=Paper=
{{Paper
|id=Vol-1216/paper7
|storemode=property
|title=Evaluation of Software Product Quality Attributes and Environmental Attributes using ANP Decision Framework
|pdfUrl=https://ceur-ws.org/Vol-1216/paper7.pdf
|volume=Vol-1216
|dblpUrl=https://dblp.org/rec/conf/re/KocakAB14
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==Evaluation of Software Product Quality Attributes and Environmental Attributes using ANP Decision Framework==
https://ceur-ws.org/Vol-1216/paper7.pdf
Evaluation of Software Product Quality Attributes
and Environmental Attributes using ANP Decision
Framework
Sedef Akınlı Koçak Gülfem Işıklar Alptekin Ayşe Başar Bener
Data Science Laboratory Computer Engineering Data Science Laboratory
Environmental Applied Science Galatasaray University Mechanical and Industrial
Ryerson University Istanbul, Turkey Engineering, Ryerson University
Toronto, Canada gisiklar@gsu.edu.tr Toronto, Canada
Sedef.akinlikocak@ryerson.ca Ayse.bener@ryerson.ca
requirements (green requirements) to the system. Those green
Abstract—Green software development is a relatively new requirements generally create conflicts on both functional
research area within green IT. Software development industry requirements and quality attributes. Interdependencies
has started getting pressure from regulators to consider green between environmental concerns and quality attributes are
software development. As a result, green attributes of software typically hidden compared to the dependencies between
products are gaining importance as quality attributes. In this
functional requirements and quality attributes. Hence, in order
study, we evaluate environmental sustainability and software
quality criteria using a well-known multi criteria decision making to develop environmentally sustainable software products, we
approach: Analytical Network Process (ANP). The aim is to need to address these conflicts and prioritize concerns and
determine the relationships among quality and environmental requirements. A multi-criteria decision making (MCDM)
attributes and relative priorities of attributes. The results are approach is suitable for this aim since it incorporates multiple
presented as a guide for green software developers. The priority objectives for prioritizing conflicts [6]. MCDM approaches are
weights of attributes may be used to analyze trade-off between known for their flexibility in handling complex information,
conflicting product quality and environmental requirements. and their ability to construct values during priority-setting in
Index Terms— Green Software, Software Quality, trade-off analysis.
Environmental Sustainability, Analytic Network Process
In our earlier work [7], we built a framework using ANP
which is a widely used MCDM approach to prioritize quality
I. INTRODUCTION and environmental criteria in a general sense. The work in this
In software development, comprehensive specification and paper extends our previous framework by defining sub-criteria
evaluation of software product quality is a key factor in of quality and environmental criteria. The main contribution of
ensuring desired level of quality. A desired level of quality for this work is that it expands the framework that enables decision
software may be achieved by defining appropriate quality makers determining the importance of each requirement when
characteristics, taking into account the purpose of usage of the deciding on the technical specifications of the software. A
software product. trade-off analysis may also be applied during requirement
Similarly, green information and communication analysis step where making decisions regarding sustainability
technologies (ICT) and green software have become important affects both software product development and software system
research areas that aim to achieve environmentally sustainable itself.
computing systems. Designing green and sustainable software Another key consideration for delivering business value with
that also satisfies customer needs in new software products environmental responsiveness is deciding what requirements to
and/ or legacy software are complex tasks. Developing green develop and how to develop. At this point, trade-offs among
software necessitates identification of the development different requirements and stakeholders emerge. Trade-offs are
requirements as well as quality metrics. There have been inevitable and need to be balance wisely. Our research seeks to
various software quality models in the literature. These models analyze the various trade-off possibilities.
propose sets of metrics to evaluate both product and process This paper is organized as follows: Section II summarizes
quality [1, 2, 3, 4]. the concepts of software quality models and evaluations,
Quality requirements (aka quality attributes) and customer requirement prioritization, software quality and environmental
requirements (aka functional requirements) sometimes may attributes. This section also presents the related literature. In
conflict. In these cases, practitioners make trade-off decisions Section III, the methodology used in the evaluation framework
or prioritization to solve the conflict. However, in most cases is briefly explained. The analysis, the results and the discussion
functional requirements and quality attributes depend on each with trade-off analysis are presented in Section IV. Section V
other, thereby complicating the trade-off decisions [5]. In gives conclusions and future work.
