i-manager Publications

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

Current Issue Vol. 18 Issue 2

Most Cited

Volume 8 Issue 4 April - June 2014

Research Paper

OpenC: Extending C with Computational Reflection

Songqing Yue*

Department of Mathematics and Computer Science, University of Central Missouri, Warrensburg, USA.

Yue ,S.(2014). OpenC: Extending C with Computational Reflection. i-manager’s Journal on Software Engineering, 8(4), 1-8. https://doi.org/10.26634/jse.8.4.3046

Abstract

Computational Reflection has shown much promise for improving the quality of software by providing programming language techniques to address issues of modularity, reusability, maintainability, and extensibility. The Meta-Object Protocol (MOP) is a powerful tool to provide the capability of computational reflection by means of object oriented and reflective techniques to organize a meta-level architecture. It provides a set of interfaces for developers to access the underlying implementation of programs in order to automate the source-to-source program translations. In this paper, the author describes how to bring the power of computational reflection to C through a MOP, named OpenC, which offers a framework to build arbitrary source-to-source program transformation libraries for large software systems written in C. The design focus of OpenC is to automate program transformations in a straightforward and transparent way through techniques of code generation, so that client users only need to add a simple annotation to their code to be manipulated, while removing the need to know the details on how the transformations are performed. The paper provides a general motivation for using reflection and explains briefly the design and implementation of the OpenC framework. In addition, this paper will show an example, how OpenC can be used to build a simple profiling library that can be employed to analyse the distribution of execution time among all functions in a project by recording the amount of time spent on executing each function.

References

[1]. Smith, B. C. (1982). Reflection and semantics in a procedural language(Doctoral dissertation, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science).

[2]. Feferman, S. (1962). Transfinite recursive progressions of axiomatic theories. The Journal of Symbolic Logic, Vol.27(03), pp.259-316.

[3]. Maes, P. (1987, December). Concepts and experiments in computational reflection. In ACM Sigplan Notices, Vol. 22, No. 12, pp. 147-155.

[4]. Bobrow, D. G., Gabriel, R. ., & White, J. L. (1993). Clos in context-the shape of the design space. Object Oriented Programming: The CLOS Perspective, pp.29- 61.

[5] Demers, F. N., & Malenfant, J. (1995, August). Reflection in logic, functional and object-oriented programming: a short comparative study. In Proceedings of the IJCAI, Vol. 95, pp. 29-38.

[6]. Maes, P. (1988). Computational reflection. The Knowledge Engineering Review, Vol.3(01), pp.1-19.

[7]. Kiczales, G. (1991). The art of the metaobject protocol. MIT press.

[8]. DeMichiel, L. G., & Gabriel, R. P. (1987, January). The common lisp object system: An overview. In ECOOP’87 European Conference on Object-Oriented Programming (pp. 151-170). Springer Berlin Heidelberg.

[9]. Chiba, S. (1995, October). A metaobject protocol for C++. In ACM Sigplan Notices, Vol. 30, No. 10, pp. 285- 299.

[10]. Tatsubori, M., Chiba, S., Killijian, M. O., & Itano, K. (2000). OpenJava: A class-based macro system for Java.

[11]. http://rosecompiler.org/

[12]. http://www.edg.com/index.php?location=c_ frontend

[13]. Kiczales, G., Ashley, J. M., Rodriguez, L., Vahdat, A., & Bobrow, D. G. (1993). Metaobject protocols: Why we want them and what else they can do. Object-Oriented Programming: The CLOS Perspective, pp.101-118.

[14]. Scott, M. L. (2000). Programming language pragmatics. Morgan Kaufmann.

[15]. Myers, G. J., Sandler, C., & Badgett, T. (2011). The art of software testing. John Wiley & Sons.

Full Article (HTML)

Pdf

Research Paper

Detecting Hidden Data Using Higher Order Empirical Transition Matrix

Swagota Bera* , Monisha Sharma**

* Associate Professor, Department of Electronics and Telecommunication, Shri Shankaracharya Institute of Engineering & Technology, Durg.

** Professor, Department of Electronics and Telecommunication, Shri Shankaracharya College of Engineering and Technology, Bhilai.

