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i-manager's Journal on Software Engineering (JSE)

Current Issue Vol. 19 Issue 1

Most Cited

Volume 7 Issue 1 July - September 2012

Article

A Survey on Testing Strategies for User Interface and its Applications

Y.V. Ashwin Karthic* , P.V.S. Sarma, S. Amarnath Babu, P. Harini, A.S.A.L.G.G. Gupta

*-*** M.Tech Student, Department of Software Engineering, St Ann's College of Engineering & Technology, JNTUK.

Professor, Department of I.T, St Ann's College of Engineering & Technology.

* Associate Professor and Head, Department of I.T, St Ann's College of Engineering & Technology.

** Professor and Head, Department of C.SE, St Ann's College of Engineering & Technology.

Y.V. Ashwin Karthick, P.V.S. Sarma, S. Amarnath Babu, P. Harini, and A.S.A.L.G.G. Gupta (2012). A Survey On Testing Strategies For User Interface And Its Applications. i-manager’s Journal on Software Engineering, 7(1),1-5. https://doi.org/10.26634/jse.7.1.1955

Abstract

User interface design is a subset of a field of study called interaction with computer. A user interface is a collection of techniques and mechanisms to interact with something. In a graphical interface, the interaction mechanism is a pointing device of some kind. Interacts with is a collection of elements referred to as objects. Event-Driven Software (EDS) can change state based on incoming events common examples are GUI and web applications. GUI Testing is to check the look and feel of the application. UI Testing is the user interface testing which is done in front of the user. There are various tools are available for automated GUI testing and web application testing. The web application is built using asp, jsp, php, servlet. Here our specific contribution is to develop a single testing tool for testing both GUI and Web Applications together. GUI is built through the java technology. Various GUI and web based testing tools are compared.

References

[1]. Bryce, R.C., Sampath, S., and Memon, A. (2011). Developing a single model and test prioritization strategies for event-driven software. IEEE Transactions on Software Engineering.

[2]. A.M. Memon and Q. Xie, “Studying the Fault- Detection Effectiveness of GUI Test Cases for Evolving Software,” IEEE Trans. Software Eng., Vol. 31, No. 10, pp. 884-896, Oct. 2005.

[3]. A.M. Memon, “Developing testing techniques for eventdriven pervasive computing applications,” in Proceedings of The OOPSLA 2004 workshop on Building Software for Pervasive Computing (BSPC 2004), Oct. 2004.

[4]. G.J. Tretmans and H. Brinksma, “TorX: Automated model-based testing,” in First European Conference on Model-Driven Software Engineering, Nuremberg, Germany, A. Hartman and K. Dussa-Ziegler, Eds., December 2003, pp. 31–43.

[5]. H. Ural and B. Yang, “A test sequence selection method for protocol testing,” IEEE Transactions on Communications, vol. 39, no. 4, pp. 514–523, 1991.

[6]. A. Marchetto and P. Tonella, “Search-based testing of Ajax web applications, “in 1st International Symposium onSearch Based Software Engineering, May 2009, pp. 3–12.

[7]. J. Offutt, S. Liu, A. Abdurazik, and P. Ammann, “Generating test data from state-based specifications,” Software Testing, Verification and Reliability, vol. 13, no. 1, pp. 25–53, 2003.

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Research Paper

Segmentation of Brain MRI Images for Tumor extraction by combining k-means clustering and Watershed algorithm

kailash sinha* , G. R. Sinha**

* Department of Electronics & Telecommunication Engineering, Shri Shankaracharya Group of Institutions, Bhilai, India.

** Professor and Associate Director, Faculty of Engineering & Technology, Shri Shankaracharya Group of Institutions, Bhilai, India.

Kailash Sinha and G.R. Sinha (2012). Segmentation of Brain MRI Images for Tumor extraction by combining k-means clustering and Watershed algorithm.i-manager’s Journal on Software Engineering, 7(1), 6-11. https://doi.org/10.26634/jse.7.1.1956

Abstract

In medical image processing, brain tumor extraction is one of the challenging tasks; since brain image are complicated and tumor can be analyzed only by expert physicians. The location of tumors in the brain is one of the factors that determine how a brain tumor effects an individual’s functioning and what symptoms the tumor causes. We have proposed a methodology in this paper that integrates k-means clustering and watershed algorithm for tumor extraction from 2D MRI (magnetic resonance imaging) images. The use of the conservative watershed algorithm for medical image analysis is pervasive because of its advantages, such as always being able to construct an entire division of the image. On the other hand, its disadvantages include over segmentation and sensitivity to false edges. The k-means clustering algorithm is used to produce a primary segmentation of the image before we apply watershed segmentation algorithm to it; which is an unsupervised learning algorithm, while watershed segmentation algorithm makes use of automated thresholding on the gradient magnitude map. It can be observed that the method can successfully detect the brain tumor size and region.

