Review of Literature on Software Quality

Article (PDF Available) · October 2018with 1,597 Reads 
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
Cite this publication
the software development industry considers quality a crucial factor in its development. Applying a certain level of standard to Software Quality (SQ) can help ensure customer satisfaction. This study primarily aims to define the different dimensions of SQ, identify the requirements for enhancing SQ, and present the challenges when SQ is restricted. The study also provides a review on the impact of quality and its measurement in the life cycle of software development. It examines the need for a quality standard to measure the increasing quality requirements and size of software. The findings of this study indicate an increasing need for high-quality software. Moreover, it provides a reference for other scholars regarding SQ testing and SQ in fuzzy logic.
Figures - uploaded by Farhan Alebebisat
Author content
All content in this area was uploaded by Farhan Alebebisat
Content may be subject to copyright.
World of Computer Science and Information Technology Journal (WCSIT)
ISSN: 2221-0741
Vol. 8, No. 5, 32-42, 2018
Review of Literature on Software Quality
Farhan M Al Obisat, Zaid T. Alhalhouli, Tamador I. Alrawashdeh, Tamara E. Alshabatat
Computer and Information Technology Department
Tafila Technical University, TTU
Tafila, Jordan
Abstract the software development industry considers quality a crucial factor in its development. Applying a certain level of
standard to Software Quality (SQ) can help ensure customer satisfaction. This study primarily aims to define the different
dimensions of SQ, identify the requirements for enhancing SQ, and present the challenges when SQ is restricted. The study also
provides a review on the impact of quality and its measurement in the life cycle of software development. It examines the need for a
quality standard to measure the increasing quality requirements and size of software. The findings of this study indicate an
increasing need for high-quality software. Moreover, it provides a reference for other scholars regarding SQ testing and SQ in fuzzy
Keywords- quality challenges; Software Development Life Cycle (SDLC); software measurement; software quality.
In the software development industry, software developers
and engineers are primarily concerned with designing software
that meet delivery, cost, and quality requirements, a property
referred to as software quality (SQ). Customer requirements are
collected in the initial stages of a software project. User
expectations, required services, and software specifications are
seriously considered in these stages because of their effect on
SQ. In the early design and development stage, SQ evaluation
must be conducted to minimize the effort, time, and cost input
into a software product [1].
SQ can be categorized into functional and nonfunctional
SQ. Functional SQ encompasses the software features and
specifications identified in the early phases, whereas
nonfunctional SQ involves features that support the functional
requirements (FRs) of the software, i.e., software services.
The success of an overall software system is significantly
based on SQ, which is considered a critical design component
by developers, users, and project managers [2]. SQ is likewise
a crucial factor in evaluating the global competitiveness of any
software enterprise [3]. Thus, it is essential in sensitive
systems, including control systems and real-time systems,
among many others because poor quality may result in
financial loss, failed missions, and even loss of human life [4].
SQ may be described as a product attribute that meets the
stringent performance and FRs, specific development criteria,
and inherent functions that all professionally designed software
must have [4]. Since the arrival of computer programs,
achieving SQ has been difficult, and various definitions of SQ
have been proposed. Some definitions have been standardized,
but the majority of definitions are deemed too vague and
theoretical. For instance, the International Standard
Organization (ISO) defines SQ as an array of product attributes
by which the quality of a software is illustrated and appraised,
ANSI Standard defines it as the sum of all the characteristics of
a software product or service that represent its capability to
satisfy customer requirements, while IEEE Standards defines
SQ as the array of features of a software product that represent
its capability to satisfy specific needs [2]. SQ is the extent to
which a process, component, or system fulfills a specific
requirement, that is, how much it fulfills customer needs or
expectations through product or service features, thus providing
customer satisfaction [5].
SQ is the extent to which characteristics such as reliability,
maintainability, efficiency, portability, usability, and
reusability are designed into a software product or service [6].
Numerous scholars from different fields have attempted to
develop suitable models to define SQ, including ISO/IEC 9126
model [7], Boehm’s model [8], Dromey’s model [9], and
FURPS Model [10]. The most famous among these models is
ISO/IEC 9126 [7] because it incorporates the features of almost
all the other models.
SQ is traditionally composed of software reliability,
accuracy, maintainability, and usability [11]. Owing to its
multidimensionality, every organization is obliged to identify
which aspects of quality are important to them. Two techniques
for ensuring the quality of a software product include (1)
ensuring the development process of the product and (2)
evaluating of the quality of the end product [12].
SQ is dependent on the access of designers to the ideal
materials, devices, processes, management strategies, and latest
technological developments [13]. Numerous authors have
WCSIT 8 (5), 32 -42, 2018
highlighted that the success or failure of a software product in a
competitive market is reliant on its quality [14, 15].
ISO 9126 standard defines quality as “the totality of
features and characteristics of a product or service that bears on
its ability to satisfy given needs [16]. The development and
improvement of software and quality, respectively, are
essentially organizational in nature, not technical [17, 18]. The
ISO 9126 model (ISO/IEC 9126:2001) consists of a four-part
standard for “Software engineering product quality,” which
includes quality model, external metrics, internal metrics, and
quality-in-use metrics, respectively [19]. A recently proposed
model, McCall's model, describes SQ as the characteristics of a
software product that represent its capability to satisfy both
explicit and implicit requirements. It proposes six high-level,
independent quality measures, namely, Reuse based on Object-
oriented Technology, Dromey's Quality Model, Software
Assurance Technology Center Quality Model, Quality Model
for Object-oriented Design, Metric-based Quality Model for
Object-oriented Design, and Software Metrics. These measures
comprise a set of software features by which the product
quality is depicted and appraised [20]. SQ has also been
depicted [21] according to its product characteristics: (i)
internal quality (i.e., mode of product development), such as
software complexity and configuration; and (ii) external quality
(i.e., product functionality), such as serviceability and
reliability [22]. The three most common SQ definitions are as
1. Software quality is determined by a set of quality
factors [23, 24]
2. Software quality is determined by user satisfaction
3. Software quality is determined by unexpected
software performance or errors [26, 27].
Several definitions of software assurance have been put
forward. In the current study, we define the SQ assurance
(SQA) as crucial to the success of any software company. SQA
guarantees product quality by monitoring its optimum
functionality and documenting its performance for
maintenance. Apart from assessing the application, it monitors
and manages the development processes and condition of all
software products [16]. SQA is a strategic and methodical
evaluation of the quality of a software product and its
conformance to specified processes, practices, and criteria [28].
SQA comprises activities for assessing the process applied
to developed or manufactured software products. Its primary
objective is to ensure that a product meets the minimum
acceptable level of confidence and satisfies the functional
technical requirements. SQA check that standard steps are
followed in evaluating a software product. It covers the entire
development process, such as defined requirements, software
design, coding, source code control, code reviews, change
management, configuration management, testing, release
management, and product integration [29]. SQA mainly serves
to preserve the product quality [30]. SQA measurements are
metrics-based and designed to help enterprises achieve high of
SQ [28]. A metric is defined as “a standard of measurement, a
mathematical function that associates real nonnegative
numbers” [31]. The lack of an effective SQA is a major factor
in the failure of many software projects. Hence, SQA is vital to
the software development life cycle (SDLC) because of its
capability to markedly diminish potential risks and enhance the
success of a project [32].