addition, sustainability concerns induce environmental
II. RELATED WORK AND BACKGROUND Requirement prioritization is a process of managing the
Researchers have been studying sustainability of software relative importance and determination of different requirements
from different perspectives: hardware energy efficiency in within the limited resources. It is a challenging activity to find
terms of power consumption; optimization of algorithms and / the right balance among competing quality requirements. In
or software architecture; effective and efficient usage of this paper, ANP, a well-known MCDM approach, is used to
software engineering practices and tools as defined in ISO/IEC prioritize the criteria. It improves the quality of decisions by
system and software engineering standard series [8, 9, 10]. providing information on trade-offs, and it increases confidence
in decisions and provide insights into the criteria and the
A. Software Quality Models alternatives. In decision support systems studies, Analytical
Software quality is defined as a “set of features and Hierarchy Process (AHP), hybrid ANP/AHP method, fuzzy
characteristics of product or service that bears on its ability to AHP [11], AHP-goal model, binary search tree creation,
satisfy the stated or implied needs” [8]. Software development greedy-type algorithms [12] are the most commonly used
projects need to be completed within certain constraints such as methods. Most of the above mentioned tools and methods
time, budget, and resources. Therefore, managers need to make recognize the AHP as a reference method. However, AHP is
trade-off decisions to determine how much functionality/ or difficult to use with large sets of requirements. In order to solve
which features will be included in each release. In software this problem, a case-based ranking method [13] is introduced
engineering literature, various software quality models have for decision support system based on a machine learning
been proposed. Some of the most standard and well-known approach. Another technique is called quality function
quality models are McCall’s model [1], Boehm’s model [2], deployment (QFD) which helps the organization from the early
Bowen’s model [3], Dromey’s model [4], ISO/EC 9126-1 [8], stages of production process (i.e. designing phase) to reach the
and ISO/EC 25010 [10] models. The considered quality criteria customer’s satisfaction with proper technical requirements.
in each model is summarized in Table 1. The common criteria QFD has been adopted as a software quality function
found in the majority of these models are: efficiency, deployment (SQFD) [14] and utilized to plan and design new
reliability, usability, functionality, maintainability, and software or improved software. Although the number of
portability. research on applying SQFD to software industry is limited
compared to manufacturing industry, the use of QFD in
TABLE I. QUALITY MODELS IN RELATED LITERATURE software engineering has gained more attention in recent years.
Criteria/Goals
McCall Boehm Bowen Dromey ISO-9126 ISO-25010 C. Software Quality Attributes and Sustainability
[1] [2] [3] [4] [8] [10]
Maintainability + + + + + Various systems (e.g. energy systems, management
Flexibility + + systems, and computer systems) take sustainability as an
Testability + + objective for reaching a high quality level. Penzenstadler,
Correctness + + Mahaux, and Salinesi [15] stated that current discussions on the
Efficiency + + + + + sustainability requirements foster on how to define, measure
Reliability + + + + + + and assess sustainability as quality attribute of software. A first
Integrity + + quality model for green and sustainable software was
Usability + + + + + developed by Kern, Dick, Naumann, Guldner, and Johann [16].
Portability + + + + + + It refers to quality factors from ISO /IEC 25000, and it is based
Reusability + + + on the directly and indirectly related criteria of software. It
Interoperability + + gives an overview of potential aspects which may be taken as
Understandability + sustainability criteria and metrics for software products. Calero
Functionality + + + and Bertoa [17] considered sustainability as a new factor that
Modifiability + affects software product and process quality. In their work, a
Performance + new quality model (ISO 2510+S) is presented based on
Supportability ISO/25010. The authors differentiated the quality factors in
Security + respect to the sustainability impact and described related and
Compatibility + unrelated sub-characteristics. All of these studies discuss the
relation of the software quality with the sustainability in a
B. Decision Making in Software Requirements general manner. They point out that the product, as well as the
Software development is a design intensive process. Every quality in use, needs to be considered when evaluating the
person involved in this process has to make a number of sustainability of the software. So far, to the best of our
decisions, each of them with several possible choices. Most of knowledge, there is no empirical study that uses the quality
the time, decisions are made according to personal knowledge attributes with sustainability attributes.
