Volume-6 , Issue-6 , Jun - 2018 Go Back

In Software Engineering, testing a website/web application has become a basic necessity for proper working of the software. Testing can be performed manually as well as with the help of automation testing tools to meet functional and non-functional requirements. Performance testing is one of the non-functional testing in Software Engineering which is necessary to check the high scalability, speed and stability of the system and to discover the number of concurrent users that can access the application without a heavy degradation of the user experience, to find application’s behavior under load, to check the time it takes to respond and understand if your website loads in a decent amount of time. The comparative analysis of testing tools is done to provide tester an easy selection of tool thereby saving the time in checking and installing each and every tool. Comparison is done based on installation process, complexity of usage, throughput generated of website, response time, report generation techniques, external library requirements, etc.

Testing tools, Types of tools

[1] Performance testing: Analyzing differences of response time between performance testing tools by Muhammad Dhiauddin MohamedSuffian and F. R. Fahrurazi,:13/04/2017
[2]Comparative Analysis of Open Source Automated Software Testing Tools: Selenium, Sikuli and Watir Inderjeet Singh and Bindia Tarika ,:13/04/2017
[3]IJCA - A Survey on Cross Browser Inconsistencies in Web Application,:13/04/2017
[4]Detection of Cross Browser Inconsistency Author`s : C.P.Patidar, Meena Sharma, Varsha Sharda. ,:13/04/2017
[5]Experiences in performance testing of web applications with Unified Authentication platform using Jmeter - IEEE Xplore Document,:13/04/2017
[6]Review on Software Testing Techniques by Bindia Tarika,:13/04/2017
[7]Analysis of Automation and Manual Testing Using Software Testing Tool Priyanka Rathi Vipul Mehra,:13/04/2017
[8]A Review On Software Testing In SDlC And Testing Tools T.Amruthavalli, S.MahaLAkshmi, K.HariKrishnan,:13/04/2017
[9]INFOTEH-JAHORINA Vol. 13, March 2014.- 63 7 -Challenges in Estimating Software Testing Effort,:13/04/2017
[11]Software Design,Testing & Engineering:Andrew S.Tanenbaum,:13/04/2017
[12]Why Performance Testing: https://abstracta.us/2015/10/12/why-performance-testing-is-necessary:13/04/2017
[13]PerformanceTestingBasics:https://www.tutorialspoint.com/software_testing_dictionary/performance_testing.htm,:13/04/2017
[14]Performance Testing Types: http://www.guru99.com/performance-testing.html,:13/04/2017
[15]Apache JMeter:http://jmeter.apache.org/ ,:13/04/2017
[16]https://www.loadtestingtool.com/ ,:13/04/2017
[17]BlazeMeter: http:// https://www.blazemeter.com ,:13/04/2017
[18]LoadStorm: https://loadstorm.com/ ,:13/04/2017
[19]Pylot Tool:https://qatestingtools.com/testing-tool/pylt,:13/04/2017
[20]BlazeMeter Procing: https://www.blazemeter.com/pricing,: 13/04/2017

Chandra Prakash Patidar, "Comparative Analysis of Performance Testing Tools," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1583-1597, 2018.

Cancellable biometrics is one of the productive areas of biometrics and it guarantees the non-invertibility and revocability properties of an ideal template security scheme. In this paper, various cancellable fingerprint template security schemes, their comparison and a novel method for securing the multi-instance fingerprints have been discussed. The pseudocode of the proposed scheme is also presented. The proposed technique produces one fused secured template and this template is formed with the help of feature level fusion technique. The division method is used to secure both of the templates in order to make them non-invertible. The calculated EER and GAR for the proposed technique are 2.87% and 89.937% respectively.

Cancellable Biometrics, Many-to-one mapping, Non-Invertible transforms, Biometric Salting

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[3] O. Ogbanufe and D. J. Kim, “Comparing fingerprint-based biometrics authentication versus traditional authentication methods for e-payment,” Decis. Support Syst., vol. 106, pp. 1–14, 2018.
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[13] Z. Jin, T. S. Ong, and C. Tee, “Generating Revocable Fingerprint Template Using Polar Grid based 3- Tuple Quantization Technique,” pp. 0–3, 2011.

Sheikh Imroza Manzoor, Abdul Rouf Wani, Arvind Selwal, "Design and Development of a Multi-Instance Fingerprint Template Security Scheme," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1598-1603, 2018.