SQA offers users and designers a guarantee that a software
product is defect-free (both intentional and accidental defects)
during its life cycle and that it performs as expected [33].
This paper is organized as follows. Section two discusses
the concept of SQ, its definitions, and purpose, as well as the
common models employed to describe SQ. Section three
provides a literature review on the measurements and
challenges of SQ, its application in SDLC, and use in assessing
software risk and fuzzy logic (FL). It also presents the two
types of SQ requirements and software testing.
A method to gauge the quality, cost, and effectiveness of a
project and its processes is essential in any software project. A
project without the capability to measure such factors cannot be
completed successfully [34]. SQ is assesses on the basis of its
capability to meet user requirements and realize the purpose it
was designed for [3]. A key element in controlling, managing,
and refining the software development process is software
measurement [3].
The outputs of product development throughout the
analysis, design, and coding stages must be measured,
observed, and managed so they can be verified against pre-
specified criteria. Moreover, product development efforts must
be upgraded at each stage to reduce costs and maintain or
improve market competitiveness [35]
The importance of SQA measurements is highlighted in
international standards such as ISO 9000-3, which is a
guideline for software development, and ISO 9001 [36]. In
addition, the capability maturity model [37] depicts the need
for measurements during software development. Although such
standards and models highlight the significance of software
measurements, detailed guidelines on carrying out SQA
measurements and the objectives of such measurement
programs are lacking [38].
Product failures can be prevented by conducting software
measurements. These measurements alert developers and
engineers about development mistakes and thus avert errors
and flaws both before and during the early stages of product
release, as well as assist in monitoring software development
ISO/IEC 9126 model [7] is provides a broad definition of
SQ in terms of six characteristics for software assessment,
namely, functionality, efficiency, maintainability, portability,
reliability, and usability. This model covers nearly all the
aspects mentioned in early models, such as Boehm’s model [8],
Dromey’s model [9], and McCall’s model [39], and includes
the implicit and explicit quality attributes of a software product.
However, how such characteristics and sub-characteristics can
be measured is not specified in the ISO/IEC 9126 model.
WCSIT 8 (5), 32 -42, 2018
THIS MODEL [3, 7].
Software functionality characteristic
refers to the appropriateness of the
functions of the software.
Correctness of the functions.
The ability of a software component
to interact with other components or
Compliant capability of software.
Unauthorized access to the software
or software functions.
Frequency of failure of the software.
Fault tolerance
Ability of software to recover from
component, or environmental, failure.
Bring back a failed software/system
to full operation, including data and
network connections.
Relates to understanding the
software/ easy to understand
( Human Computer Interaction
Effort for learning different users
Easily to operate the software by a
given user in a given environment.
Time behaviour
Response times for a given thru put,
i.e. transaction rate.
resources used characterized, i.e.
memory, cpu, disk and network
The ability to identify the root cause
of a failure within the software.
The ability to change a software/
The sensitivity to change of a given
system that is the negative impact
that may be caused by system
Testing a software/ system (change).
Change the system to new
specifications or operating
Install the software.
Relates to portability. One example
would be Open SQL conformance
which relates to portability of
database used.
The ability to exchange a given
software component within a
specified environment.
Table 1 summarizes the characteristics and sub-
characteristics of this model [3, 7].
In the ISO 9126 standard [40], SQ has six major quality
characteristics, namely, functionality, reliability, usability,
efficiency, portability, and maintainability, all of which have
sub-characteristics. These quality characteristics are sorted into
external and internal quality sets. Functionality, reliability,
usability, efficiency, flexibility, friendliness, and simplicity are
some of the external quality characteristics that customers
expect from software products because these can be easily
observed. By contrast, maintainability, portability, reusability,
and testability are some of the internal quality characteristics
that developers consider in a software product because these
are linked to their efforts during the development stage [38].
Two types of measurement methods have been developed
to handle the external and internal quality characteristics of
software quality. These methods apply both external and
internal measurements and complement each other.
Nonetheless, owing to the different objectives of each method,
they cannot completely substitute for the other [38].
The ISO 9126 quality model has three perspectives on
quality that distinguish the characteristics and sub-
characteristics of software quality [21]. The first two
perspectives, namely, external and internal, comprise the same
6 characteristics and 26 sub-characteristics (Figure 1). The
third perspective, quality in use, has four unique characteristics.
All three perspectives complement one another. Internal quality
affects external quality, which in turn influences quality in use.
Internal quality measures serve as an early gauge of external
Figure 1. ISO/IEC 9126 quality model [21].
The related properties of SQ present researchers an
effective means to understand the quality being measure, the
meaningful operations on the measured values, and the
interpreted results [41]. Software methodologies such as V-
Model, waterfall method, and RUP are classified as traditional
software development methodologies or heavyweight
methodologies [42]. A software development methodology is
the framework utilized to design, manage, and monitor the
development process of an information system [43]. An SQ
model serves as the framework for evaluating the quality
attributes of a software product [44]
ISO/IEC IS 9126-1 [7] defines a quality model as “the set
of characteristics, and the relationships between them that
provides the basis for specifying quality requirements” and
International Journal of evaluation". The fundamental factors
(i.e., characteristics) are defined through the models
constructed specifically to evaluate SQ [7].
Considerable efforts have been exerted to develop models
of software product quality. Many authors have carried out
WCSIT 8 (5), 32 -42, 2018
literature reviews on quality models and included some
benchmarking, including Al-Badareen [45], Dubey [46], Al-
Qutaish [47], Ghayathri [48], and Samadhiya [49]. These cited
works are all concerned with basic quality models.
External measurements are those that require the end
product and, in the majority of cases, users’ participation,
whereas internal measurements are automatically performed on
the program code via internal software metrics. Furthermore,
external measurements can regulate internal measurement tools
and provide the perceived SQ measurements of a user, whereas
internal measurements can help prevent errors and defects in
the early stages of development [50].
The ultimate purpose of QA is to design and manufacture
software capable of reducing vulnerabilities and satisfying
specific standards of function, consistency, and performance.
Quality assurance (QA) aims to complete a project according to
previously agreed on conditions, criteria, and functionalities
without flaws and potentials for failure [51]. Software metrics
and SQ models are regarded as primary references in the SQ
assessment, though specific methods for assessing SQ via
software metrics have yet to be established [52].
Scarpino and Kovacs [53] investigated the negative effects
on an organization when an SQA tool is applied without first
setting up an SQ process. In their research, an organization that
applied an SQA tool was used selected. Data were collected
through open observational analysis and interviews by an
internal QA expert and an external specialist, respectively.
They author found that team members were not provided
proper guidance and training in using the SQA tool and that
documentation on how the system would align with the
company’s SDLC was nonexistent. The brief period and lack
of prior communication with team members also resulted in
high user resistance toward the application of the tool.
Additionally, the capability of the tool to meet company
requirements was not properly assessed, and inconsistencies in
the reported progress of tool implementation were observed.
IEEE, ISO, and other organizations have attempted to
standardize SQ by forwarding models that incorporate related
SQ characteristics and sub-characteristics [52].
Challenges in Software Quality
Software firms inevitable encounter numerous challenges in
their efforts to provide high-quality software and achieve user
satisfaction [54]. The factors that may impair SQ management
include bureaucracy, inefficient management, tight deadlines,
developer ego, conflicting opinions and principles, additional
costs (e.g. for tool purchases), insufficient resources to
automate the development process, absence of organizational
training on quality standards, low familiarity with and
understanding of the process, an organizational lack of quality
management structure, disapproval from top management, and
futility of an early version of the process [54]. The respondents
confirmed that the initiatives to implement SQA practices are
facing serious obstacles [32].