and experience. But, as software development projects get D. Environmental Criteria and Green Metrics
complex and diverse, decision making becomes a group
activity where people need to use decision tools and techniques With the purpose of clarifying different ways that ICT can
to communicate and coordinate their decisions. contribute to the reduction of CO2 emissions and sustainable
development, three levels of effects of ICT have been
identified [18, 19]: direct, indirect and systematic. In terms of TABLE II. GREEN METRICS CLASSIFICATION
identifying environmental criteria, in this paper, we are IT resource
interested in direct effects. Direct effects refer to those caused Lifecycle Energy Impact Organizational
usage
by software product, i.e. the resource consumption when using - CPU usage - Lifecycle cost - Consumables - Laws and
the applications. Environmental performance of the software - Memory - Process - System regulations
systems can be evaluated with environmental criteria and usage engineering power/energy - Resource efforts
green metrics. They are also necessary for practitioners to - Storage - Quality of usage - Greenhouse gas
usage service - Greenhouse credits
develop, deploy and control the system sustainability. Here, we - I/O usage gas (e.g. CO2 - Green solutions
address this point by analyzing green metrics as a criteria set. emissions)
Those criteria may be used in setting up environmental
objectives and evaluating the sustainability of software while GPIs measure the system greenness with respect to
products. In order to refine such a group of criteria, we energy consumption. Their approach focuses on GPIs, in
reviewed recent green metrics studies. Kansal and Zhao [20] particular, usage and energy related indicators. Although most
presented an energy profiling metric for applications at design of them may represent different system layers, their proposed
phase. The energy consumed by an application is divided into framework turns indicators through normalization functions
two parts: the performance required by the application and the such that they can be compared and aggregated within
system resources used. Their application energy profiling meaningful clusters.
approach considers how energy consumption is spanned across In our study, we focus on the criteria that are directly related
the involved resources such as CPU, disk, memory and to the quality and environmental attributes. Hence, we
network interfaces during different states. Lago and Jansen [21] aggregate two sub-criteria sets for our study using Kipp, Jiang,
focused on process, service and people aspects and proposed a and Fugini [24] and Ferreira, Pernici, and Plebani [25]
framework which makes environmental strategies and green classifications: resource consumption that have the higher
indicators available as-a-service. Their adaptation strategies are influence over energy consumption and energy impact which is
based on one single metric for each. The metrics are not closely related to the resource usage. The cluster criteria, sub-
analyzed together and their dependencies are not considered. criteria and their indicators are listed in Section IV.B.
However, in our proposed approach, we analyze priorities
based on the dependencies of metrics using ANP approach. III. ANALYTIC NETWORK PROCESS (ANP)
In a recent systematic literature review, Bozzelli, Gu, and The ANP generalizes a widely used multi-criteria decision
Lago [22] identified that most of the measures were defined to making tool, the AHP, by replacing hierarchies with networks.
measure energy consumption, either directly or as a The AHP is a well-known technique that decomposes a
performance indicator. Following that study, Agarwal, Nath, problem into several levels in such a way that they form a
and Chowdhur [23] indicated that software performance hierarchy [6]. ANP uses a network structure with
depends on the basic computing resources like the CPU and the interrelationships among the decision levels and attributes [26].
memory. Software is required for faster CPUs and increased A relative ratio scale of each element measurement is derived
memory. In this situation, sustainability is affected from the from pairwise comparisons of the elements in a level of the
increasing resource consumption. Therefore, we concluded that hierarchy with respect to an element of the preceding level.
we need to evaluate software products sustainability in terms of ANP provides a general approach to deal with decisions
resource usage. considering the dependence of higher level elements to lower
According to Kipp, Jiang, and Fugini [24], green metrics are level elements and the dependence of the elements within a
the key drivers to identify the “greenness” of an IT application level. In general the ANP consists of three stages:
and to indicate the energy consumption, energy efficiency, and 1. Defining the goal, criteria, sub-criteria,
energy saving possibilities. They classify four clusters of 2. Defining the interdependencies and the network, and
metrics: a) IT resource usage that is related to energy 3. Calculation of the priorities of the elements [26].
consumption, b) lifecycle metrics that monitor energy The relative importance value according to pairwise
consumption and metrics that develop energy aware comparisons between criteria and sub-criteria are performed
indicators, c) energy impact metrics that are related to the using a scale of 1–9, where a score of 1 indicates equal
lifecycle impact on the environment (i.e. the electricity, the importance between the two elements and 9 represents the
power supply, the consumed material, and the CO2 emissions), extreme importance of one element compared to the other one.