The approach of scheduling the tasks directly impacts the performance of cloud. Deadline based approach is the major area of concern in cloud computing, because deadline based tasks must be executed in time. In this paper, a novel deadline based task scheduling system model has been proposed. In proposed model, tasks have been executed on the basis of deadline constraint. Auto-scalability and resource optimization are other factors that have also been taken into consideration into the present work. The proposed model assists in reducing the makespan of the requests that is valuable to user.

Cloud computing, deadline based constraint, makespan, resource optimization, scheduling algorithm

[1] Javier Celaya, Unai Arronategui, “A Highly Scalable Decentralized Schedulerof Tasks with Deadlines”, in IEEE/ACM 12th International Conference on Grid Computing, 2011, pp. 58 – 65.
[2] Lu Guan, Ying Wang, Yanfei Li, “A Dynamic Resource Allocation Method in IaaSBased on Deadline Time”, in IEEE 14th Asia-PacificNetwork Operations and Management Symposium (APNOMS), 2012, pp. 1 – 4.
[3] Guan Le, Ke Xu, Junde Song, “Dynamic Resource Provisioning and Scheduling with Deadline Constraint in Elastic Cloud”, in IEEE International Conference on Service Science, 2013, pp. 113-117.
[4] Zhao-Rong Lai, Che-Wei Chang, Xue Liu, Tei-Wei Kuo, Pi-Cheng Hsiu, “Deadline-Aware Load Balancing for MapReduce”, in IEEE 20th Internati-onal Conference on Embedded and Real-Time Computing Systems & Applications, 2014, pp. 1-10.
[5] Chien-Hung Chen, Jenn-Wei Lin, Sy-Yen Kuo, “Deadline-Constrained MapReduce Scheduling Based on Graph Modelling”, in IEEE International Conference on Cloud Computing, 2014, pp. 416-423.
[6] Maurice Khabbaz, Chadi Assi, “Modelling and Analysis of A Novel Deadline-Aware Scheduling Scheme for Cloud Computing Data Centers”,in IEEE Transactions on Cloud Computing, 2015, pp. 15.
[7] Dinesh Komarasamy, Vijayalakshmi Muthu-swamy, “Adaptive Deadline based Dependent Job Scheduling algorithm in Cloud Computing”, in IEEE Seventh International Conference on Advanced Computing, 2015, pp. 1-5.
[8] R Krishnam Raju Indukuri, Suresh Varma Penmasta, Dr. M V Rama Sundari, Dr. G. Jose Moses, “Performance Evaluation of Deadline Aware Multi Stage Schedulingin Cloud Computing”, in IEEE 6th International Conference on Advanced Computing, 2016, pp. 229-234.
[9] Vinay Kand S M Dilip Kumar, “Auto-scaling for Deadline Constrained Scientific Workflows in Cloud Environment”, in IEEE Annual India Conference (INDICON), 2016, pp. 1-6.
[10] Longkun Guo, Hong Shen, “Efficient Approxi-mation Algorithms for the Bounded Flexible Schedu-ling Problem in Clouds”, in IEEE Transactions on Parallel and Distributed Systems, 2017, pp. 1-12.
[11] Jieun Choi, Younsun Ahn, Seoyoung Kim, Yoonhee Kim1, Jaeyoung Choi, “VM Auto-scaling Methods for High Throughput Computing on Hybrid Infrastructure”, in cluster computing, springer, 2015, pp. 1063–1073.

Monika, Pardeep Kumar, Sanjay Tyagi, "VM Optimization using Deadline Based Task Scheduling in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1604-1608, 2018.

Research on wireless sensor networks has recently received much attention as they offer an advantage of monitoring various kinds of environment by sensing physical phenomenon. Prolonged network lifetime, scalability, and load balancing are important requirement for many sensor network applications. Clustering sensor nodes is an effective technique for achieving these goals. In this paper, we evaluate and compare state-of-the-art clustering protocols, i.e., LEACH,LEACH C and HEED. Finally, these clustering approaches were compared based on a few metrics such as convergence rate, cluster stability, cluster overlapping, location-awareness and support for node mobility. Finally, we summarize and conclude the paper with some future directions

Cluster Head Selection, HEED and LEACH Protocol ,Wireless Sensor Networks (WSN)

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Suresh Kumar, Kalpana Midha, "Review of Comparisons on Secure & Energy Efficient Clustering Protocols in Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1609-1614, 2018.