Several scholars have based the input to measure SQ on the
perspectives of the developers, managers, and users regardless
of the attribute’s relevance [55]. A disadvantage of this
approach is that even though the developer may be unaware of
how the user assessed the SQ, he/she will still judge the
qualities of the project manager and user. Likewise, the user
may be unaware of how developers assess the SQ but will still
judge the developer’s quality. This deficiency may lead to
erroneous results [3]. In the software industry, software must
function, released quickly in the market, and offer competitive
value, all of which must be accomplished with limited
resources [56]
SDLC refers to the time period from conceiving the
software to its release [57]. This life cycle is divided into
various phases as follows: Feasibility study, Analysis and
specification of requirements, Designing, Implementation or
Coding, Testing, Operations, and Maintenance [57]. These
phases can change depending on client demand and situations
and on the SDLC model employed [57]. SDLC concerns the
course of building or maintaining software systems [58]. It
represents the entire development process that a software
development organization must employ to successfully develop
a software product. The modern SDLC has two main
categories, traditional and agile [59]. Agile SDLC methods aim
to shorten the life cycle, minimize bug rates, enhance customer
satisfaction, and accommodate evolving business requirements
during the development process [60]. SQA covers the entire
SDLC, including software design, coding/implementation,
source code control, code reviews, configuration, and testing at
both the development and user end, as well as the management
of changes and market release [61]. There are many reasons,
and one is the improper choice of SDLC model [62]. The
fundamental concepts of software development methodologies
must be properly understood when evaluating the best SDLC
methodology [63].
Software projects confront various risks throughout their
life cycle. Risk is defined as a potential condition or event that
may adversely influence the success of a project; it refers to the
possibility of loss or damage or a factor that involves a
potential danger [64, 65, 66]. Risks affect the reliability, cost,
timetable, and quality of a software product [67].
WCSIT 8 (5), 32 -42, 2018
1. Insufficient analysis of changes in
2. Extended changes in requirements
3. Lack of documentation for
4. Poor definition of requirements
5. Ambiguous changes in requirements
Risks in Software
1. Erroneous estimation of project costs
2. Unrealistic schedule
3. Defective or malfunctioning hardware
Risks in Software Cost
1. Insufficient budget
2. Human errors
3. Limited knowledge on techniques and
4. Necessity of long-term personnel
Risks in Software
1. Inadequate documentation
2. Nonexistent project standards and
3. Nonexistent design documentation
4. Insufficient budget
5. Human errors
6. Unrealistic schedule
Risks in SQ
Understanding the risks in SQ is important. Numerous risks
in SQ have been represented in early studies, and relations
between quality risk events have been observed. Some relations
are based on tool and hardware failures, some pertain to human
errors, limited knowledge, supply disagreements between
developers and clients, and requirements and costs that can
affect SQ [68].
Identifying the different risks in software engineering
projects is a difficult or even impossible endeavor. The most
important risks in such projects are categorized as software
requirement risks, software cost risks, software scheduling risk,
SQ risks, and software business risks [67].
SQ management involves a set of activities that delineate
the process of controlling and managing the SQ, such as SQA,
SQ plan, and SQ control [29]. SQA is the process that
guarantees the quality of a software product through the
application of different methods, knowledge, guidelines, and
criteria in the course of its development life cycle [29].
Different SQ techniques, including code reviews, process
improvements, software testing, risk management, change
management, and configuration have been proposed, all of
which can be implemented manually and automatically via
specialized tools [32]. Software risks can be broken down to
external and internal risks; the former originates from factors
outside the organization and are difficult to manage, and the
latter stems from factors inside the organization. These risks
can likewise be categorized into process, project, and product
risks [69].
Quality is the ability to implement permission, certification,
and intentional denial-of-service attacks [70]. Numerous
quality-related issues and management responsibilities have
been identified in literature. Moreover, management is a key
part of SQA [71].
This unsuccessful development is mainly attributed to the
fact that by the time problems are identified, it is too late to
rectify them. Developers and project managers must possess
the foresight to ascertain potential risks to decrease cost and
enhance quality [72].
Functional, structural, and process qualities are the three
key aspects of SQ [73]. Functional quality refers to the
capability of the software to properly perform its tasks
according to user needs and intended objectives. Structural
quality refers to the resilient structure of the code itself and is
difficult to test compared to functional quality. These first two
qualities are the most common aspects discussed in SQ
literature. However, the last and most critical aspect is process
The specification of FRs is the first stage of software
development and is considered the most important in the
software life cycle. Requirements designed in this stage affect
the succeeding life cycle stages and, subsequently, the SQ [74].
Recognizing and specifying requirements are key components
in the eventual success or failure of a software project [21].
Thus, prioritizing requirements is critical in software
development [75].
Requirements are usually communicated via natural
language [76, 77] and categorized into FRs and nonfunctional
requirements (NFRs). FRs only state the required functionality,
such as “the system must allow users to log in,” whereas NFRs
are statements of human needs (i.e., cognitive requirements),
such as “the user login must be simple and efficient” [78].
Given that requirements frequently change throughout a
project’s life cycle, the resulting design modifications disrupts
the organizational processes applied to manage requirements,
thus causing a wide range of potential defects [79]. The typical
difference between FRs and NFRs is in how the system carries
out a task contrary to what the system is expected to perform
[80, 81]
a. Functional Requirement Quality
The two most popular definitions of FRs are as follows: (1)
a statement that expresses the objective of a product or process
to achieve the expected performance and/or results, and (2) a
requirement that stipulates the mandated function of a system
or system component [82].
Scheduling and budgeting aspects should also be included
in SQA activities in addition to the technical aspects of FRs.
This expanded scope is attributed to the close association
between scheduling and/or budget failure and the fulfillment of
functional technical requirements. Projects with a tight
WCSIT 8 (5), 32 -42, 2018
timetable are frequently burdened by professionally
“dangerous” professional revisions in the project schedule that
can damage the possibility of fulfilling FRs. Projects with
limited budgets and resources allocated to its maintenance
confront the same negative consequences as well [83]. The
basic explanation for the similar effects is that NFRs also
describe behavioral properties [84] and should thus be
considered in the same manner as FRs in the development
process [85].
FRs are expressed through their intended use and describes
users’ interactions with the software product. FRs comprise the
scope, objective, perspective, tasks, user characteristics,
detailed functionalities, software attributes, interface
requirements, and database requirements [86].
FRs are defined by the expected inputs and outputs
expected, otherwise known as Functionality (F), while NFRs
are defined as Usability (U), Reliability (R), Performance (P),
and Product Support (S) [87], as shown in Figure 2. The main
problem in the table is that several key features (e.g.,
portability) are not included.
SQ is defined as “How well the software complies with or
conforms to a given standard or requirements, based on FRs or
specifications.” This attribute can likewise be described as the
aptness of a software product to satisfy a specific objective or
how it compares as a competitive product in the marketplace
b. Nonfunctional Requirement Quality
NFRs are defined as software requirements that designate
how software products will accomplish the tasks they were
designed to perform. They are also referred to as design
constraints. Given that NFRs are difficult to test on occasion,
they commonly undergo subjective assessments [82].