Bera,S., and Sharma,M. (2014). Detecting Hidden Data Using Higher Order Empirical Transition Matrix. i-manager’s Journal on Software Engineering, 8(4), 9-16. https://doi.org/10.26634/jse.8.4.3047

Abstract

Steganalysis is the art of detecting the presence of hidden data in any common data. Universal steganalysis is general class of steganalysis techniques which can be implemented with any steganographic embedding algorithm, even an unknown algorithm. In this paper, the detection technique is based on the fact that there occurs variation in the feature vectors in an image before and after hiding. The whole image is divided in blocks of 8x8. There exists interdependency among the pixel values within the image blocks known as intrablock dependency. The statistical feature is calculated on the basis of the transition in the pixel value. The features used here is the transition probability matrix calculated by using the markov statistical process. If the transition probability matrix is found out by considering the transition in the pixel value of second pixel w.r.t to first one and so on, then it is known as one step transition probability matrix. If the transition probability matrix is found out by considering the transition in the pixel value of third pixel w.r.t to first and second one simultaneously and so on then it is known as two step transition probability matrix. Further the values of the quantized DCT pixel is restricted in -4 to 4 values which is known as thresholding. This way a feature set is calculated with optimum dimension for the classification between the cover image and the stego image.

References

[1]. Kelly, J. (2001). Terror groups hide behind web encryption, USA Today, 2 May 2001, Retrieved from http:// www.usatoday. com/life/cyber/tech/2001- 02- 05- binladen.htm.

[2]. Anonymous, What is steganography?. Retrieved from www.tech-faq.com/steganography.html.

[3]. Maile, A., Zhanna, L., Ana, S., & Yasemin, Y. (2002, April). Image Encryption Using LSB/MSB.Term Project, CpE- 462.

[4]. Anonymus. Miscellaneous Steganographic. Retrived from scien.stanford.edu/class/psyh221/project/05/ vvikram/stegomisc.htm.

[5]. Bernd. J. (II Ed.). (1993). Concepts, Algorithms, an Scientific Applications. Digital Image processing. Springer-Verlag.

[6]. Wolfgang, R.B., Delp, E.J. (1996). A watermark for digital Image. (16-19 Sep1996). International Conference on Images Processing, Lausanne, Switzerland. IEEE, pp.219–222.

[7]. Kharrazi, M., Sencar, T.H., & Memon, N., (2002). Image Steganography: Concepts and Practices. Department of Computer and Information Science Polytechnic University, Brooklyn, NY 11201, USA.

[8]. Bera, S. & Sharma, M. (2007). Survey on Steganographic Techniques & Steganalysis. (2007, 28-29 Oct). National Conference in Advances in electronics & Telecommunication Technologia. vision- 2020.

[9]. Bera, S. & Sharma, M. Steganalysis of Real Time Image by Statistical Attacks.(2010). International Journal of Engineering Science and Technology, Vol. 2(9), pp.4397- 4406.

[10]. Monisha Sharma and Swagota Bera (2011). Steganalysis of the Image by Visual & Statistical Attacks, imanager’s Journal on Electronics Engineering. 1(2) Dec- Feb, 2011 Print ISSN: 2229-7286, E-ISSN: 2249-0760, pp,48-55.

[11]. Bera, S. & Sharma, M. (2012). A Review on blind still image Steganalysis Techniques Using Features Extraction and Pattern Classification Method. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), ISSN:2231-3117(Online);2231- 3605(print). Vol.2(3), pp.117-135.

[12]. Shi. Y. Q., Chen. C. and Chen. W. (2007). A markov process based approach to effective attacking JPEG steganography. Lecture Notes in Computer Science, Information Hiding, Springer. 4437: pp.249–264.

[13]. Kumar M., (2012). Steganography and Steganalysis of Joint Picture Expert Group (JPEG) Images. Ph.D. Thesis, University of Florida.

[14]. Andrews Ker ,K. (2001). Retrieved from http://www.outguess.org

[15]. Latham, A. (2001). Retrived from http://wwwrn.inf.tudresden. de/~westfeld/f5.html.

