Decision Making Framework in e-Business Cloud Environment Using Software Metrics

International Journal on Information Theory (IJIT) Vol.5, No.3, July 2016
DOI : 10.5121/ijit.2016.5301 1
Decision Making Framework in e-Business Cloud
Environment Using Software Metrics
S.Kaliraj and T.JaisonVimalraj
Department of Computer Science Engineering,University College of engineering,BIT
Campus,Tiruchirapalli
Department of Information Technology,University College of Engineering, BIT
Campus,Tiruchirapalli
Abstract
Cloud computing technology is most important one in IT industry by enabling them to offer access to their
system and application services on payment type. As a result, more than a few enterprises with Facebook,
Microsoft, Google, and amazon have started offer to their clients. Quality software is most important one in
market competition in this paper presents a hybrid framework based on the goal/question/metric paradigm
to evaluate the quality and effectiveness of previous software goods in project, product and organizations
in a cloud computing environment. In our approach it support decision making in the area of project,
product and organization levels using Neural networks and three angular metrics i.e., project metrics,
product metrics, and organization metrics
IndexTerm
cloud computing, decision making,eBusiness, predicition,software metrics,RBF,Genetic algorithm
I.Introduction
Cloud Computing plays a significant role in e-business process via internet. Business transactions
fully based on electronically. Huge enterprises needed to access the data processing and data
accuracy due to large number of users. Software organization needs to develop quality software
in global market competition.Software plays an important role in middleware for E-Business
interoperability and the practice for enterprise application integration. With the rapid
development of cloud-computing based E-Business, the size and complexity of software products
are continuously increasing as a result of continuously increasing their functionalities,
requirements, improvements, modifications, etc.
Software metrics define, collect, and analyze the data of a measurable process and these facilitate
the understanding, evaluation, control, and improvement of the software product procedure for E-
Business. In traditional E-Business systems, to provide measurement about the schedule, work
effort, and product size among indicators, various software metric methods are proposed from
different views.
In a cloud computing environment, traditional software metric approaches have inherent
shortcomings, such as the lack of systematic software metrics, the lack of integral metrics
indicators, and the lack of a standard data collection process. Moreover, traditional approaches
depend on the key people of the project team and their experiences.

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We need to maintain the user interface till the last stage of the processing. During the software
process metrics become a Critical role to measure the development and maintenance of software
using effective and efficient data’s [1]
Several programs need to buildquality software in developing stagesit may consume large amount
of time and resources [2].
Monitoring is most important one to improve the software process and product utilization it tries to
focus on (G/Q/M) approach. This approach identified the focus of a problem for an organization
[5]
II. RELATED WORK
Several researches on software metrics to be conducted in past decades it leads to different
approaches and ideas. It aims to improve the software process and development. GQM are one of
the most famous measurement methods. It extends the approaches and framework [8], both
functionality and complexity of a problem represented in orthogonal manner to provide the
measurement size of a software system.
The implementation of software metrics using supporting tools and methods [9] several current
metrics able to fit the metric-driven software process [10] GQM represented the Software life
cycle deals all the type of software metrics from any stages like store, model [11] software metrics
used to estimate and predict the future projects such as techniques, risk and cost. Classification of
techniques used to predict the risk in different stages [12]
comparing the cost and project plans from earlier projects to make plan for future projects
evaluating the software performance using intelligent techniques [13] patters are most important
one to compare the similarity of a different patters with code metrics as predictors for code
analysis in future projects [14] Based on the preventive software its shows the efficient of bug
prediction [15]
Software metrics grouped in several categories they are statistical, machine learning[16],neural
networks[17][18] and decision making methods[19]this models working based on relationship
between input and output data.
Decision making approach widely used for project managersfor planning and directing software
activities NASA project data estimating the software effort using RBF [17] Neural network is one
of the best method to estimate and predict the cost of a software [20]
III. HYBIRD METRICS FRAMEWORK
In this section, a hybrid frame work based GQM used to evaluate the quality efficiency of the
previous software’s like products, projects and development for a cloud based ebusiness. Hybrid
Frame consists of several layers they are Given Below
1) Management Console Layer. It mainly used for supervising and analyzing the data to be
collected in cloud computing based framework.
2) Data Process Layer. It consists of several tasks to be validating the data to be collected
from across the cloud resources.