A universally acknowledged definition for NFR has yet to
be proposed [89]. One definition states that “NFRs not only
introduce quality factors but also represent global constraints
under which a system must operate.” Different from FRs which
tackle specific problems, NFRs, also referred to as quality
requirements [90, 91], are usually applied via precise localized
modules or mechanisms.
Pohl [92] argues that the term “nonfunctional” is
misleading and that the term “quality requirements” should be
utilized for product-related and non-constraint NFRs. The value
of NFRs in software and systems development cannot be
repudiated, yet the majority of discussions and studies on how
NFRs should be regarded are still focused on distinguishing
them from FRs [93, 94]. NFRs offers support for design
decisions and constraints by presenting how a required
functionality may be achieved to fulfill the quality concerns of
stakeholders [91, 95, 96, 97, 98, 99, 100]. NFR definitions
remain vague [101,102] and unquantified [103], and as a result,
studying and testing NFRs are continuing challenges [101,102,
Figure 2: FURPS Model
In 1965, Zadeh introduced FL, motivated primarily by the
inaccuracies in measurement methods [63]. FL technique is
applied using heuristic information and indefinite inputs to
achieve complicated functions in modeling complex systems.
In a world full of ambiguities, FL has succeeded in diverse
fields such as decision support, dynamic control, and other
expert systems. Therefore, fuzzy systems are essential in risk
estimation because it can handle crisp values [104].
In [70], an intelligent software early warning system based
on FL was proposed. The warning system utilizes a combined
set of software measurements to gauge the associated risks in
lagging behind schedule, exceeding the allotted budget, and
WCSIT 8 (5), 32 -42, 2018
producing poor quality in software development and
maintenance. The measurements are derived from various
perspectives to help address inaccurate, vague, and partial
information, as well as settle conflicts in an uncertain
environment in software risk assessment by utilizing fuzzy
inference rules, fuzzy linguistic variables, and fuzzy sets.
At the onset of developing an SQ prediction model, the
factors that greatly affect SQ and the number of residual errors
must be identified. In [105] , an FL-based approach was
proposed to calculate error-prone modules via inspection
information. By representing ambiguous and insufficient
information, FL enables machines to comprehend the world in
the same manner as humans [106].
A fuzzy set S of a discourse universe U is indicated by a
membership function associated with each element y of U in a
number in the interval [0, 1], which denotes the membership
grade of y in S [107]. FL-based reasoning offers novel
perspectives in software development, including accumulative
software life cycle and fuzzy artifacts [107]. Given the
difficulties in implementing traditional model-based
approaches, FL-based reasoning aids in gauging the reusability
of software components. The growing complexity of modeling
the problem with various components to measure reusability
has given rise to another renowned technique called neuro-
fuzzy approach [108].
FL is a potent technique for tackling problems with
complex and vague phenomena, which can be evaluated only
linguistically rather than numerically [109]. Furthermore, FL is
useful in estimating multi-criteria decision problems [110,
111]. Numerous fields, such as artificial intelligence and
control theory, have benefitted from FL because it explains a
mathematical system that can be applied to model the inference
framework that facilitates proper human reasoning capabilities
[101]. The variable of FL may have a truth value that ranges
from 0 and 1. FL provides an expedient means of generating
precise mapping between output and input spaces owing to the
natural expression of fuzzy rules [113].
In their work, “Software Quality Assessment Based on
Fuzzy Logic Technique,” Mittal et al. [114] proposed a detailed
FL-based approach to measure SQ. A fuzzy system represents
a mapping between linguistic terms (e.g., “very small”)
ascribed to variables [115]. Fuzzy sets are depicted by
membership functions that associate real numbers in the
interval [0, 1] with points in the fuzzy sets; this function is
known as grade or degree of membership [6]. Attempts have
been made to use FL with historical data to predict error-prone
code modules [105]. FL techniques have been used to compute
metric tree scores, which were then evaluated and
experimented on using other methods. Appropriate components
of the Mandeni fuzzy inference engine were utilized to satisfy
customer demands, resulting in the technique being constructed
according to user requirements [116].
Testing involves various measurement methods to improve
SQ and is in the broad category of software management
practices known as QA. Similar to other activities such as
design and code inspections and defect tracking, software
testing is oriented toward “detection” [117] and is a method to
detect system errors. It helps find and debug system errors,
mistakes, faults, and failures and guarantees the expected
functionality of the system [118]. Unit testing is conducted
only on small units. In integration testing, various integrated
modules are assessed, whereas in system testing, the entire
system is assessed. The method of software system testing
affects the way SQ is evaluated [118].
Software that is user-friendly, error-free, and provides
client satisfaction is considered to be of high quality.
Appropriate software testing techniques are thus necessary to
enhance and maintain quality [58, 119]. Software testing in
SQA involves assessing the functionality, regression, load,
performance, and security of a software product [65]. This
process offers information on whether a software program or
application fulfills the technical and business requirements that
informed its design and development and performs as needed
[120]. Software testing helps identify when problems arise and
diagnose the root of such problems.
As an activity that implements software in a controlled
manner, software testing answers the question “Does the
software behave as specified?” Furthermore, it is frequently
employed along with verification and certification [120]. It is a
process that both verifies the results of SQA and achieves the
intended quality [118]. Software testing developed along with
the development of the software and is thus an essential
element in SDLC. It provides a guarantee that the system will
perform with the required functionality. Consequently,
numerous software testing systems and strategies have been
implemented, including White-box, Black-box, and Grey-box
testing [118]. Software testing can also be conducted in three
ways as follows: unit testing, in which testing is done only on
small units; integration testing, in which different integrated
modules are assessed; and system testing, in which the entire
system is tested [118].
As we know Mobile Learning (mLearning) characterize a
new trend of learning that uses innovations like wireless
communication, personal digital assistants, digital content from
traditional textbooks, and other sources to provide a dynamic
learning environment [121]. Some studies have been done as a
case study in Jordan universities [122].
On the other hand, E-learning involves the use of the
Internet as a communications medium between instructors and
students who are separated by physical distance. Wireless
networks have become very common in this environment, often
replacing wired networks, in order to provide mobile access to
educational systems and the Internet for students and staff. But
these networks must be secured [123].
In [124]), the study was devoted to describe the
government of Jordan Initiative toward E-Government and to
explain the blue print and roadmaps provided to the
government of Jordan. the study has been investigated all the
necessary information technology requirements that are vital to
build an E-Government in Jordan and assess the status of E-
Government initiative achievements in Jordan from many
aspects; E-Connectivity and Infrastructure, E-Human
Resources, E-payment, E-leadership and Information
WCSIT 8 (5), 32 -42, 2018
Technology Industry to determine the problem and challenges
that faces this project.
VIII. Conclusion
This study establishes and describes the current state of
SQA. It focuses on SQ measurement, which can strengthen the
quality of software products or processes. The challenges in
SQ, SQ in SDLC, relation of SQ and software risk, two types
of SQ requirements (i.e., NFRs and FRs), FL technique, and
SQ testing models and methods are also discussed in terms of
their implications in SQA. Additionally, this study analyzed
early studies conducted on SQ. These studies depicted the
development of the SQ models and the increase in SQ features
throughout the years.
