Towards Extensible and Adaptable Methods in Computing

409 Pages·2018·13.9 MB·English

by  Shampa Chakraverty

Shampa Chakraverty · Anil Goel
Sanjay Misra Editors
Towards
Extensible and
Adaptable
Methods in
Computing
Towards Extensible and Adaptable Methods
in Computing
Shampa Chakraverty Anil Goel
(cid:129)
Sanjay Misra
Editors
Towards Extensible
and Adaptable Methods
in Computing
123
Editors
ShampaChakraverty SanjayMisra
Department ofComputer Engineering, Department ofElectrical andInformation
Netaji SubhasInstitute of Technology Engineering
University of Delhi Covenant University
NewDelhi, India Ota, Nigeria
Anil Goel
SAPCanada
Waterloo, ON,Canada
ISBN978-981-13-2347-8 ISBN978-981-13-2348-5 (eBook)
https://doi.org/10.1007/978-981-13-2348-5
LibraryofCongressControlNumber:2018952626
©SpringerNatureSingaporePteLtd.2018
Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart
of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,
recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission
orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar
methodologynowknownorhereafterdeveloped.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this
publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom
therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse.
The publisher, the authors, and the editorsare safeto assume that the adviceand informationin this
book are believed to be true and accurate at the date of publication. Neither the publisher nor the
authorsortheeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinor
for any errors or omissions that may have been made. The publisher remains neutral with regard to
jurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations.
ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSingaporePteLtd.
Theregisteredcompanyaddressis:152BeachRoad,#21-01/04GatewayEast,Singapore189721,
Singapore
Preface
Extensible and adaptable computing refers to the array of methods and techniques
thatsystematicallytacklethefuturegrowthofsystemsbyrespondingproactivelyto
change. This mandates a synergetic coordination amongst various facets of com-
puting. Agile software development is a significant driver towards this paradigm
shiftandhasindeedbecometheindustrydefactostandard.Theever-evolvingdata
is another component that requires new methods of storage, transmission and
processing. The Web which hosts almost all applications and data is potent with
latentintelligence,readytobeminedandutilizedforextendingtheapplicationsand
making them respond seamlessly to changing contexts. Innovative machine
learning tools enable us to extricate patterns of information from repositories and
adapt to changes in real time.
Our journey towards extensible and adaptable methods in computing investi-
gates various challenges in the above areas. Accordingly, this book is divided into
the following parts: agile software development, data management, machine
learning, Web intelligence and computing in education. The first four domains of
computing work together in mutually complementary ways to build automated
systemsthatscalewelltomeetthedemandsofchangingcontextandrequirements.
The fifth part highlights an important application of adaptable computing that
enables lifelong learning for all. The concept of this book emanated from the
deliberations of the international conference Towards Extensible and Adaptable
Methods in Computing that took place during March 26–28, 2018, in New Delhi.
The top research papers presented in it were selected to prepare its chapters.
The first part on agile software development addresses some of the important
challenges in developing quality software within collaborative environments such
as risk management, test case prioritization, open-source software reliability and
predicting software change proneness. The second part on data management pre-
sents elegant solutions for cost-efficient storage of data, transmitting data securely
and processing data in specific applications such as health care. The third part on
machine learning showcases innovative algorithms and applications including
portfolio optimization, disruption classification and outlier detection. The fourth
part on Web intelligence covers emerging Web applications in dynamic social
v
vi Preface
contexts including metaphor detection in natural language processing, language
identification and sentiment analysis. It also underscores Web security issues such
as fraud detection and trust and reputation systems. The fifth part on computing in
educationpresentscomputer-aidedpedagogicalmethodsthatadaptandpersonalize
to each learner, thus overcoming the constraints of traditional methods.
WewishtothankoursectioneditorsRituSibal,AnandGupta,SushmaNagpal,
Swati Aggarwal and Pinaki Chakraborty for their invaluable contribution. We are
grateful to our publishing coordinators Krati Srivastav, Antony Raj Joseph, Suvira
Srivastava, Sona Chahal and Nidhi Chandhoke for their support and encourage-
ment. It is through the dedicated efforts of all the authors and reviewers that this
book has been compiled, and we are deeply thankful to all of them.
New Delhi, India Prof. Shampa Chakraverty
Ota, Nigeria Prof. Sanjay Misra
Waterloo, Canada Dr. Anil Goel
June 2018
Contents
Part I Agile Software Development
Ritu Sibal
Risk Assessment Framework: ADRIM Process Model for Global
Software Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Chamundeswari Arumugam, Sriraghav Kameswaran
and Baskaran Kaliamourthy
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Literature Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3.1 Mitigation Strategy for Various Risks . . . . . . . . . . . . . . . . . . . . . 5
3.2 ADRIM Process Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
An Extended Test Case Prioritization Technique Using Script
and Linguistic Parameters in a Distributed Agile Environment . . . . . . . 13
Anita and Naresh Chauhan
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2 Literature Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3 Modified User Story . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4 Sprint—Story Prioritization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
5 Linguistic Parameters-Noun and Verb . . . . . . . . . . . . . . . . . . . . . . . . . 17
5.1 Agile Change Management (ACM)—User Story . . . . . . . . . . . . . 17
6 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
vii
viii Contents
AutoJet: Web Application Automation Tool . . . . . . . . . . . . . . . . . . . . . 27
Sheetika Kapoor and Kalpna Sagar