[16]. A. Latham. (1999, August). Jp hide&seek. [Online]. Available: http://linux01.gwdg.de/latham/stego.html

Full Article (HTML)

Pdf

Research Paper

Clustered Micro Calcification Detection Based On the Texture Feature Extraction Using Adaptive Bilateral Filter Of Mathematical Morphology

B. Sridhar* , K.V.V.S. Reddy, A.M. Prasad*

* Faculty, Department of ECE, Lendl Institute of Engineering and Technology, Vizianagaram, India.

Professor, Department of ECE, Andhra University, Visakhapatnam, India.

* Professor, Department of ECE, JNT University, Kakinada, India.

Sridhar.B., Reddy.K.V.V.S., and Prasad.A.M. (2014). Clustered Micro Calcification Detection Based On the Texture Feature Extraction Using Adaptive Bilateral Filter of Mathematical Morphology. i-manager’s Journal on Software Engineering, 8(4), 17-24. https://doi.org/10.26634/jse.8.4.3048

Abstract

A morphological adaptive bilateral filter based scheme has been proposed for textures segmentation of small field digital mammograms. Mathematical morphology offers flexible operations for extraction of micro calcifications. Unlike the traditional texture filters, adaptive bilateral filter is applied to remove the noise and improve the quality of the selection of the different texture regions. The proposed method uses an adaptive threshold selection, which can remove unwanted textures region from image. The texture regions extract the features of micro calcification and noise boundaries are smoothing again by adaptive bilateral filter. Segmentation results are displayed by inclusion of textures with input image. The proposed method is experimented on 10 micro calcification images and the quality of the method is evaluated.

References

[1]. N. Riyahi Alam, F. Younesi, M. S. Riyahi Alam (2009). 'Computer-Aided Mass Detection on Digitized Mammograms using a Novel Hybrid Segmentation System', International Journal of Biology and Biomedical Engineering, Volume 3, Issue 4, 51.

[2]. B.Sridhar, Dr. K.V.V.S.Reddy. Efficient computer aided system based on mathematical morphology and higher order partial differential equations for breast cancer detection, CNC2013 234-239.

[3]. Jelena Bozek, Kresimir Delac, Mislav Grgic (2008). Computer-Aided Detection and Diagnosis of Breast Abnormalities in Digital Mammography, 50^th International Symposium ELMAR-2008, 10-12, Zadar, Croatia.

[4]. T. Matsubaraa,, T. Ichikawab, T. Harab, H. Fujitab, S. Kasaic, T. Endod, T. Iwasee (2003). ' Automated detection methods for architectural distortions around skinline and within mammary gland on mammograms, International Congress Series, Vol. 1256, pp 950–955.

[5]. Liu, S.,(1999). The Analysis of Digital Mammograms: Speculated Tumor Detection and Normal Mammogram Characterization, in School of Electrical and Computer Engineering, Purdue University.

[6]. Rashed, E.A. and M.G. Awad, (2006). Neural networks approach for mammography diagnosis using wavelets features, in First Canadian Student Conference on Biomedical Computing.

[7]. Qian, W., L. Li, and L. Clarke, (1999). Image feature extraction for mass detection in digital mammography: influence of wavelet analysis. Med Phys,. 26(3): p. 402- 408.

[8]. Chen, C. and G. Lee, (1997). Image segmentation using multi resolution wavelet analysis and expectationmaximization (EM) algorithm for digital mammography. Int J Imaging Syst Technol,. 8(5): p. 491-504.

[9]. Wang, T. and N. Karayiannis, (1998). Detection of micro calcifications in digital mammograms using wavelets. IEEE Trans Med Imaging,. 17(4): p. 498-509.

[10]. Kim, J.K. and H.W. Park, (1999). Statistical textural features for detection of microcalci"cations in digitized mammograms. IEEE Trans. Med. Imag.,18(3): p. 231-238.

[11]. Lefebvre, F., H. Benali, and E. Kahn, (1992). Fractal analysis of clustered micro calcifications in mammograms. Acta Stereol,.11: p. 611-616.

[12]. Haralick, R.M. (1979). Statistical and structural approaches to texture. In Proceedings of the IEEE. Vol. 67, No. 5.

[13]. Kim, J.K. and H.W. Park, (1999). Statistical textural features for detection of micro calcinations in digitized mammograms. IEEE Trans. Med. Image.,18(3): p. 231- 238.