[1] Azar, D., Harmanani, H., & Korkmaz, R. (2009). A hybrid
heuristic approach to optimize rule-based software quality
estimation models. Information and Software Technology, 51(9),
[2] Youness, B., Abdelaziz, M., Habib, B., & Hicham, M. (2013).
Comparative Study of Software Quality Models. IJCSI
International Journal of Computer Science Issues, 10(6), 1694-
[3] Challa, J. S., Paul, A., Dada, Y., Nerella, V., Srivastava, P. R., &
Singh, A. P. (2011). Integrated Software Quality Evaluation: A
Fuzzy Multi-Criteria Approach. JIPS, 7(3), 473-518.
[4] Suman, M. W., & Rohtak, M. D. U. (2014). A Comparative Study
of Software Quality Models. International Journal of Computer
Science and Information Technologies, 5(4), 5634-5638.
[5] Hossain, A., Kashem, M. A., & Sultana, S. (2013). Enhancing
software quality using agile techniques. IOSR Journal of Computer
Engineering, 10(2), 87-93.
[6] Gupta, D., Goyal, V. K., & Mittal, H. (2011). Comparative study
of soft computing techniques for software quality
model. International Journal of Software Engineering Research &
Practices, 1(1), 33-37.
[7] International Organization for Standardization, & International
Electrotechnical Commission. (2001). Software Engineering--
Product Quality: Quality model (Vol. 1). ISO/IEC Available at
[8] Boehm, B. W., Brown, J. R., & Lipow, M. (1976, October).
Quantitative evaluation of software quality. In Proceedings of the
2nd international conference on Software engineering (pp. 592-
605). IEEE Computer Society Press.
[9] Dromey, R. G. (1995). A model for software product quality. IEEE
Transactions on software engineering, 21(2), 146-162.
[10] Singh, I. (2013). Different Software Quality Model. International
Journal on Recent and Innovation Trands in Computing and
Communication, 1(5), 438-442.
[11] R.C. Dromey, A model of software product quality, IEEE
Transactions on software Engineering (February) (1995) 146162.
[12] Kannabiran, G., & Sankaran, K. (2011). Determinants of software
quality in offshore developmentAn empirical study of an Indian
vendor. Information and Software Technology, 53(11), 1199-1208.
[13] Li, E. Y., Chen, H. G., & Cheung, W. (2000). Total quality
management in software development process. The Journal of
Quality Assurance Institute, 14(1), 4-6.
[14] Luftmann, J., & Kempaiah, R. (2008). Key issues for IT executives
2007. MIS Quarterly Executive, 7(2).
[15] Tian, J. (2004). Quality-evaluation models and
measurements. IEEE software, 21(3), 84-91.
[16] Agrawal, M., & Chari, K. (2007). Software effort, quality, and
cycle time: A study of CMM level 5 projects. IEEE Transactions
on software engineering, 33(3)..
[17] Issac, G., Rajendran, C., & Anantharaman, R. N. (2006). An
instrument for the measurement of customer perceptions of quality
management in the software industry: An empirical study in
India. Software Quality Journal, 14(4), 291-308.
[18] Gopal, A., & Koka, B. R. (2009). Determinants of service quality
in offshore software development outsourcing. In Information
Systems Outsourcing (pp. 497-523). Springer, Berlin, Heidelberg.
[19] S. Birla, M. Johansson, "Quality Requirements for Software
dependent Safety-critical Systems History current status and future
needs", 26.01. 2014, [online] Available:
[20] Ortega, M., Pérez, M., & Rojas, T. (2003). Construction of a
systemic quality model for evaluating a software product. Software
Quality Journal, 11(3), 219-242.
[21] Boegh, J. (2008). A new standard for quality requirements. IEEE
Software, 25(2), 57.
[22] Gorla, N., & Ramakrishnan, R. (1997). Effect of software structure
attributes on software development productivity. Journal of
Systems and Software, 36(2), 191-199.
[23] Iee, E. (1990). IEEE standard glossary of software engineering
[24] International Organization for Standardization. (1994). ISO 8402:
1994: Quality Management and Quality Assurance-Vocabulary.
International Organization for Standardization.
[25] Deephouse, C., Goldenson, D., Kellner, M., & Mukhopadhyay, T.
(1995, January). The effects of software processes on meeting
targets and quality. In System Sciences, 1995. Proceedings of the
Twenty-Eighth Hawaii International Conference on(Vol. 4, pp.
710-719). IEEE.
[26] Carey, D. (1996). Is software quality intrinsic, subjective, or
relational?. ACM SIGSOFT Software Engineering Notes, 21(1),
[27] Lanubile, F., & Visaggio, G. (1997). Evaluating predictive quality
models derived from software measures: lessons learned. Journal
of Systems and Software, 38(3), 225-234.
[28] Agarwal, R., Nayak, P., Malarvizhi, M., Suresh, P., & Modi, N.
(2007, August). Virtual quality assurance facilitation model.
In Global Software Engineering, 2007. ICGSE 2007. Second IEEE
International Conference on (pp. 51-59). IEEE.
[29] Sharma, E. A., Padda, E. S., & Kaur, E. J. “New Approach
Towards Ensuring Software Quality.”, International Journal of
Engineering Research and Applications (IJERA), Vol. 2, Issue 1,
Jan-Feb 2012, pp. 452-454.
[30] Boehm, B., Chulani, S., Verner, J., & Wong, B. (2007, May). Fifth
workshop on software quality. In Software Engineering-
Companion, 2007. ICSE 2007 Companion. 29th International
Conference on (pp. 131-132). IEEE.
[31] Dictionary, M. W. (2013). 2013 Merriam-Webster,
Incorporated. An Encyclopedia Britannica Company.
[32] Sowunmi, O. Y., Misra, S., Fernandez-Sanz, L., Crawford, B., &
Soto, R. (2016). An empirical evaluation of software quality
assurance practices and challenges in a developing country: a
comparison of Nigeria and Turkey. SpringerPlus, 5(1), 1921.
[33] Committee on National Security Systems. National Information
Assurance Glossary (April 26, 2010).
measurements/ accessed 29 January 2018.
[35] Kevitt, M. (2010). Best Software Test & Quality Assurance
practices in the project life-cycle. An approach to the creation of a
process for improved test & quality assurance practices in the
project life-cycle of an SME (Doctoral dissertation, Dublin City
[36] ISO, B. (2000). 9001: 2008 Quality management systems.
Requirements. International Organization for Standardization.
[37] Paulk, M. C., Curtis, B., Chrissis, M. B., & Weber, C. V. (1993).
Capability maturity model, version 1.1. IEEE software, 10(4), 18-
[38] Stavrinoudis, D., & Xenos, M. N. (2008, June). Comparing internal
and external software quality measurements. In JCKBSE (pp. 115-
[39] McCall, J. A., Richards, P. K., & Walters, G. F. Factors in
Software Quality, 1977, Vol. I, II, and III, US Rome Air
Development Center Reports-NTIS AD/A-049 014. NTIS AD/A-
049 015 and NTIS AD/A-049 016, US Department of Commerce.
WCSIT 8 (5), 32 -42, 2018
[40] Iso, I., & Std, I. E. C. (2001). 9126 Software product evaluation
quality characteristics and guidelines for their use. ISO/IEC
Standard, 9126.
[41] Jorgensen, M., & Shepperd, M. (2007). A systematic review of
software development cost estimation studies. IEEE Transactions
on software engineering, 33(1).
