture,

definition and validation of the Quality Model that

will be used for quality management. The Quality

Model contains: Features and Sub-Features (both are

categories of an entity’s properties). A Feature is a

higher-level category of the domain description of

an entity, while a Sub-Feature is a lower-level

category. A Property points out the degree to which

a Sub-Feature is measured. In simple terms, a

Property is used for measuring Sub-Features. Below,

different levels for Properties and Quality

Characteristics are explained.

 Feature (FT-<Level 1>): It is a general concept

of an entity, a set of properties, but a higher-level

concept of an entity’s characterization that

describes it broadly. A Feature has a set of Sub-

Features.

 Sub-Feature (FT-<Level 0>): It is a specific

concept of an entity. It is a set of Properties, but

a lower-level concept of an entity’s

characterization. It is used to categorize the

Properties of the entity in two levels (Feature and

Sub-Feature).

 Property: A Property is used for describing and

analyzing the Sub-Features of an entity.

As explained before, Quality Characteristics

(hierarchical by Quality Characteristics (or QC-

<Level 1>) and Quality Sub-Characteristics (or QC-

<Level 0>) are the quality aspects together with

these Properties that have to be assured on an entity.

Subsequently, as shown in figure 1, the author

would define the relations between these Properties

and Quality Characteristics to identify how each

Sub-Feature in each Quality Sub-Characteristic is

influenced. These association links would represent

dependencies between Properties and Quality

Characteristics. They would show Quality

Characteristics that are affected by Sub-Features or

areas of the entity that would be significantly

affected if it changed. Association links may be

based on proven and real-world experience.

 Properties are the descriptive environment in

which the quality management is going to be

performed.

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Property

Sub-Feature

Feature

Quality

Characteristic

Quality

Sub-Characteristic

Properties

Quality Characteristics

Quality Metamodel

1..*

Figure 1: Quality Metamodel.

 Quality Characteristics are those quality aspects

designers must ensure in the set of Properties that

are offered to users.

On the contrary, Quality Characteristics and

Quality Sub-Characteristics are quality aspects

influenced by an environment description or

Properties. In other words, Quality Characteristic is

a higher-level quality aspect. Higher-level attributes

are called Quality Characteristics and lower-level

attributes are called Quality Sub-Characteristics, in a

hierarchy of Quality Characteristics. A MoI (Matrix

of Influences) relates Properties and Quality

Characteristics Properties and Quality

Characteristics are organized in rows and columns;

Properties (hierarchical in Features and Sub-

Features) are listed in rows and Quality

Characteristics (hierarchical in Quality

Characteristics and Sub-Characteristics) are

represented in columns.

For instance, if a Web application is going to be

evaluated from the point of view of users, all

requirements have to be defined by the NDT

methodology. As regards properties, all Web

applications requirements have to be described like

functions or their interfaces that the Web application

offer to users. Once the properties are defined,

quality characteristics must be defined following the

defined strategies for the specific context. Then, for

Web applications a very important aspect is the

usability and functionality of the application. In fact,

these two quality characteristics are based on ISO

25000 but, in the end, all these quality

characteristics are abstract concept that have to be

measured by some properties or defined metrics. So,

as regards usability quality characteristic is

concerned, to obtain some value from users, we can

do them some questions like:

 Does the user feel that it is easy and efficient to

get things done with the Web application?

 Does the user see the Web application as visually

attractive?

 Does it feel pleasurable in hand? Does the Web

application give me inspiration? Or wow

experiences?

 Is it easy to learn?

As regards functionality, we can also do some

questions to users like:

Does the user perceive the functions in the Web

application as useful and fit for the purpose?

QuEF can be used from two points of view:

designers’, who need to analyse, control, evaluate

and improve entities and consumers, who need to

compare entities (depending on their context) to

decide the most suitable one for them. The main

difference with other frameworks is that QuEF focus

on the quality model and the framework also defines

a life cycle in which all phases revolve around the

quality model. It is based on ITIL v3 but with a big

difference which is that is not focused on services

but on a quality model. The same way to ITIL v3, it

is composed by five phases to ensure the quality

continual improvement of the quality model. The

aim is to centralize all efforts of the quality

management on the quality model. This means that it

comprises several phases which include different

objectives and artefacts:

 Quality Model Strategy phase: This phase is a

strategic active that focuses on the definition of a

strategy for the quality management. The past, the

present and future view elements of the quality

model in the domain under study are fundamental

to achieve effective and efficient quality

management.

 Quality Model Design phase: This phase is where

the quality model is finally designed in terms of

all strategic actives in the previous phase. This

quality model is the model used in the next phase

for operating for the quality management.

 Quality Model Operation phase: In this phase the

quality model is used to carry out the Quality

management. So, the Analysis and Evaluation

management processes are performed within this

phase.