[14]. Polakowski, W., et al.,(1997). Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative based feature saliency. IEEE Trans Med Imaging,.16(6): p. 811-819.

[15]. Buyue Zhang, Jan P. Allebach (2008). Adaptive Bilateral Filter for Sharpness Enhancement and Noise Removal, IEEE Transactions on Image Processing, Vol. 17, No. 5.

[16]. Alexander Wong, (2008). Adaptive bilateral filtering of image signals using local phase characteristics, Elsevier Signal Processing, 88, 1615–1619

[17]. Jinshan Tang, Shengwen Guo, Qingling Sun, Youping Deng, Dongfeng Zhou (2009). Speckle noise reduction by bilateral filter for cattle follicle segmentation. The 2009 International Conference on Bioinformatics & Computational Biology (BioComp 2009) Las Vegas, NV, USA. 13-16.

[18]. Hyung W., Kang Charles K., Chui Uday K. Chakraborty (2006). A unified scheme for adaptive stroke-based Rendering, Visual Computer, 22: 814–824

[19]. Xiaoping Lin (2009). Research and Application of Mathematical Morphology Algorithms on OSSC, IEEEOSSC 173.

[20]. Joseph M. Reinhardt and William E., (1996). Higgins Efficient Morphological Shape Representation, IEEE Transactions on Image Processing, Vol. 5, No. 1, 89.

[21]. Tomasz Arod´za, (2006). Detection of clustered micro calcifications in small field digital mammography, Computer methods and programs in biomedicine, 81 56–65.

[22]. Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins (2004). Digital Image Processing using MATLAB. Pearson Education. ISBN 978-81-7758-898-9

[23]. http://www.mammoimage.org/databases/

[24]. Jinsan Tang (2009). A multi-direction GVF snake for the segmentation of skin cancer images, ELSEVIER Pattern recognition, Vol: 42, 1172-1179.

Full Article (HTML)

Pdf

Research Paper

Discount Analysis for Decision Indication in Retail Mart

K. Lavanya* , J. Shaik Mohamed, S. Ramakrishna*

* MCA Department, SITAMS, Chittoor, A.P.

Assistant Professor, MCA Department, SITAMS, Chittoor, A.P.

* Professor, Department of Computer Science, SVUCCMCS, S.V. University, Tirupati, A.P.

Lavanya.K.,, Mohamed,S.J., and Ramakrishna.S. (2014). Discount Analysis for Decision Indication in Retail Mart. i-manager’s Journal on Software Engineering, 8(4), 25-32. https://doi.org/10.26634/jse.8.4.3049

Abstract

Businesses use discount pricing strategy to sell the products in high quantities. With this strategy, it is important to cut costs and stay competitive. Large retailers are able to demand price discounts and make a discount pricing strategy effective. The shopping mart in general gives different kinds of discounts such as one plus one offer, cost reduction, bulk discount and seasonal offers on near expiry products and low sales products. In this study, the Discount Offering algorithm is proposed and implemented to help the retailer to make effective decision by listing out the products with its discount details. This algorithm automatically calculates the combination of products for selling one plus one offer, cost reduction techniques, bulk discount offer and seasonal offer by comparing cost, expiry date and lowest sales of all the products in the retail mart. The main advantage of this approach is that it attracts attention and boost up sales for retailer.

References

[1].Discount benefits. Retrieved from www.inc.com/encyclopedia/discount-sales.html

[2]. Relationship between pricing strategy and discount. Retrieved from www.lead-converter.com/blog/how-toincrease- sales-with-an-effective-discounting-strategy/

[3]. Discount situation. Retrieved from www.entrepreneurs-journey.com/7306/how-these-3- discounting-strategies-maximize-your-profits

[4]. Sales impact on discount. National Society for accountants. Retrieved from www.accountingweb.com/ blogs/bbmarketingplus/top-line/are-discounts-goodway- increase-sales

[5]. Discount Types. Retrieved from www.smallbusiness. chron.com/discount-pricing-strategy-794.html

[6]. Discount Strategy. Retrieved from www.amanet.org/ training/articles/Should-You-Offer-a-Discount.aspx

[7]. Hartley, Tom. (1992). "You Always Hear the Word 'Sale—But Does It Work?" Business First of Buffalo.