[42] Ņikiforova, O., Sukovskis, U., & Ņikuļšins, V. (2008). Integration
of MDA framework into the model of traditional software
development. publication. editionName, 229-239.
[43] Sommerville, I., & Prechelt, L. (2010). Software testing. Software
Engineering, 9th edn. Addison-Wesley.
[44] Behkamal, B., Kahani, M., & Akbari, M. K. (2009). Customizing
ISO 9126 quality model for evaluation of B2B
applications. Information and software technology, 51(3), 599-609.
[45] Bassam, A. B. A. (2011). Software Quality Evaluation: User’s
View. International Journal of Applied Mathematics and
Informatics, (3), 200-207.
[46] Dubey, S. K., Ghosh, S., & Rana, A. (2012). Comparison of
software quality models: an analytical approach. International
Journal of Emerging Technology and Advanced Engineering, 2(2),
[47] Al-Qutaish, R. E. (2010). Quality models in software engineering
literature: an analytical and comparative study. Journal of
American Science, 6(3), 166-175.
[48] Ghayathri, J., & Priya, E. M. (2013). Software Quality Models: A
Comparative Study. International Journal of Advanced Research in
Computer Science and Electronics Engineering (IJARCSEE), 2(1),
[49] Samadhiya, D., Wang, S. H., & Chen, D. (2010, October). Quality
models: Role and value in software engineering. In Software
Technology and Engineering (ICSTE), 2010 2nd International
Conference on (Vol. 1, pp. V1-320). IEEE.
[50] Shepperd, M. (1993). Software engineering metrics I: measures
and validations. McGraw-Hill, Inc..
[51] Iacob, I. M., & Constantinescu, R. (2008). Testing: First Step
Towards Software Quality. Journal of Applied Quantitative
Methods, 3(3).
[52] Khosravi, K., & Guéhéneuc, Y. G. (2004, November). On issues
with software quality models. In Proceedings of the 11th Working
Conference on Reverse Engineering (pp. 172-181).
[53] Scarpino, J., & Kovacs, P. (2008). An Analysis of a Software
Quality Assurance Tool’s Implementation: A Case Study. Journal
of the International Association for Computer Information
Systems, 9(2), 9.
[54] Elgebeely AR (2013) Software quality challenges and practice
recommendations. In: IBM.
quality-challenges-practice- recommendations/. Accessed 13
March 2017.
[55] Srivastava, P. R., Singh, A. P., & Vageesh, K. V. (2010).
Assessment of software quality: A fuzzy multi-criteria
approach. Evolution of Computation and Optimization Algorithms
in Software Engineering Applications and Techniques, IGI Global
USA, 200-219.
[56] Srivastava, P. R., Jain, P., Singh, A. P., & Raghurama, G. (2009,
December). Software quality factor evaluation using Fuzzy multi-
criteria approach. In IICAI (pp. 1012-1029).
[57] Snöbohm, J. (2015). A Study of Creativity and Innovation Support
Within an Agile Context: Applied on a Scrum Team.
[58] Singh, S., Singh, S., & Singh, G. (2010). Reusability of the
Software. International Journal of Computer Applications (0975
8887) Volume, 7, 38-41
[59] Leau, Y. B., Loo, W. K., Tham, W. Y., & Tan, S. F. (2012).
Software development life cycle AGILE vs traditional approaches.
In International Conference on Information and Network
Technology (Vol. 37, No. 1, pp. 162-167).
[60] Cho, J. (2008). Issues and Challenges of agile software
development with SCRUM. Issues in Information Systems, 9(2),
[61] Scarpino, J. J., & Chicone, R. G. (2014). The Quality of Agile-
Transforming A Software Development Company's Process: A
Follow-Up Case Study. Issues in Information Systems, 15(2).
[62] Clara, V. T. (2013). SDLC and model selection: a study
International Journal of Application or Innovation in Engineering
& Management (IJAIEM) , Volume 2, Issue 1, January 2013.
[63] Seema, S. (2012). Analysis and tabular comparison of popular
SDLC models. International Journal of Advances in Computing
and Information Technology.
[64] Antonov, A., Nikolov, V., & Yanakieva, Y. (2006). Risk
Simulation in Project Management System. In International
Conference on Computer Systems and Technologies-Compsystech.
[65] Galorath, D. D., & Evans, M. W. (2006). Software sizing,
estimation, and risk management: when performance is measured
performance improves. CRC Press.
[66] Kruchten, P. (2004). The rational unified process: an introduction.
Addison-Wesley Professional. chapter 7.
[67] Agrawal, A., & Maurya, L. S. Implementing Fuzzy Logic for
Software’s Risk and Quality Estimation. note : Published in
National Conference in SRMS CET, Publication Date: Jan 24,
2014 Implementing Fuzzy Logic for Software’s Risk and Quality
[68] Hoodat, H., & Rashidi, H. (2009). Classification and analysis of
risks in software engineering. World Academy of Science,
Engineering and Technology, 56(32), 446-452.
[69] Yong, H., Juhua, C., Zhenbang, R., Liu, M., & Kang, X. (2006,
December). A neural networks approach for software risk analysis.
In Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth
IEEE International Conference on (pp. 722-725). IEEE.
[70] Rahman, W. N. W. A., Talha, H., Josiah, B., Adamu, L., Liming,
W., & Rosli, N. S. M. (2015). Software Quality AssuranceE-
commerce Customers Satisfaction in Requirements Engineering
Process. International Journal of Software Engineering and Its
Applications, 9(3), 57-70.
[71] Hribar, L., Burilovic, A., & Huljenic, D. (2009, June).
Implementation of the Software Quality Ranks method in the
legacy product development environment. In Telecommunications,
2009. ConTEL 2009. 10th International Conference on(pp. 141-
145). IEEE.
[72] Liu, X. F., Kane, G., & Bambroo, M. (2006). An intelligent early
warning system for software quality improvement and project
management. Journal of Systems and Software, 79(11), 1552-1564.
[73] Dash, R., & Dash, R. (2010). Risk assessment techniques for
software development. European journal of scientific
research, 42(4), 629-636.
[74] Eckroth, J., & Amoussou, G. A. (2007, March). Improving
software quality from the requirements specification.
In Proceedings of the 2007 Symposium on Science of Design (pp.
38-39). ACM.
[75] Giesen, J., & Volker, A. (2002). Requirements interdependencies
and stakeholders preferences. In Requirements Engineering, 2002.
Proceedings. IEEE Joint International Conference on (pp. 206-
209). IEEE.
[76] Luisa, M., Mariangela, F., & Pierluigi, N. I. (2004). Market
research for requirements analysis using linguistic
tools. Requirements Engineering, 9(1), 40-56.
[77] Neill, C. J., & Laplante, P. A. (2003). Requirements engineering:
the state of the practice. IEEE software, 20(6), 40-45.
[78] Felici, M., Sujan, M. A., & Wimmer, M. (2000). Integration of
functional, cognitive and quality requirements. A railways case
study. Information and Software Technology, 42(14), 993-1000.
[79] Pressman, R. S. (2005). Software engineering: a practitioner's
approach. Palgrave Macmillan.
[80] Robertson, S., & Robertson, J. (2012). Mastering the requirements
process: Getting requirements right. Addison-wesley.
[81] Sommerville, I., & Sawyer, P. (1997). Requirements engineering: a
good practice guide. John Wiley & Sons, Inc..