References

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[2]. V. Grau, A.U.J. Mewes, M. Alcaniz, R. Kikinis, and S.K. Warfield, (2004). “Improved watershed transform for medical image segmentation using prior information”, IEEE Transactions on Medical Imaging, 23(4):447-458.

[3]. Mohamed Lamine Toure, (2010). “Advanced Algorithm for Brain Segmentation using Fuzzy to Localize Cancer and Epilepsy Region”, International Conference on Electronics and Information Engineering (ICEIE 2010), Vol. no 2.

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[6]. M.N. Ahmed, and S.M. Yamany, (2002). “A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data”, IEEE Transactions on Medical Imaging, 21(3):193-199.

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[10]. H.P. Ng, S.H. Ong, K.W.C. Foong, and W.L. Nowinski, (2005). “An improved watershed algorithm for medical image segmentation”, Proceedings 12th International Conference on Biomedical Engineering.

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[12]. Gonzalez, R.C., R.E. Woods and S.L. Eddins, (2004). Digital Image Processing using MATLAB. 1st Edn., Pearson Education India, India, ISBN: 9788177588989: 620.

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Research Paper

Integration of Color and Texture features for Content Based Image Retrieval

kandala lakshmi aparna* , M Venu Gopala Rao**

*Assistant Professor, Department of EEE, KL University

** Professor & Head, Department of EEE, KL University.

K. Lakshmi Aparna and M. Venu Gopala Rao (2012). Integration of Color and Texture Features for Content Based Image Retrieval. i-manager’s Journal on Software Engineering, 7(1), 12-18. https://doi.org/10.26634/jse.7.1.1957

Abstract

This paper presents a new image indexing and retrieval algorithm by combining the color (RGB histogram) and texture feature (local derivative patterns (LDPs). Texture feature, LDP extracts the high-order local information by encoding various distinctive spatial relationships contained in a given local region. Color features, histogram extracts the distribution of various colors in an image. The experimentation has been carried out for proving the worth of our algorithm. It is further mentioned that the database considered for experiment is Corel 1000 databased. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LDP, RGB histogram.

References

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[12]. H.A. Moghaddam and M. Saadatmand T. Gabor wavelet Correlogram Algorithm for Image Indexing and Retrieval, 18th Int. Conf. Pattern Recognition, K.N. Toosi Univ. of Technol., Tehran, Iran, (2006) 925-928.

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[15]. M. Saadatmand T., and H.A. Moghaddam, Enhanced Wavelet Correlogram Methods for Image Indexing and Retrieval, IEEE Int. Conf. Image Processing, K.N. Toosi Univ. of Technol., Tehran, Iran, (2005) 541-544.

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Research Paper

Medical Data Handling Using Cloud Computing And A Proposal for Countrywide Medical System

Samayita Bhattacharya* , Kalyani Mali**

*-** Department of Computer Science & Engineering, University of Kalyani, Kalyani, West Bengal, India.

Samayita Bhattacharya and Kalyani Mali (2012). Medical Data Handling Using Cloud Computing and A Proposal for Countrywide Medical System.i-manager’s Journal on Software Engineering, 7(1), 19-24. https://doi.org/10.26634/jse.7.1.1962

Abstract

This paper focuses on hosting and analyzing medical diagnostic data using cloud computing. Cloud computing is a general term for anything that involves delivering hosted services over the Internet. This is a project proposal for medical database system using cloud computing. The proposed database system can provide new delivery models to make healthcare more efficient and effective, and at a lower cost to technology budgets.

References

[1]. Cloud computing http://en.wikipedia.org/wiki/ Cloud_computing.

[2]. What is Cloud Computing http://search cloud computing.techtarget.com/definition/cloud -computing.

[3]. Concept: Cloud Computing http://www.wikinvest. com/concept/Cloud_Computing.

[4]. Medical record http://en.wikipedia.org/wiki/ Medical_record.

[5]. Medical diagnosis http://en.wikipedia.org/wiki/ Medical_diagnosis.

[6]. Data, Tools and Statistics-U.S. National Library of Medicine http://www.nlm.nih.gov/hsrinfo/datasites.html.

[7]. Medical Records Management: Conquering Piles of Paper. http://www.smead.com/hot-topics/medicalrecords- management-1374.asp.

[8]. Health care http://en.wikipedia.org/wiki/Health_care.

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Research Paper

Analysis on fuzzy membership functions for image segmentation using ultrafuzziness

M. Seetharama Prasad* , Kolluri Raju, C.H. Venkata Narayana*

* KL University, Vijayawada, India.

GVR & S col. of E&T, Guntur, India.

* LBR College of Engineering, Mylavaram, India.