Full Article (HTML)

Pdf

Review Paper

Guidelines to Improve Software Engineering Process Using Artificial Intelligence Techniques

Hema Shankari* , R. Thirumalai Selvi**

Assistant Professor, Department of Computer Science, Women's Christian College, Chennai.

Assistant Professor, Department of Computer Science, Govt. Arts College (Men) (Autonomous), Chennai.

Shankari,H.K., and Thirumalaiselvi.R. (2014). Guidelines to Improve Software Engineering Process Using Artificial Intelligence Techniques. i-manager’s Journal on Software Engineering, 8(4),33-43. https://doi.org/10.26634/jse.8.4.3050

Abstract

Software Engineering and Artificial Intelligence are the two important fields of the Computer Science. Artificial Intelligence is about making machines intelligent, while Software Engineering is knowledge intensive activity, requiring extensive knowledge of the application domain and to target on software itself. This study intends to review the techniques developed in artificial intelligence from the standpoint of their application in software engineering. The goal of this paper is to give some guidelines to use the artificial intelligence techniques that can be applied in solving problems associated with software engineering processes. This paper also find out the exact AI technique is likely to be fruitful for particular software development process.

References

[1]. Jyoti tewari, Swati Arya, Prem Narayan Singh (2013). “Approach of Intelligent Software Agents in Future Development”, IJARCSSE, ISSN:2277128X, May 2013.

[2]. H. H. Ammar, W. Abdelmoez, and M. S. Hamdi, (2012). "Software Engineering Using Artificial Intelligence Techniques: Current State and Open Problems", Proceedings of the First Taibah University International Conference on Computing and Information Technology (ICCIT 2012), Al-Madinah Al-Munawwarah, Saudi Arabia, 12-14 March 2012.

[3]. Mark Harman (2011). “The Role of Artificial Intelligence in Software Engineering” ACM computing surveys, 2011.

[4]. Prince Jain (2011). “Interaction between Software Engineering and Artificial Intelligence-A Review ”- International Journal on Computer Science and Engineering (IJCSE), ISSN: 0975-3397, Vol. 3 No.12, December 2011.

[5]. Dr. Nachamai. M, Senthil Vadivu and Tapaskar (2011). “Enacted Software Development Process Based on Agent methodologies”, IJEST, Vol.3, No.11, November 2011.

[6]. Farid Meziane, Sunil Vadera, (2010). “Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects” DOI: 10.4018/978-1-60566-758-4.ch014.

[7]. Farah Naaz Raza (2009). “Artificial Intelligence Techniques in Software Engineering, International multiconference of Engineers and Computer Scientists 2009 – IMECS, ISBN 978-988.17012-2-0 Vol. 1 2009.

[8]. Parveen Ranjan Srivastava, and Tai-Hoon Kim (2009). Application of Genetic Algorithm in Software Testing, International Journal of Software Engineering and its Applications. Vol. 3, No.4, October 2009.

[9]. Hooshyar, B., Tahmani, A., & Shenasa, M. (2008). A Genetic Algorithm to Time-Cost Trade off in project scheduling. Proceedings of the IEEE World Congress on Computational Intelligence, Hong Kong, pp. 3081-3086.

[10]. Yang, Y., Xia, F., Zhang, W., Xiao, X., Li, Y., & Li, X. (2008). Towards Semantic Requirement Engineering. IEEE International Workshop on Semantic Computing and Systems (pp. 67-71).

[11]. Zhen-Yu, Z., Wei-Yang, Y., & Qian-Lei, L. (2008). Applications of Fuzzy Critical Chain Method in Project Scheduling. In Proceedings of the Fourth International Conference on Natural Computation (pp. 473-477), Jinan, China.

[12]. Bertolino, A. (2007). Software Testing Research: Achievements, Challenges, Dreams. In Proceedings of the IEEE International Conference on Software Engineering (pp. 85-103), Minneapolis, MN.

[13]. Kobbacy, K. A., Vadera, S., & Rasmy, M. H. (2007). AI and OR in management of operations: history and trends. The Journal of the Operational Research Society, 58, 10–28. doi:10.1057/palgrave.jors.2602132.