 Quality Model Transition phase: If the domain or

context is changed for the appearance of new

trends, then this phase describes the processes

that carry out the changes in the quality model but

without affecting the Operation phase.

 Quality Continual Improvement phase: This

phase performs all processes to improve quality

of all processes in the life cycle and the very

same quality model.

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Then, we propose a process to capture, define,

validate and manage the quality continual

improvements of Web application requirements and

psychological/emotional experiences to be expected

by users. This process, as shown in figure 2, is

based on a hypothetical quality model. This

hypothetical quality model is built from the Web

application requirements and psychological/

emotional experiences to be expected by users that

have been captured and defined by the NDT

methodology. Then, this hypothetical Quality Model

must be validated using biofeedback. The steps

include the following activities:

1. Definition of Web application requirements

and psychological/emotional experiences to be

expected by users (this step is covered by the NDT

methodology)

2. Define the hypothetical Quality Model using

templates and methods of QuEF and enforces the

quality continual improvement of the Quality Model.

3. Neuroscience Research for quality evaluation.

a. Measurement of selected parameters about

biofeedback

b. Validation of hypothesis by the evaluation

of information.

Figure 2: Process to capture, define, validate and to

manage the quality continual improvements of

requirements.

5 THE QUALITY MODEL

VALIDATION BY USING

NEUROSCIENCE

TECHNIQUES

This way to evaluate quality by psychological and

emotional experiences let us to express new other

abstracter concepts (independently that ISO

recommend) like directly the perception value of the

Web application for users with question like:

 Is the Web application important to me? What is

its value for me?

There is some cognitive neuroscience research

methodology like Steady State Topography

(abbreviated SST) which is a methodology for

observing and measuring human brain activity. This

methodology has been principally used as a

commercial application in the field of

neuromarketing and consumer neuroscience. In this

case, there is a relation between the quality

assurance of products and neuromarketing but with

some differences. The main objective of

neuromarketing is to sell the product while for the

quality assurance of products is that the product to

be accepted by users.

Figure 3: Activities to validate the hypothetical quality

model for Web applications and the development of Web

applications.

In addition, biofeedback may be used for the process

of gaining greater awareness of many physiological

functions primarily using instruments. Biofeedback

may be used to improve health, performance, and

the physiological changes which often occur in

conjunction with changes to thoughts, emotions, and

behavior. Some kown equipment and techniques that

can be used are:

 Functional magnetic resonance imaging or

functional MRI (fMRI) to measure changes in

activity in parts of the brain. It is an MRI

procedure that measures brain activity by

detecting associated changes in blood flow. This

technique relies on the fact that cerebral blood

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flow and neuronal activation are coupled. When

an area of the brain is in use, blood flow to that

region also increases.)

 Electroencephalography (EEG) is the recording

of electrical activity along the scalp. EEG

measures voltage fluctuations resulting from ionic

current flows within the neurons of the brain.

EEGs can detect changes over milliseconds,

which is excellent considering an action potential

takes approximately 0.5-130 milliseconds to

propagate across a single neuron, depending on

the type of neuron. EEG measures the brain's

electrical activity directly, while fMRI record

changes in blood flow. In fact, fMRI are indirect

markers of brain electrical activity. Anyway,

EEG can be used simultaneously with fMRI.

 Heart rate, respiratory rate and galvanic skin

response to learn why consumers make the

decisions they do, and what part of the brain is

telling them to do it. Heart rate refers to the speed

of the heartbeat, specifically the number of

heartbeats per unit of time. The heart rate is

typically expressed as beats per minute (bpm).

The heart rate can vary according to the body's

physical needs, including the need to absorb

oxygen and excrete carbon dioxide.

For this case, a quality model is going to be

defined by properties and quality characteristics.

Properties are going to represent all requirements

that describe a Web application and quality

characteristics are going to represent psychological

and emotional experiences of Web applications by

users as shown in figure 3. Then, the goal of this

quality management using QuEF is to identify and

assess, on one hand, how changing elements of Web

applications impacts on users behavior. And, On the

other hand, how changing elements of a Web

applications development process impacts on

developers behavior. Thus, a quality model is going

to be defined as a set of properties of Web

applications or a set of properties of the

development process of Web applications that have

to be related to psychological and emotional

experiences.

6 CONCLUSIONS AND FUTURE

WORKS

This paper proposes a process to capture, define,

validate and manage the quality continual

improvements of user requirements and

psychological/emotional experiences to be expected

by users. It focus on the validation of user-centered

designs and requirements of Web applications by

neuroscience techniques and suggest the use of these

techniques to achieve efficient and effectiveness

validated designs by real behavior of potential users.