and d) organizational metrics that consider the assessment of The relations are calculated as in Equation.1
additional costs due to energy-related initiatives. The
following table (Table II) summarizes the main categories of
metrics used for each cluster [24]. a ji 1 (1)
aij
Using the same classification, Ferreira, Pernici, and Plebani
[25] recently have presented two different types of indicators
for different system layers: Key Performance Indicators where aij denotes the importance of the ith element compared to
(KPIs) and Green Performance Indicators (GPIs). KPIs the jth element, and aii=1 are preserved in the pairwise
measure system attributes that are based on quality models, comparison matrix to improve the consistency of the
judgments. The ANP requires that the consistencies of pairwise
comparison matrices are checked to prove the consistency of Each step is presented in more detail in the following sub-
comparisons. A zero value would indicate perfect consistency sections.
whereas larger values indicating increasing levels of
B. Determination of Criteria and Sub-Criteria
inconsistency. Saaty [26] has indicated that the consistency
should be about 10% or less to be acceptable. If it exceeds the Quality criteria (functionality, usability, efficiency,
10% level, value judgment may need to be revised. reliability) and their related sub-criteria were adopted from
After calculating the importance aij, the composite weights common software product criteria in the literature.
are extracted considering the interrelationships between criteria Environmental criteria were adopted from IT resource usage
and sub-criteria [27]. We utilized ANP approach to define and energy impact categories (see sub-section II.D) [24].
priority weights of the quality and environmental criteria and Table III describes the criteria and the selected sub-criteria.
their sub-criteria. This approach best suits in our aim since it Figure 1 depicts our decision framework. Here metrics and
identifies the interdependencies between criteria and their sub- indicators are used interchangeably.
criteria in the network structure. For environmental dimension of sustainability, the
classification is important since all the interdependencies
IV. EVALUATION FRAMEWORK related to both quality and environment are elicited for the
trade-off analysis. Similar classification may also be used to
A. Integrating Environmental Requirements in Decision identify other aspects of sustainability dimensions that
Making correspond to quality requirements.
Users as well as other stakeholders, such as project
managers, regulators, other decision makers, recognize the TABLE III. SOFTWARE QUALITY AND ENVIRONMENTAL CRITERIA
limited resources of the system and introduce green
requirements to the development process. In addition to Software Quality Criteria (Q) [8]
quality requirements, environmental concerns possess multiple A set of attributes that bear on the
existence of a set of functions and their
viewpoints and conflicts. They must be identified and Q1 Functionality
specified properties; the functions are
eventually resolved. The value here is not only identifying the those that satisfy stated or implied needs.
conflicts but also eliciting the opportunities resulting from the A set of attributes that bear on the effort
trade-offs. Q2 Usability needed for use, and on the individual
assessment of such use, by a stated or
Resource concerns, efficiency and time constraints have implied set of users.
direct effects on software development life cycle. Integrating A set of attributes that bear on the
green requirements to the development process creates Q3 Reliability capability of software to maintain its level
diversity in understandings, source of knowledge, and of performance under stated conditions for
a stated period of time.
opportunities based on interdependencies rather than conflicts A set of attributes that bear on the
between requirements. Resource usage, for example, directly relationship between the level of
leads to changes in efficiency and reliability, which affects the Q4 Efficiency performance of the software and the
quality. While resource usage affects functionality of the amount of resources used, under stated
conditions.
product, it also brings the opportunity to save energy [28, 29].
Environmental Sustainability Criteria (E) [24, 25]
As a result, presence of green requirements in the process The energy consumption by a software
of developing the decision framework provides an opportunity relates both to the nature of the application
E1 Resource usage
to explore and construct different development outcomes and to the system configuration of the run
regarding different goals. time environment
Metrics relate to the impact of the
The objective of our proposed framework is to determine application on the environment,
the priority weights of software quality and environmental E2 Energy impact
considering electricity, power supply,
characteristics. Those weights will help to identify technical consumed material, and emissions
requirements and their correlations that will be useful in
design and development processes of green software.