[14]. Nand, S., Kaur, A., Jain S. (2007). Use of Fuzzy Logic in Software Development. Issues in Information Systems. Volume VIII, No. 2, pp. 238-244.

[15]. Hu, Y., Chen, J., Rong, Z., Mei, L., & Xie, K. (2006). A Neural Networks Approach for Software Risk Analysis. In Proceedings of the Sixth IEEE International Conference on Data Mining Workshops (pp. 722-725), Hong Kong.

[16]. Wappler, S., & Wegener, J. (2006). Evolutionary unit testing of object-oriented software using strongly-typed genetic programming. In Proceedings of the Eighth Annual Conference on Genetic and Evolutionary Computation (pp. 1925-1932).

[17]. Yujia, G., & Chang, C. (2006). Capability-based Project Scheduling with Genetic Algorithms. In Proceedings of the International Conference on Intelligent Agents, Web Technologies and Internet Commerce (pp. 161-161).

[18]. Briand, L. C., Labiche, Y., & Shousha, M. (2005). Stress testing real-time systems with genetic algorithms. In Proceedings of the Conference on Genetic and Evolutionary Computation (pp. 1021- 1028).

[19]. Baresel, A., Binkley, D., Harman, M., & Korel, B. (2004). Evolutionary testing in the presence of loopassigned flags: a testability transformation approach. In Proceedings of the ACM SIGSOFT International Symposium on Software Testing and Analysis (pp. 108- 118).

[20]. Gupta, M., Bastani, F., Khan, L., & Yen, I.-L. (2004). Automated test data generation using MEA-graph planning. In Proceedings of the Sixteenth IEEE Conference on Tools with Artificial Intelligence (pp. 174- 182).

[21]. Wallace, L., & Keil, M. (2004). Software project risks and their effect on outcomes. Communications of the ACM, Vol. 47(4), 68–73. doi:10.1145/975817.975819.

[22]. Baudry, B., Fleurey, F., Jezequel, J.-M., & Le Traon, Y. (2002a). Automatic test case optimization using a bacteriological adaptation model: application to. NET components. In Proceedings of the Seventeenth IEEE International Conference on Automated Software Engineering (pp.253-256).

[23]. Baudry, B., Fleurey, F., Jezequel, J. M., & Le Traon, Y. (2002b). Genes and Bacteria for Automatic Test Cases Optimization in the. Net Environment. In Proceedings of the Thirteenth International Symposium on Software Reliability Engineering (ISSRE'02), 195- 206).

[24]. Briand, L. C., Feng, J., & Labiche, Y. (2002). Using genetic algorithms and coupling measures to devise optimal integration test orders. In Proceedings of the Fourteenth International Conference on Software Engineering and knowledge Engineering (pp. 43-50).

[25]. Hindi, K. S., Hongbo, Y., & Fleszar, K. (2002). An evolutionary algorithm for resource-constrained project scheduling. IEEE Transactions on Evolutionar y Computation, 6(5), 512–518.

[26]. Jonathan Onowakpo Goddey Ebbah “Deploying Artificial Intelligence Techniques In Software Engineering, American Journal of Undergraduate Research, March 2002.

[27]. Neumann, D. (2002). An Enhanced Neural Network Technique for Software Risk Analysis. IEEE Transactions on Software Engineering, 28(9), 904–912. doi:10.1109/ TSE.2002.1033229.

[28]. Shan, Y., McKay, R. I., Lokan, C. J., & Essam, D.L. (2002). Software project effort estimation using genetic programming. In Proceedings of the IEEE International Conference on Communications, Circuits and Systems (pp. 1108-1112), Arizona. Washington DC: IEEE Computer Society.

[29] Thwin, M. M. T., & Quah, T.-S. (2002). Application of neural network for predicting software development faults using object-oriented design metrics. In Proceedings of the Ninth International Conference on Neural Information Processing (pp.2312-2316).

[30]. Mark Harman, Bryan F. Jones, (2001). 'Search Based Software Engineering”, ACM Computing Surveys, Elsevier science, 2001.

[31]. T. Menzies, “Practical machine learning for software engineering and knowledge engineering,” in Handbook of Software Engineering and Knowledge Engineering. World-Scientific, December 2001.