[82] ISO, I. (2010). IEEE, Systems and Software Engineering--
Vocabulary. IEEE computer society, Piscataway, NJ.
[83] Galin, D. (2004). Software quality assurance: from theory to
implementation. Pearson Education India.
[84] Glinz, M. (2007, October). On non-functional requirements.
In Requirements Engineering Conference, 2007. RE'07. 15th IEEE
International(pp. 21-26). IEEE.
WCSIT 8 (5), 32 -42, 2018
[85] Broy, M. (2015). Rethinking nonfunctional software requirements.
Computer, IEEE Computer 48(5), 96-99.
[86] Bassil, Y. (2012). A Simulation Model for the Waterfall Software
Development Life Cycle. International Journal of Engineering and
Technology, 2(5).
[87] Grady, R. B. (1992). Practical software metrics for project
management and process improvement. Prentice-Hall, Inc.
[88] Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). Servqual:
A multiple-item scale for measuring consumer perc. Journal of
retailing, 64(1), 12.
[89] Chung, L., & do Prado Leite, J. C. S. (2009). On non-functional
requirements in software engineering. In Conceptual modeling:
Foundations and applications (pp. 363-379). Springer, Berlin,
[90] Barbacci, M., Klein, M. H., Longstaff, T. A., & Weinstock, C. B.
(1995). Quality Attributes (No. Cmu/Sei-95-Tr-021). Carnegie-
Mellon Univ Pittsburgh Pa Software Engineering Inst.
[91] Cysneiros, L. M., & do Prado Leite, J. C. S. (2004). Nonfunctional
requirements: From elicitation to conceptual models. IEEE
transactions on Software engineering, 30(5), 328-350.
[92] Pohl, K. (2010). Requirements engineering: fundamentals,
principles, and techniques. Springer Publishing Company,
[93] Broy, M. Rethinking nonfunctional software requirements: A novel
approach categorizing system and software requirements. Software
Technology, 10.
[94] Glinz, M. (2007, October). On non-functional requirements.
In Requirements Engineering Conference, 2007. RE'07. 15th IEEE
International(pp. 21-26). IEEE.
[95] Cysneiros, L. M., do Prado Leite, J. C. S., & Neto, J. D. M. S.
(2001). A framework for integrating non-functional requirements
into conceptual models. Requirements Engineering, 6(2), 97-115.
[96] Chung, L., Nixon, B. A., Yu, E., & Mylopoulos, J. (2012). Non-
functional requirements in software engineering (Vol. 5). Springer
Science & Business Media.
[97] Matoussi, A., & Laleau, R. (2008). A survey of non-functional
requirements in software development process. LACL.
[98] Sommerville, I. Software Engineering (Tenth edition,2016).
[99] ElFar, I. K., & Whittaker, J. A. (2001). ModelBased Software
Testing. Encyclopedia of Software Engineering.
[100] Chung, L., & do Prado Leite, J. C. S. (2009). On non-functional
requirements in software engineering. In Conceptual modeling:
Foundations and applications (pp. 363-379). Springer, Berlin,
[101] Borg, A., Yong, A., Carlshamre, P., & Sandahl, K. (2003). The bad
conscience of requirements engineering: an investigation in real-
world treatment of non-functional requirements.
[102] Ameller, D., Ayala, C., Cabot, J., & Franch, X. (2012, September).
How do software architects consider non-functional requirements:
An exploratory study. In Requirements Engineering Conference
(RE), 2012 20th IEEE International (pp. 41-50). IEEE.
[103] R. B. Svensson, T. Gorschek, and B. Regnell. Quality requirements
in practice: An interview study in requirements engineering for
embedded systems. In Requirements Engineering: Foundation for
Software Quality, volume 5512 of Lecture Notes in Computer
Science. Springer, 2009.
[104] Svensson, R. B., Gorschek, T., & Regnell, B. (2009, June). Quality
requirements in practice: An interview study in requirements
engineering for embedded systems. In International Working
Conference on Requirements Engineering: Foundation for
Software Quality (pp. 218-232). Springer, Berlin, Heidelberg.
[105] So, S. S., Cha, S. D., & Kwon, Y. R. (2002). Empirical evaluation
of a fuzzy logic-based software quality prediction model. Fuzzy
Sets and Systems, 127(2), 199-208.
[106] Michael, N. (2005). Artificial intelligence a guide to intelligent
systems, Pearson Education Limited, 2005.
[107] Marcelloni, F., & Aksit, M. (1997, June). Applying fuzzy logic
techniques in object-oriented software development. In European
Conference on Object-Oriented Programming (pp. 295-298).
Springer, Berlin, Heidelberg.
[108] Al-Jamimi, H. A., & Ahmed, M. (2012, June). Prediction of
software maintainability using fuzzy logic. In Software
Engineering and Service Science (ICSESS), 2012 IEEE 3rd
International Conference on (pp. 702-705). IEEE.
[109] Lin, C. T. (2007, December). New product portfolio selection
using fuzzy logic. In Industrial Engineering and Engineering
Management, 2007 IEEE International Conference on (pp. 114-
118). IEEE.
[110] Lin, C. T. (2000) ‘A knowledge-based method for bid/no-bid
decision making in project management‘, Proceedings of PMI
Research Conference 2000 Paris, France: pp. 347-355.
[111] Chen, L. H., & Chiou, T. W. (1999). A fuzzy credit-rating
approach for commercial loans: a Taiwan case. Omega, 27(4), 407-
[112] Martin, C. L., Pasquier, J. L., Yanez, C. M., & Tornes, A. G.
(2005, September). Software development effort estimation using
fuzzy logic: a case study. In Computer Science, 2005. ENC 2005.
Sixth Mexican International Conference on(pp. 113-120). IEEE.
[113] Zadeh, L. A. (2002). From computing with numbers to computing
with words: From manipulation of measurements to manipulation
of perceptions. In The Dynamics of Judicial Proof(pp. 81-117).
Physica, Heidelberg.
[114] Mittal, H., & Bhatia, P. (2009). Software maintainability
assessment based on fuzzy logic technique. ACM SIGSOFT
Software Engineering Notes, 34(3), 1-5.
[115] Munakata, T., & Jani, Y. (1994). Fuzzy systems: An
overview. Communications of the ACM, 37(3), 69-77.
[116] Senior, J., Allison, I. K., & Tepper, J. A. (2007). Automated
software quality visualisation using fuzzy logic
techniques. Communications of the IIMA, Volume 7, Number 1.
[117] Kaner, C., Bach, J., & Pettichord, B. (2008). Lessons learned in
software testing. John Wiley & Sons.
[118] Jan, S. R., Shah, S. T. U., Johar, Z. U., Shah, Y., & Khan, F.
(2016). An Innovative Approach to Investigate Various Software
Testing Techniques and Strategies. International Journal of
Scientific Research in Science, Engineering and Technology
(IJSRSET), Print ISSN, 2395-1990.
[119] Pressman, R. S. (2005). Software engineering: a practitioner's
approach. Palgrave Macmillan.
[120] El-Sofany, H. F., Taj-Eddin, I. A., El-Hoimal, H., Al-Tourki, T., &
Al-Sadoon, A. (2013). Enhancing Software Quality by an SPL
Testing based Software Testing. International Journal of Computer
Applications, 69(6).