3
3) Metrics modeling Layer. After receiving the formatted data from the data layer to be
compute and exports to the metrics Database
4) Metrics Output Layer. It generates the relevant results based on the user request and
presents as a report to the project Manager
A. Management Console for Cloud
It defines the rule for collecting different data from cloud Resources includes the access
management of the data and authorization functionality to control the role of permission in
Software QA. The Metrics process will reviewed and report will generated to the software
developer.
Data access management provides the data access and control the information of the metrics and
its related jobs. In hybrid framework it consists of two different data access they are automatic
and manual data access. SQAG will need to configure the URL of software repositories and the
parameter of URL.
This configuration information will be stored in metadata. Manual data access Framework will
accepts the two different types of files, they are Excel and comma-separated value (CSV) files,
basically the comma files are used to stored data.
The automatic metrics jobs configuration is based on triggered by the time. Manual metrics will
triggered anytime based on user needs. Task Monitors will measure the metric jobs and monitor
will triggered once the metric jobs is executed
B. Data processing
Cloud resource consists of several raw data across the software repositories or external files to
pre-processing from data process layer .It includes several activities they are data collection and
data validation .The analysis process includes data transferring and data formatting.
Several processing will perform using different data like testing and coding analysis, bug
trackingwith help of different data sets.Raw data will consider as a both structured and non-
structured. Every fixed period of times data collections will be triggered by timers.
Data validation is needed to check the data from the collection of process to meet the
requirements. Some data’s are wrong, invalid or error. Unqualified data will eliminate and ensure
the effectiveness of data. The data’s are structured into XML or Java Script.
Data transferring the formatted data to domain model, source data and domain data will found in
localMeta data.

4
Figure 1:Hybrid Metrics Framework
C. Domain Specific Cloud Metrics
Domain models consists of data process layer, metrics model layer and the processing of each
layer data’s are stored in metric database. There are two types of metrics model basic and
derivative metrics model.
In basic model it generate the computing from domain model directly, in derivative model it
generate through basic metrics model andVariety of resource available in cloud areaAfter metrics

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process to be completed, all the data’s are stored in metrics databases it used to review the
historical data of software product and experience of the development.
The average productivity and code duplication is the process calculating the computational
resources.Lines of code have been calculated based on metrics indicator, computed from basic
metrics lines of code.
Duplication rate is indicating based on two domain models, project lines of code and command
lines of code. Each code of lines will be measured by the software metrics to find the optimal
values.
Average productivity
ℎ
ℎ ℎ
× 6 ℎ
Code duplication rate
Reused lines of code
all lines of code
× 100%
C. Decision Making Metrics
Decision making is fully based on angular metrics and relevant metrics by their reporting tools.
The role of decision maker to analysis and generate the ideas based on the reporting .There are
different types of metric tools are available.
IV. THREE ANGULAR METRICS
In hybrid framework it consists of three different angular metrics, they are project metrics,
product metrics and organization metrics. Each metrics used to analysis and produce high quality
software. The GQM leads to define the goal of organization whether goals has been reached or
not. It helps to identified the solutions of different question

6
Figure 2: Software Metrics Process
A. Project metrics in hybrid Framework
Project metrics is used to control and manage the Situation of the project and status. This metrics
willanalysis the old projects and measure the drawbacks Andtime management of future projects.
Based onThe Reportssoftware developer andorganization willmake adecisionto adjust the plan to
produce high quality software.
B. Product Metrics in Hybrid Framework
It is used to predict the software product while itEvaluate the metrics process. Several metrics
areApplicable like testing, coding, design.
C. Organization Metrics in Hybrid Framework
Organization Metrics process is based on theMeasurement of product and its development to
improve the quality of software and regulating the process in proper manner to produce high
quality software.
V. RBF Algorithm with Genetic algorithm
Radial basis Function (RBF) model is similarly related to the neural network with approximation
of a non-linear function widely used to decision making. Many basis functions are available in
linear and Gaussian function.
Three different layersare available in RBF. Such as input layer, hidden layer and the output layer,
the hidden layer only transforms the intermediate function from input vector. Other two layers are
based on the input functions. It helps to find out the optimal solutions to make a decision.