M. Seetharama Prasad, Kolluri Raju, and C.H. Venkata Narayana (2012). Analysis on Fuzzy membership functions for Image Segmentation using Ultrafuzziness. i-manager’s Journal on Software Engineering, 7(1), 25-34. https://doi.org/10.26634/jse.7.1.1965

Abstract

In this paper, a study on fuzzy membership functions for image segmentation using ultrafuzziness is conducted. In this work, Tizhoosh membership function which is totally supervised, Huang & Wang membership function and S-function are considered. This work is an improvement of an existing work of Tizhoosh. Each membership function has its own merits and demerits in the computation process. Using fuzzy logic concepts, the problems involved in finding the minimum/maximum of a entropy criterion function are avoided. We attempt to make it clear that identifying the better membership function to assign the fuzzy membership grade to every pixel in the image, for optimum image segmentation using ultrafuzziness. For low contrast images contrast enhancement is assumed. Experimental results demonstrate a quantitative improvement with S-function over other two other functions.

References

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[9]. J.N. Kapur, P. K. Sahoo & A. K. C. Wong (1985). A new method for graylevel picture thresholding using the entropy of the histogram, Graph. Models Image Process., 29, 273–285.

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[13]. M. Seetharama Prasad, T. Divakar & L.S.S. Reddy (2011). Improved Entropic Thresholding based on GLSC histogram with varying similarity measure, International Journal of Computer Applications, 23(1), 25-32.

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i-manager's Journal on Software Engineering (JSE)

Current Issue Vol. 19 Issue 1

Most Read

Most Cited

Volume 7 Issue 1 July - September 2012

A Survey on Testing Strategies for User Interface and its Applications

Y.V. Ashwin Karthic* , P.V.S. Sarma**, S. Amarnath Babu***, P. Harini****, A.S.A.L.G.G. Gupta*****

Y.V. Ashwin Karthick, P.V.S. Sarma, S. Amarnath Babu, P. Harini, and A.S.A.L.G.G. Gupta (2012). A Survey On Testing Strategies For User Interface And Its Applications. i-manager’s Journal on Software Engineering, 7(1),1-5. https://doi.org/10.26634/jse.7.1.1955

Abstract

References

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Segmentation of Brain MRI Images for Tumor extraction by combining k-means clustering and Watershed algorithm

kailash sinha* , G. R. Sinha**

* Department of Electronics & Telecommunication Engineering, Shri Shankaracharya Group of Institutions, Bhilai, India. ** Professor and Associate Director, Faculty of Engineering & Technology, Shri Shankaracharya Group of Institutions, Bhilai, India.

Kailash Sinha and G.R. Sinha (2012). Segmentation of Brain MRI Images for Tumor extraction by combining k-means clustering and Watershed algorithm.i-manager’s Journal on Software Engineering, 7(1), 6-11. https://doi.org/10.26634/jse.7.1.1956

Abstract

References

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Integration of Color and Texture features for Content Based Image Retrieval

kandala lakshmi aparna* , M Venu Gopala Rao**

*Assistant Professor, Department of EEE, KL University ** Professor & Head, Department of EEE, KL University.

K. Lakshmi Aparna and M. Venu Gopala Rao (2012). Integration of Color and Texture Features for Content Based Image Retrieval. i-manager’s Journal on Software Engineering, 7(1), 12-18. https://doi.org/10.26634/jse.7.1.1957

Abstract

References

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Medical Data Handling Using Cloud Computing And A Proposal for Countrywide Medical System

Samayita Bhattacharya* , Kalyani Mali**

*-** Department of Computer Science & Engineering, University of Kalyani, Kalyani, West Bengal, India.

Samayita Bhattacharya and Kalyani Mali (2012). Medical Data Handling Using Cloud Computing and A Proposal for Countrywide Medical System.i-manager’s Journal on Software Engineering, 7(1), 19-24. https://doi.org/10.26634/jse.7.1.1962

Abstract

References

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Analysis on fuzzy membership functions for image segmentation using ultrafuzziness

M. Seetharama Prasad* , Kolluri Raju**, C.H. Venkata Narayana***

* KL University, Vijayawada, India. ** GVR & S col. of E&T, Guntur, India. *** LBR College of Engineering, Mylavaram, India.

M. Seetharama Prasad, Kolluri Raju, and C.H. Venkata Narayana (2012). Analysis on Fuzzy membership functions for Image Segmentation using Ultrafuzziness. i-manager’s Journal on Software Engineering, 7(1), 25-34. https://doi.org/10.26634/jse.7.1.1965

Abstract

References

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Y.V. Ashwin Karthic* , P.V.S. Sarma, S. Amarnath Babu, P. Harini, A.S.A.L.G.G. Gupta

* Department of Electronics & Telecommunication Engineering, Shri Shankaracharya Group of Institutions, Bhilai, India.

** Professor and Associate Director, Faculty of Engineering & Technology, Shri Shankaracharya Group of Institutions, Bhilai, India.

*Assistant Professor, Department of EEE, KL University

** Professor & Head, Department of EEE, KL University.

M. Seetharama Prasad* , Kolluri Raju, C.H. Venkata Narayana*

* KL University, Vijayawada, India.

GVR & S col. of E&T, Guntur, India.

* LBR College of Engineering, Mylavaram, India.