[32]. C. Mair, G. Kadoda, M. Lefley, K. Phalp, C. Schofield, M. Shepperd, and S. Webster, “An investigation of machine learning based prediction systems,” The Journal of Systems and Software, Vol. 53, No. 1, pp.23-29, Jul. 2000.

[33]. Meziane, F., Vadera, S., Kobbacy, K., & Proudlove, N. (2000). Intelligent Systems in Manufacturing: Current Developments and Future Prospects. The International Journal of Manufacturing Technology Management, Vol. 11(4), pp.218-238.

[34]. C. Ryan (2000). Automatic re-engineering of software using genetic programming. Kluwer Academic Publishers, 2000.

[35]. Ian Sommerville, (2000). Software Engineering (6th Edn.) (Addison Wesley Publishers, New York, USA) 2000.

[36]. Memon, A. M., Pollack, M. E., & Soffa, M. L. (1999). Using a Goal Driven Approach to Generate Test Cases for GUIs. In Proceedings of the Twenty-first International Conference on Software Engineering (pp. 257-266).

[37]. J. Karlsson and K. Ryan, (1997). “A cost- value approach for prioritizing requirements,” IEEE Software, Vol. 14, No. 5, pp. 67–74, September/October 1997.

[38]. Khoshgoftaar, T. M., & Lanning, D. L. (1995). A Neural Network Approach for Early Detection of Program Modules Having High Risk in the Maintenance Phase. Journal of Systems and Software, Vol. 29, pp.85–91. doi:10.1016/0164-1212(94)00130-F.

[39]. Cheng, R., & Gen, M. (1994). Evolution program for resource constrained project scheduling problem. In Proceedings of the 1st First IEEE Conference on Evolutionary Computation, s(pp. 736-741), Orlando, FL, USA.

[40]. Stuart Russell and Peter Norvig, (1994). Artificial Intelligence: A Modern Approach (Prentice Hall Publishers, Upper Saddle River, New Jersey, USA) 1994.

[41]. DeMillo, R.A., Offutt, A.J. (1991). Constraint- based automatic test data generation. IEEE Transactions on Software Engineering, Vol. 17 (9), pp.900–910

[42]. Boardman, J. T., & Marshall, G. (1990). A knowledgebased architecture for project planning and control. In Proceedings of the UK Conference on IT, (pp. 125-132), Southampton, UK.

[43]. Seth Hock, (1989). Computers and Computing. Houghton Mifflin College Publishers, Boston, Massachusetts, USA, 1989.

[44]. Bering, C. A., & Crawford, M. W. (1988). Using an expert system to test a logistics information system. In Proceedings of the IEEE National Aerospace and Electronics Conference (pp.1363-1368), Dayton, OH. Washington DC: IEEE Computer Society.

[45]. M.L. Emrich, A. Robert Sadlowe, and F. Lloyd Arrowood (1988). Expert Systems and Advanced Data Processing. Proceedings of the conference on Expert Systems Technology the ADP Environment, (Elsevier-North Holland, New York, USA) 1988.

[46]. Roger S. Pressman (1988). A Beginner's Guide. McGraw Hill Higher Education Publishers, New York, 1988.

[47]. Booch, G. (1986). Object-Oriented Development. IEEE Transactions on Software Engineering, Vol. 12(2), pp. 211–221

[48]. Sathi, A., Fox, M. S., & Greenberg, M. (1985). Representation of Activity Knowledge for Project Management. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-7(5), pp.531–552. doi:10.1109/TPAMI.1985.4767701.

[49]. Wright, J. M., Fox, M. S., & Adam, D. (1984). SRL/2 User Manual. Robotic Institute, Carnegie- Mellon University, Pittsburgh, PA.

[50]. Siemens, N. (1971). A Simple CPM Time-Cost Tradeoff Algorithm. Management Science, Vol. 17(6), 354–363. doi:10.1287/mnsc.17.6.B354.

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

Current Issue Vol. 18 Issue 2

Most Read

Most Cited

Volume 8 Issue 4 April - June 2014

OpenC: Extending C with Computational Reflection

Songqing Yue*

Department of Mathematics and Computer Science, University of Central Missouri, Warrensburg, USA.