[121] Al Obisat, FM; HS Alrawashdeh; H Altarawneh and M
Altarawneh. 2013. "Factors Affecting the Adoption of E-
Learning: Jordanian Universities Case Study." Computer
Engineering and Intelligent Systems, 4(3), 32-39.
[122] Alzboun, Faried; Haroon Alatarwneh; Mohammad Altarawneh
and Farhan M Al Obisat. 2013. "An Assessment for Jordan's E-
Government Initiative Projects: A Conceptual Framework."
Computer Engineering & Intelligent Systems, 4(3), 1-11.
[123] Masadeh, Shadi R; Nedal Turab and Farhan Obisat. 2012. "A
Secure Model for Building E-Learning Systems." Network
Security, 2012(1), 17-20.
[124] Obisat, Farhan and Ezz Hattab. 2009. "A Proposed Model for
Individualized Learning through Mobile Technologies.”,
International Journal of Communications 3, no. 1 (2009): 125-132.
Farhan Alebeisat:
(PhD) is an Associate Prof. working in Tafila Technical University (TTU) in
the Department of Computer and Information Technology since 2012, my
research interests are E-technology, web application and software engineering.
I carried out my PhD degree in 2009. I have many published papers in
different areas.
Zaid T. Alhalhouli:
(PhD) is an Assistant Professor of computer and Information System at the
Department of Computer and Information Technology of the University of
Tafila Technical University (Jordan). He was awarded a PhD from Tenaga
National University (UNITEN), Malaysia in the beginning of 2015. His
research interests include E-learning systems, mobile computing technology,
big data and machine learning, Information and knowledge sharing,
WCSIT 8 (5), 32 -42, 2018
Information technology and healthcare, Social Networks, and human-
computer interaction.
Tamador I. Alrawashdeh:
She is graduated from Tafila technical university department of Computer and
Information Technology and she was worked as a research assistant in 2016-
Tamara E. Alshabatat:
She is graduated from Tafila technical university department of computer and
information technology and she was worked as a research assistant in 2016-
2017. Now she is studying master of computer science in Mutah university.
  • Article
    With years of frantic development, when release fast and release often was the mandatory rule for web technologies and services, the open source paradigm and online distribution repositories have imposed de facto standards for quality assessment in fast‐paced innovation processes. Nowadays, however, in pursuit of productivity, security, and user satisfaction, the industry is beginning, through the introduction of new standards such as ECMAScript 6 or web components, to consider software engineering mandates for web technologies. This article reports a quality model aligned with international standard ISO/IEC 25010, covering web components technology, which ultimately aims to improve adoption by the software engineering industry, traditionally wary of agile Internet practices, the open source paradigm, and public repositories. Our research also presents an experimentation platform on which end users have validated the quality properties, highlighting the implicit connection with the perceived quality. The key result of our research convinces us that user ratings are suitable as a testing mechanism for product quality and quality‐in‐use metrics in order to define an absolute scale of comparison for web component quality. ‐Internet developers use an implicit quality model for Web Components ‐Web Components can be endowed with an explicit quality model based on ISO 25010 ‐The relationship between implicit/explicit models can be validated by end‐users ‐Quality of web components can be predicted based in explicit metrics.
  • Article
    Full-text available
    In recent years there has been an increasing focus in many countries on the concept of electronic government. Many countries see it as a central component of efforts to " modernize " or " reinvent " government. In Jordan, there has been discussion around using an E-Government initiative to move government from what is best described as a manual model to a networked model. The aim of this study is to describe the government of Jordan Initiative toward E-Government and to explain the blue print and roadmaps provided to the government of Jordan. Recent studies indicate that E-Government initiatives have not held their promise of improving government services. This study investigate all the necessary information technology requirements that are vital to build an E-Government in Jordan and assess the status of E-Government initiative achievements in Jordan from many aspects; E-Connectivity and Infrastructure, E-Human Resources, E-payment, E-leadership and Information Technology Industry to determine the problem and challenges that faces this project. A comprehensive questionnaire is designed to help us to put our hands on the existing problems and take it directly from people who directly interact with E-Government. Those people are public, government, and business. Many recommendations resulted as a consequence of this thesis; the expected resulting recommendations eventually will serve various sectors (public, government, and business). Regardless of great challenges, Jordan is certain to move up the ladder towards reaching the developed countries of the digital world.
  • Article
    Full-text available
    Background: The importance of quality assurance in the software development process cannot be overempha‑ sized because its adoption results in high reliability and easy maintenance of the software system and other software products. Software quality assurance includes different activities such as quality control, quality management,quality standards, quality planning, process standardization and improvement amongst others. The aim of this work is to further investigate the software quality assurance practices of practitioners in Nigeria. While our previous work covered areas on quality planning, adherence to standardized processes and the inherent challenges, this work has been extended to include quality control, software process improvement and international quality standard organization membership. It also makes comparison based on a similar study carried out in Turkey. The goal is to generate more robust findings that can properly support decision making by the software community. The qualitative research approach, specifically, the use of questionnaire research instruments was applied to acquire data from software practitioners. Results: In addition to the previous results, it was observed that quality assurance practices are quite neglected and this can be the cause of low patronage. Moreover, software practitioners are neither aware of international standards organizations or the required process improvement techniques; as such their claimed standards are not aligned to those of accredited bodies, and are only limited to their local experience and knowledge, which makes it questionable. The comparison with Turkey also yielded similar findings, making the results typical of developing countries. The research instrument used was tested for internal consistency using the Cronbach’s alpha, and it was proved reliable. Conclusion: For the software industry in developing countries to grow strong and be a viable source of external revenue, software assurance practices have to be taken seriously because its effect is evident in the final product.Moreover, quality frameworks and tools which require minimum time and cost are highly needed in these countries.
  • Article
    Full-text available
    Software quality models play an important role in the measurement of software quality. A number of qualities models are used to build quality software. Different researchers have proposed different software quality models to help measure the quality of software products. In our research, we are discussing the different software quality models and comparing the software quality models with each other.
  • Article
    Every software project is exposed to adverse external influences, the so called project risks that affect the cost, duration of the project and possibly the quality of the product but without risk, the project becomes lackluster and sometimes can't be completed to the fullest satisfaction. So with risk analysis, it can be determined for a specific project what the risks are. These risks then should be included in a systematic and formal manner in the project estimate in order to obtain a realistic project plan. This paper focuses on the problem of how to manage risk in the software development. We have discussed how Spiral model deals with the prevention and reduction of risks, continuously access all possible problems, define potential risks, and determine what risks are important and how to deal with them. Finally we have discussed some risk estimation method for software product development. Again we can conclude "the findings of this paper can be a project management tool to assess and tone down the events that might adversely affect a project, thereby increasing the possibility of success".
  • Article
    In the requirement analysis phase, good quality requirements are needed to develop the foundation of good quality software. This paper proposes quality attributes from the SQA activities in requirement phase from the end users' perspective in an e-commerce application. By analyzing SQA activities in requirement engineering process, we found out five quality attributes that most affect customer's satisfaction: functionality, security, usability, reliability and efficiency. This paper describes the quality attributes that are gained from the requirements elicitation, requirements documentation, requirements validation, negotiation and the requirements management planning activities. The results show that functionality, security, usability, reliability and efficiency affect e-commerce customers' satisfaction. Most of the online shopping websites comply with customer requirement and requirement expectations.