Calculation of the metrics used in the framework are not C. Questionnaire and Pairwise Comparisons
detailed in this paper since the main focus is on the relative Our proposed approach has two major criteria in the first
importance and priorities of the criteria on their cluster. The level, six sub-criteria in the second level and twenty one sub-
decision process addresses the problem of selecting the most criteria in the third level. In our previous study [7], we
important criteria that a software developer needs to focus in calculated the priority weights of two criteria (quality and
the design process. The process can be divided into three main environment) and related sub-criteria (Qi, Ei).
steps: We prepared a questionnaire for experts in order to find out
1. Identifying the criteria and sub-criteria and their the weights for criteria and interactions among all the second
interdependencies. and third level indicators. Hence, we extended the framework
2. Making pairwise comparisons using questionnaire. by integrating third level indicators. The questionnaire was
3. Obtaining the interdependent priorities of the criteria filled out by the same expert group as in our first study. Experts
and sub-criteria. are from four different software development companies. They
are software experts with an experience of 4 to 20 years in the
field. Khorramshahgol and Moustakis [30] pointed out that best personal interviews. At the beginning of the questionnaire, the
suited number of experts as an interviewee is in general 5–15 meaning of the criteria and comparison of criteria were
persons. provided to advance understanding of the contents in the
In order to avoid any vagueness or hard readability that questionnaire by the experts. The geometric mean of 10
may affect the answers given by the experts, and facilitate the questionnaires is used to form the output of the pairwise
understanding about criteria and the ANP model, we conducted comparisons [27].
Fig. 1. Research Framework
D. Analysis of the interdependencies between the criteria and TABLE IV. PAIRWISE COMPARISON OF ENVIRONMENTAL (E) SUB-
CRITERIA
sub-criteria
In this step of the methodology, we applied the ANP E1 E2
E1 1 2
approach. We used the Super Decisions1 software tool in order E2 1/2 1
to calculate the results of final priorities of the sub-criteria
(Figure 1). The Super Decisions is a simple and easy-to-use TABLE V. PAIRWISE COMPARISON OF QUALITY (Q) SUB-CRITERIA
software for constructing decision models with dependence and
feedback. The consistency indexes of all these matrices were Q1 Q2 Q3 Q4
Q1 1 2 3 1/3
found to be smaller than 0.10, which is in the acceptable range. Q2 1/2 1 1/2 1/3
Small consistency indicates the consistency of experts’ Q3 1/3 2 1 1/4
evaluations. The pairwise comparisons enabled us to retrieve Q4 3 3 4 1
relative weights of criteria and sub-criteria and indicators.
TABLE VI. PAIRWISE COMPARISON OF FUNCTIONALITY (Q1) INDICATORS
E. Results and Discussion UNDER THE QUALITY
Each expert has to decide which one of the two given Q11 Q12 Q13 Q14
criteria is more important in developing a green and sustainable Q11 1 4 2 2
software product, along with its relative importance degree. Q12 1/4 1 1/2 1/2
They defined the degree of importance using the 1-9 scale. (For Q13 1/2 2 1 1/2
Q14 1/2 2 2 1
instance, 9 means that the first criterion is extremely important
compared to the second one). In order to determine aggregate
individual judgments, we took the geometric average of the TABLE VII. PAIRWISE COMPARISON OF USABILITY (Q2) INDICATORS
UNDER THE QUALITY
responses. Table IV through Table XI represent the geometric
means of pairwise comparisons between environmental Q21 Q22 Q23 Q24
Q21 1 2 1 3
sustainability criteria and quality criteria. Q22 1/2 1 2 2
Q23 1 1/2 1 3
Q24 1/3 1/2 1/3 1
1 Super Decisions (ver. 2.2.6) [Open source Software]. Available
from: http://www.superdecisions.com
TABLE VIII. PAIRWISE COMPARISON OF RELIABILITY (Q3) INDICATORS The results indicate that, among the two environmental
UNDER THE QUALITY
criteria, the resource usage criterion has the highest priority
Q31 Q32 Q33 Q34 with 66%. This shows that it has a high impact on green
Q31 1 1 2 3 software development. Among the four quality related sub-
Q32 1 1 1 1 criteria, the reliability has the highest priority with 43%,
Q33 1/2 1 1 2
Q34 1/3 1 1/2 1 followed by the efficiency and the functionality with 24%, and
23%, respectively.