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2 Related Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.1 Context of Proposal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2 Participants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.3 The AutoJet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.4 Adaption of Autojet in Agile Methodology . . . . . . . . . . . . . . . . . 40
4 Conclusion and Future Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Prioritization of User Story Acceptance Tests in Agile Software
Development Using Meta-Heuristic Techniques and Comparative
Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Ritu Sibal, Preeti Kaur and Chayanika Sharma
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2 Basic Concepts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.1 Given-When-Then Format. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.2 Overview of Meta-Heuristic Algorithms. . . . . . . . . . . . . . . . . . . . 45
3 Proposed Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.1 Case Study: Bank Management System . . . . . . . . . . . . . . . . . . . . 49
3.2 User Story: Account holder withdraws cash . . . . . . . . . . . . . . . . . 50
3.3 Acceptance Criteria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4 Conclusion and Future Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
Software Reliability Assessment Using Deep Learning Technique . . . . . 57
Suyash Shukla, Ranjan Kumar Behera, Sanjay Misra
and Santanu Kumar Rath
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
2 Related Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3 Bug Tracking System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4 Identification of Critical Fault Based on Neural Network . . . . . . . . . . . 59
5 Identification of Critical Fault Based on Deep Learning . . . . . . . . . . . . 61
5.1 Analysis of Dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.2 Creation of the Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.3 Compilation of the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.4 Fitting the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.5 Evaluation of the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
6 Dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
7 Analysis of Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
8 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
Contents ix
Empirical Validation of OO Metrics and Machine Learning
Algorithms for Software Change Proneness Prediction . . . . . . . . . . . . . 69
Anushree Agrawal and Rakesh Kumar Singh
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
2 Related Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.1 Independent Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.2 Dependent Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.3 Prediction Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.4 Validation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4 Empirical Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.1 Descriptive Statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5 Result Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.1 Univariate LR Analysis Results. . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.2 Model Evaluation Using ROC Curve. . . . . . . . . . . . . . . . . . . . . . 80
5.3 Friedman Test Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
Part II Data Management
Anand Gupta
Extending Database Cache Using SSDs . . . . . . . . . . . . . . . . . . . . . . . . . 89
Prateek Agarwal and Vaibhav Nalawade
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
2 Configuring NV Cache . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
3 NV Cache Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
3.1 Buffer Cache Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
3.2 Data Layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
3.3 Page Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
3.4 Page Writes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
3.5 Lazy Cleaner Task . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
3.6 Page Eviction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4 Performance Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.1 Benchmark. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.2 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5 Related Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
6 Enhancements and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
7 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
x Contents
Cloud-BasedHealthcareMonitoringSystemUsingStormandKafka .... 99
N. Sudhakar Yadav, B. Eswara Reddy and K. G. Srinivasa
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
2 Related Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
3 Proposed System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.1 Web Portal. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
3.2 Data Adapter and Integrator . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
3.3 Apache Kafka. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
3.4 Storm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4 Experiment and Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
Honeynet Data Analysis and Distributed SSH Brute-Force Attacks. . . . 107
Gokul Kannan Sadasivam, Chittaranjan Hota and Bhojan Anand
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
2 Related Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
3 Honeynet Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
4 General Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
4.1 Source of the Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
5 Secure Shell (SSH) Traffic Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . 113
6 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
Efficient Data Transmission in WSN: Techniques and Future
Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
Nishi Gupta, Shikha Gupta and Satbir Jain
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
2 Routing in WSN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
2.1 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
2.2 Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
3 Routing Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
4 Future Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
AStudyofEpidemic SpreadingandRumor Spreadingover Complex
Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
Prem Kumar, Puneet Verma and Anurag Singh
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
1.1 Random Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
1.2 Scale-free Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
1.3 Properties. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
1.4 Characteristics of Some Real Network Data Available
and Widely Used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133