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Hidden layer will hide the center process of information between input and output layer. While
using the genetic engineering process chromosome, neurons will connected with neural network
dramatically.
The centers and width of hidden layer neurons defined by two input patterns are coded and
moved into the chromosome. It shows the basic function of neurons coded with parameter to
provide the freedom of an optimal solution.
It helps to avoid the risk of getting stuck in weights between input and hidden layers. The
outcome will be more efficient than optimal solutions.
VI. CONCLUSION
Cloud computing has been successful Implemented in several sectors like Business, government
and industry to defined the collaborative environment and service via internet eBusiness. In
hybrid framework it extracts three different processes to evaluate the metrics for developing high
quality software.
Project metrics, product metrics, organizationlevel metrics will combinearound all levels in cloud
computing Environment. It helps to make a decision based on the metrics outcome. In future RBF
model used to enable appropriate the cloud vendor dynamically using the decision making
process.
REFERENCE
[1] S. P. Abhinandan and R. Shrisha, “A mechanism design approach to resource procurement in cloud
computing,” IEEE Trans. Comput., vol. 63, no. 1, pp. 17–30, Jan. 2014.
[2] Q. Zoubi, I. Alsmadi, and B. Abul-Huda, “Study the impact of improving source code on software
metrics,” in Proc. Int. Conf. Comput., Inf. Telecommun. Syst.,2012, pp1–5.
[3] W. Tan, Y. Sun, L. X. Li, G. Lu, and T. Wang, “A trust service-oriented scheduling model for
workflow applications in cloud computing,” IEEE Syst. J., vol. 8, no. 3, pp. 868–878, Sep. 2014.
[4] H. Tu, W. Sun, and Y. Zhang, “The research on software metrics and software complexity metrics,”
in Proc. Int. Forum Comput. Sci.–Technol. Appl., 2009, vol. 1, pp. 131–136.
[5] V. Caldiera and H.D. Rombach, “The goal question metric approach,” in Encyclopedia Software
Engineering, vol. 2. New York, NY, USA: Wiley, 1994, pp. 528–532.
[6] P. Berander and P. Jönsson, “A goal question metric based approach for efficient measurement
framework definition,” in Proc. ACM/IEEE Int. Symp.Empirical Softw. Eng., 2006, pp. 316–325.
[7] T. R. Devine, K. Goseva-Popstajanova, S. Krishnan, R. R. Lutz, and J. J. Li, “An empirical study of
pre-release software faults in an industrial product line,” in Proc. IEEE 5th Int. Conf. Softw. Testing,
Verification Validation, 2012, pp. 181–190.
[8] T. Hastings and A. Sajeev, “A vector-based approach to software size measurement and effort
estimation,”IEEE Trans. Softw. Eng., vol. 27, no. 4, pp. 337–350, Apr. 2001.
[9] R. Xu, Y. Xue, P. Nie, Y. Zhang, and D. Li, “Research on CMMI-based software process metrics,” in
Proc. 1st Int. Multi-Symp. Comput.Comput. Sci., 2006, vol. 2, pp. 391–397.
[10] L. Cheng and Z. Huang, “A flexible metric-driven framework for software process,” in Proc. 5th Int.
Joint Conf. NCM INC, IMS IDC, 2009, pp. 1198–1202.

8
[11] L. Schrettner, L. Jeno Fulop, Á. Beszédes, Á. Kiss, and T. Gyimóthy, “Software quality model and
framework with applications in industrial context,” in Proc. 16th Eur. Conf. Softw. Maintenance
Reeng.,2012 pp. 453–456
[12] Z. Xu, X. Zheng, and P. Guo, “Empirically validating software metrics for risk prediction based on
intelligent methods,” in Proc. 6th Int. Conf. Intell. Syst. Des. Appl., 2006, vol. 1, pp. 1049–1054.
[13] T. M. Khoshgoftaar, J. C. Munson, B. B. Bhattacharya, and G. D. Richardson, “Predictive modeling
techniques of software quality from software measures,” IEEE Trans. Softw. Eng., vol. 18, no. 11, pp.
979–987, Nov. 1992.
[14] L. Li and H. Leung, “Mining static code metrics for a robust prediction of software defect-
proneness,” in Proc. Int. Symp. Empirical Softw. Eng. Meas., 2011, pp. 207–214.
[15] G. Maskeri, D. Karnam, S. A. Viswanathan, and S. Padmanabhuni, “Bug prediction metrics based
decision support for preventive software maintenance,” in Proc. 19th Asia-Pac. Softw. Eng. Conf.,
2012, vol. 1, pp. 260–269.
[16] M. Shepperd and C. Schoﬁeld, “Estimating software project effort using analogies,” IEEE Trans.
Softw. Eng., vol. 23, no. 11, pp. 736–743, Nov. 1997.
[17] M.ShinandA.L.Goel,“Empiricaldatamodelinginsoftwareengineering using radial basis functions,”
IEEE Trans. Softw. Eng., vol. 26, no. 6, pp. 567–576, Jun. 2000.
[18] I. Attarzadeh and S. H. Ow, “Proposing a new software cost estimation model based on artiﬁcial
neural networks,” in Proc. 2nd Int. Conf. Comput. Eng. Technol., 2010, vol. 3, pp. V3-487–V3-491.
[19] M. Shepperd and M. Cartwright, “Predicting with sparse data,” IEEE Trans. Softw. Eng., vol. 27, no.
11, pp. 987–998, Nov. 2001.
[20] F. S. Gharehchopogh, “Neural networks application in software cost estimation: A case study,” in
Proc. Int. Symp. Innov.Intell. Syst. Appl., 2011, pp. 69–73.

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