TABLE IX. PAIRWISE COMPARISON OF THE EFFICIENCY (Q4) INDICATORS The reliability of a system is a measure of its ability to
UNDER THE QUALITY
provide a failure-free operation. This also means the actual
Q41 Q42 usage time of the product by user. Therefore, the reliability is
Q41 1 3 correlated with the behavior of software with respect to time,
Q42 1/3 1 and it is one of the sub-criteria of the efficiency. The
TABLE X. PAIRWISE COMPARISON OF THE RESOURCE USAGE (E1) efficiency is the second important quality criteria, with 28% of
INDICATORS UNDER THE ENVIRONMENT weight, followed by usability and functionality. Software
E11 E12 E13 E14 efficiency considers the amount of information technology
E11 1 2 2 4 resources that are used efficiently in terms of energy.
E12 1/2 1 2 3 Undoubtedly, the priority weights may depend on the software
E13 1/2 1/2 1 3
type.
E14 1/4 1/3 1/3 1
The results related to environmental criteria show that
CPU usage has the highest priority with 43% followed by I/O
TABLE XI. PAIRWISE COMPARISON OF THE ENERGY IMPACT (E2) usage and memory usage with 24% and storage usage with
INDICATORS UNDER THE ENVIRONMENT
9%. This demonstrates that the most important energy related
E21 E22 E23 resource is the CPU. The resource usage has a direct influence
E21 1 2 1
E22 1/2 1 2
on the energy consumption sub-criteria, which has the higher
E23 1 1/2 1 relative weight with 41% in the energy impact criteria
followed by CO2 emission with 33%.
These comparison values were used as input to the ANP Figure 2 shows the comparison of results of our findings
methodology, and we calculated the priorities which are in this paper with our previous study [7]. The results reveal
summarized in Table XII. As the last step of the proposed that the priority ranking of the criteria is different. In our
procedure, the final priorities of the each criteria, sub-criteria previous work, the efficiency had the highest priority (34%)
and indicators were calculated. followed by reliability (24%), functionality (23%) and
usability (19%). However, in this work, the reliability of the
TABLE XII. SUB-CITERIA AND INDICATOR LEVEL WEIGHTS product has the highest weight (43%) followed by efficiency
(28%), usability (14%) and functionality (13%). The reason is
Relative
Quality Criteria Weight Indicators
Weight
that the integration of more detailed sub-criteria and indicators
enables DMs to better analyze interdependencies. Therefore,
Availability 0.37
the final priority ranking may change if we ignore the
Fault Tolerance 0.24 interactions of detailed sub–criteria and indicators.
Reliability 0.43
Recoverability 0.23
Maturity 0.15
Accuracy 0.44
Suitability 0.22
Functionality 0.13
Security 0.22
Interoperability 0.11
Operability 0.36
Understandability 0.28
Usability 0.14
Learnability 0.26
Attractiveness 0.11
Time Behaviour 0.75
Efficiency 0.28 Resource
0.25
Utilization
Environmental Relative
Weight Indicators
Criteria Weight
CPU Usage 0.43
I/O Usage 0.24
Resource Usage 0.66 Fig. 2. Comparison of priority weight results with our previous work [7]
Memory Usage 0.24
Storage Usage 0.09
Energy Consumption 0.41
Energy Impact 0.33 CO2 Emission 0.33
Green Energy Usage 0.26
F. Assessing the Criteria and Trade-off Analysis indicators. In the process of creating the ANP decision
The sub-criteria describe the entire system with a given framework, the discussion with experts was made before the
goal and are, therefore, usable and widely understood relationships among levels and criteria are given. Therefore,
indicators of quality and green attributes of software. Criteria the ANP structure in this research is reliable and valid.
with higher influence to the system’s goal get higher weights. Using our approach and framework, decision makers may
For instance, considering the energy consumption, criteria determine requirements’ priorities and use the framework as
related to the CPU power consumption have higher importance the basis to see the potential outcomes and impacts of the
than I/O. Hence, we can conclude that CPU related events criteria. Additionally, this approach offers an opportunity to
represent higher risk for both quality and environment. properly shape software development projects from the
The set of quality and environmental criteria are highly beginning to the maintenance phase in a strategic way.
related to the time behaviour that has high impact on resource Next steps will be constructing a QFD model for new
usage, energy consumption and efficiency. The results indicate green software product development, by using the ANP
two major trade-offs: First one is between reliability, which is weights obtained from this study. We plan to integrate a goal
threatened by performance degradation, system behavior and programming technique to QFD in order to consider resource
functionality loss, and limited resource usage. The second one and budget limitations.
is between energy impact which is threatened by energy
REFERENCES
consumption and related CO2 emissions, and efficiency of the
product which is threatened by time behaviour. Although [1] J. A. McCall, P. K. Richards, and G. F. Walters. Factors in
resource usage has high priority, resource utilization does not software quality, General Electric, National Technical
Information Service, 1977.
show the same behaviour as quality criteria.