Yue ,S.(2014). OpenC: Extending C with Computational Reflection. i-manager’s Journal on Software Engineering, 8(4), 1-8. https://doi.org/10.26634/jse.8.4.3046

Abstract

References

Full Article (HTML)

Pdf

Detecting Hidden Data Using Higher Order Empirical Transition Matrix

Swagota Bera* , Monisha Sharma**

* Associate Professor, Department of Electronics and Telecommunication, Shri Shankaracharya Institute of Engineering & Technology, Durg. ** Professor, Department of Electronics and Telecommunication, Shri Shankaracharya College of Engineering and Technology, Bhilai.

Bera,S., and Sharma,M. (2014). Detecting Hidden Data Using Higher Order Empirical Transition Matrix. i-manager’s Journal on Software Engineering, 8(4), 9-16. https://doi.org/10.26634/jse.8.4.3047

Abstract

References

Full Article (HTML)

Pdf

Clustered Micro Calcification Detection Based On the Texture Feature Extraction Using Adaptive Bilateral Filter Of Mathematical Morphology

B. Sridhar* , K.V.V.S. Reddy**, A.M. Prasad***

* Faculty, Department of ECE, Lendl Institute of Engineering and Technology, Vizianagaram, India. ** Professor, Department of ECE, Andhra University, Visakhapatnam, India. *** Professor, Department of ECE, JNT University, Kakinada, India.

Sridhar.B., Reddy.K.V.V.S., and Prasad.A.M. (2014). Clustered Micro Calcification Detection Based On the Texture Feature Extraction Using Adaptive Bilateral Filter of Mathematical Morphology. i-manager’s Journal on Software Engineering, 8(4), 17-24. https://doi.org/10.26634/jse.8.4.3048

Abstract

References

Full Article (HTML)

Pdf

Discount Analysis for Decision Indication in Retail Mart

K. Lavanya* , J. Shaik Mohamed**, S. Ramakrishna***

* MCA Department, SITAMS, Chittoor, A.P. **Assistant Professor, MCA Department, SITAMS, Chittoor, A.P. *** Professor, Department of Computer Science, SVUCCMCS, S.V. University, Tirupati, A.P.

Lavanya.K.,, Mohamed,S.J., and Ramakrishna.S. (2014). Discount Analysis for Decision Indication in Retail Mart. i-manager’s Journal on Software Engineering, 8(4), 25-32. https://doi.org/10.26634/jse.8.4.3049

Abstract

References

Full Article (HTML)

Pdf

Guidelines to Improve Software Engineering Process Using Artificial Intelligence Techniques

Hema Shankari* , R. Thirumalai Selvi**

Assistant Professor, Department of Computer Science, Women's Christian College, Chennai. Assistant Professor, Department of Computer Science, Govt. Arts College (Men) (Autonomous), Chennai.

Shankari,H.K., and Thirumalaiselvi.R. (2014). Guidelines to Improve Software Engineering Process Using Artificial Intelligence Techniques. i-manager’s Journal on Software Engineering, 8(4),33-43. https://doi.org/10.26634/jse.8.4.3050

Abstract

References

Full Article (HTML)

Pdf

* Associate Professor, Department of Electronics and Telecommunication, Shri Shankaracharya Institute of Engineering & Technology, Durg.

** Professor, Department of Electronics and Telecommunication, Shri Shankaracharya College of Engineering and Technology, Bhilai.

B. Sridhar* , K.V.V.S. Reddy, A.M. Prasad*

* Faculty, Department of ECE, Lendl Institute of Engineering and Technology, Vizianagaram, India.

Professor, Department of ECE, Andhra University, Visakhapatnam, India.

* Professor, Department of ECE, JNT University, Kakinada, India.

K. Lavanya* , J. Shaik Mohamed, S. Ramakrishna*

* MCA Department, SITAMS, Chittoor, A.P.

Assistant Professor, MCA Department, SITAMS, Chittoor, A.P.

* Professor, Department of Computer Science, SVUCCMCS, S.V. University, Tirupati, A.P.

Assistant Professor, Department of Computer Science, Women's Christian College, Chennai.

Assistant Professor, Department of Computer Science, Govt. Arts College (Men) (Autonomous), Chennai.