For the specification of requirements and

psychological/emotional experiences the NDT

methodology is proposed. NDT is a Model-Driven

Web development approach for the development of

Web applications. In addition, a framework to

enforce quality and the quality continual

improvement of Web applications is proposed.

QuEF is a framework to manage quality of any

product or process. So, it can be applied to Web

applications. It is composed by five phases to ensure

the quality continual improvement of the quality

model. The aim is to centralize all efforts of the

quality management on the quality model. In

addition, the framework also defines protocols and

methods to perform each phase, so all protocols and

methods are systematized.

The proposed process is based on a hypothetical

quality model. This hypothetical quality model is

built from the requirements that have been captured

and defined by the NDT methodology and must be

validated using biofeedback.

As far as Web applications development

processes are concerned, we are currently working

in the improvement of the NDT methodology and

the QuEF framework. Furthermore, a tool support is

also being implemented in order to implement this

solution in real environments. So, we can get quality

management in an automatic way using QuEF,

automating the quality management of entities

(products, processes, services, organizations, etc.) in

order to reduce costs, minimize time and improve

quality of the quality management process.

ACKNOWLEDGEMENTS

This research has been supported by the MeGUS of

the Ministerio de Ciencia e Innovación, by the

MERIMEE Project “Programme MERIMEE de

collaboration entre ecoles doctorales françaises et

espagnoles”, by the NDTQ-Framework project

(TIC-5789) of the Junta de Andalucía, Spain and by

the FEDER of European Union for financial support

via the project “THOT. Proyecto de innovación de la

gestión documental aplicada a expedientes de

contratación de servicios y obras de infraestructuras

de transporte” of the “Programa Operativo FEDER

de Andalucía 2007- 2013”.

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REFERENCES

Karmarkar, U. R. Note on Neuromarketing, Harvard

Business School. (9-512-031).

Adee and Sally, 2008. Reverse engineering the brain,

IEEE Spectrum 45 (6): 51–55.

doi:10.1109/MSPEC.2008.4531462.

Barsalou, L.W., 2012. The human conceptual system, The

Cambridge handbook of psycholinguistics, 239-258..

INCF, International Neuroinformatics Coordinating

Facility, http://www.incf.org/documents/incf-core-

documents/INCFStrategyOverview-. Last access July

2013

Gilb, T., 2013. How to Quantify Quality: Finding Scales

of Measure. Twenty First International Conference on

Software Quality Management (SQM 2013). Pp. 3-14,

ISBN: 978-0-9563140-8-6.

Escalona, M.J., Aragón G., 2008. NDT. A Model-driven

Approach for Web requirements. IEEE Transaction on

Software Engineering. Vol. 34. Nº3. IEEE Computer

Society

García-García, J.A., Ortega, M.A., García-Borgoñón, L.,

Escalona, M.J., 2012. NDT-Suite: A Model-Based

Suite for the Application of NDT. Lecture Notes in

Computer Science. Volume 7387, pp 469-472.

ISO/IEC 25000:2005, Software Engineering -- Software

Product Quality Requirements and Evaluation

(SQuaRE) -- Guide to SQuaRE,

Hoc, J.M., Towards a cognitive approach to human–

machine cooperation in dynamic situations,

International Journal of Human-Computer Studies,

Volume 54, Issue 4, Pages 509-540, ISSN 1071-5819,

http://dx.doi.org/10.1006/ijhc.2000.0454, 2001.

Hofman, R., 2009. Software quality psychology. In

Proceedings of the International MultiConference of

Engineers and Computer Scientists (Vol. 1).

Domínguez-Mayo, F.J., Escalona, M. J., Mejías, M., Ross

M., and Staples, G., 2012. A Quality Management

Based on the Quality Model Life Cycle, Computer

Standards & Interfaces, Vol. 34, Issue 4, pp. 396-412.

Domínguez-Mayo, F.J., Escalona, M. J., Mejías, M., Ross

M., and Staples, G., 2012. Quality evaluation for

Model-Driven Web Engineering methodologies,

Information and Software Technology, Volume 54,

Issue 11, pp. 1265-1282, ISSN 0950-5849,

http://dx.doi.org/10.1016/j.infsof.2012.06.007.

Stark, J., 2011. Product Lifecycle Management: 21st

Century Paradigm for Product Realisation, Springer

Science, ISBN: 978-0-85729-545-3.

Wang Y., Patel, S., 2009. Exploring the cognitive

foundations of software engineering. International

Journal of Software Science and Computational

Intelligence (IJSSCI), 1(2), 1-19.

Zaytsev V. Y., Morrison, 2012. A. Increasing quality and

managing complexity in neuroinformatics software

development with continuous integration, Frontiers in

neuroinformatics 6.

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