The analysis reveals that future action would be converting [2] B. W. Boehm, J. R. Brownand, and J. R. Kaspar, Characteristics
reliability, efficiency and resource usage trade-offs into the of Software Quality, TRW Series of Software Technology,
Amsterdam, North Holland, 1978.
opportunities for saving energy.
[3] T .P. Bowen, G. B. Wigle, and J. T. Tsai. “Specification of
G. Threats to Validity software quality attributes,” Technical Report RADC-TR-85-37,
The overall objective of the study is to evaluate the Rome Air Development Center, 1985.
priorities of the criteria to achieve green software. We [4] R. G. Dromey, “A model for software product
introduced a prioritization method to gain a better quality,” Software Engineering, IEEE Transaction, vol.21, no.2,
1995, pp. 146-162.
understanding of interdependencies of the quality and
environmental criteria in green software development. [5] E. E. Karsak, S. Sozer and S. E. Alptekin “Product planning in
Selection of the criteria may introduce bias. We chose the quality function deployment using a combined analytic network
process and goal programming approach,”, Computers &
quality criteria from well-known and mostly used quality
Industrial Engineering, vol. 44, 2002, pp. 171–190.
models. We selected the environmental criteria from the IT
related environmental studies. Environmental applications on [6] T. L. Saaty, The Analytic Hierarchy Process, McGraw-Hill,
IT systems are fairly new research area. Therefore, the New York, NY, 1980.
environmental criteria might be different according to the [7] S. Akınlı Koçak, G. Gonzales Calienes, G. Işıklar Alptekin, and
application area. The number of questionnaires filled by the A. Başar Bener, “Requirements prioritization framework for
experts may be another threat to validity. Since a continuous developing green and sustainable software using ANP - based
decision making,” In Proceedings of the Environmental
nature of the study, we needed to interview the same DMs in
Informatics Conference, 2013 (Hamburg, Germany, September
order to be consistent and be able to make comparisons. 2-4, 2013).
V. CONCLUSION AND FUTURE WORK [8] ISO/IEC 9126-1. Information technology - Software quality
characteristics and metrics - Part 1: Quality characteristics and
Most software development companies need to adapt their sub-characteristics, ISO, Int. Electrotechnical Commission,
development processes to meet new software business Geneva, 2001.
standards. One of these standards is the development of [9] ISO/IEC IS 12207. Systems and software engineering- Software
environmentally friendly products. In many cases, these life cycle processes, International Organization for
objectives are achieved at a cost of reducing or increasing the Standardization, Geneva, 2008.
consumption of various resources within the organization. As [10] ISO/IEC, IS 25010. Systems and software engineering - Systems
a result, the software development industry needs making and software Quality Requirements and Evaluation (SQuaRE) -
certain trade-offs between the demands of end-users and the System and software quality models, Geneva, 2011.
requirements for environmental sustainability. This work is an [11] Z Sener and E. E. Karsak. “A fuzzy regression and optimization
initial attempt to prioritize quality and environmental approach for setting target levels in software quality function
attributes, and further to build trade-off models. We made use deployment,” Software Quality Journal, 2010, vol.18, pp.323–
of ANP, a well-known MCDM technique, for dealing with 339.
conflicting criteria. [12] G., Ruhe, A., Eberlein, and D. Pfahl, “Quantitative Win-win: A
The proposed ANP framework is subject to the judgment of New Method for Decision Support in Requirements
experts for the comparison among the criteria, sub-criteria and Negotiation,” In proceeding of the 14th Int’l Conf. Software
Eng. and Knowledge Eng., (Ischia, Italy), 2002, pp.159-166.
[13] P. Avesani, C. Bazzanella, A. Perini, and A. Susi. “Supporting [22] P. Bozzelli, Q. Gu, and P. Lago. “A systematic literature review
the Requirements Prioritization Process. A Machine Learning on green software metrics,” Technical Report: VU University
Approach,” In proceeding of 16th Int’l Conf. Software Eng. and Amsterdam, 2013, Available at:
Knowledge Eng. (Maurer F. and Ruhe, G., Eds, Banff, Canada), http://www.sis.uta.fi/~pt/TIEA5_Thesis_Course/Session_10_20
2004, pp.306-311. 13_02_18/SLR_GreenMetrics.pdf
[14] S. Haag, M. Raja, and L. L. Schkade. “Quality function [23] S. Agarwal, A. Nath, and D. Chowdhury, “Sustainable
deployment usage in software development,” Communications Approaches and Good Practices in Green Software
of the ACM, 1996, vol.39, no.1, pp.41-49. Engineering,” International Journal of Research and Reviews in
[15] B. Penzenstadler, M. Mahaux, and C. Salinesi. “Requirements Computer Science (IJRRCS), 2012, vol. 3, no.1. pp. 1425-1428.
engineering for sustainable systems, In proceeding of the [24] A.Kipp, T. Jiang, and M. Fugini, “Green metrics for energy-
International Working Conference on Requirements aware IT systems,” IEEE Computer Society, 2011, vol.42,
Engineering: Foundation for Software Quality. (Essen, pp.241-248.
Germany), 2012, vol.52, pp.8-13. [25] A. M. Ferreira, B. Pernici, and P. Plebani. “Green performance
[16] E. Kern, M. Dick, S, Naumann, A, Guldner, and T. Johann, indicators aggregation through composed weighting
“Green Software and Green Software Engineering – Definitions, system,” ICT as Key Technology against Global Warming.
Measurements, and Quality Aspects,” In proceeding of the First Springer Berlin Heidelberg, 2012, pp.79-93.
International Conference on ICR for Sustainability (Zurich, [26] T. L. Saaty, Decision making with dependence and feedback:
Switzerland, 14-16 February 2013), 2013, pp.87-94. The analytic network process. RWS Publications, 1996,
[17] C. Calero and M. F. Bertoa. “25010+S: A software quality Pittsburgh, PA.
model with sustainable characteristics. Sustainability as an [27] T. L. Saaty and L. G. Vargas “Diagnosis with dependent
element of software quality,” In Proceeding of the GIBSE 2013, symptoms: Bayes theorem and the analytic hierarchy process"
(Fukuoka, Japan), 2013. Operations Research, 1998, vol.46, no.4, pp.491–502.
[18] Organization for Economic Co-operation and Development [28] S. Akınlı Koçak, A. Miransky, G. Işıklar Alptekin, A. Başar
(OECD), 2010, OECD Greener and Smarter: ICTs, the Bener, E. Cialini, “The impact of improving software
environment and climate change 2010. OECD Pub. Paris, functionality on environmental sustainability,” In: Proceedings
France. of the First International Conference on ICT for Sustainability
[19] L. M., Hilty, P. Arnfalk, L. Erdmann, J. Goodman, M. Lehmann, (ICT4S, Zurich, Switzerland), 2013, pp.104-110.
and P. A. Wäger, “The relevance of information and [29] A. Miransky, S. Akınlı Koçak, E. Cialini, and A. Başar Bener,
communication technologies for environmental sustainability–A “A. Save energy with the DB2 10.1 for Linux, UNIX, and
prospective simulation study,” Environmental Modelling & Windows data compression feature,” IBM DeveloperWoks,
Software, 2006, vol.21, no.1, pp.1618-1629. Technical Library, 2013, Available at:
[20] A. Kansal, F. Zhao, “Fine-grained energy profiling for power- http://www.ibm.com/developerworks/data/library/techarticle/dm
aware application design,” SIGMETRICS Performance -1302db2compression/
Evaluation Review, 2008, vol.36, no.2, pp.26–31.
[30] Khorramshahgol, R., and Moustakis, V. S. “Delphic hierarchy
[21] P. Lago, T. Jansen, “Creating Environmental Awareness in process (DHP): A methodology for priority setting derived from
Service Oriented Software Engineering,” In: Maximilien, E.M., the Delphi method and analytical hierarchy process,” European
Rossi, G., Yuan, S.-T., Ludwig, H., Fantinato, M. (eds.) ICSOC Journal of Operational Research, 1988, vol.37, no.3, pp.347-354.
2010. LNCS, Springer, Heidelberg, 2011, vol. 6568, pp. 181–
186.