Volume-2 ~ Issue-1
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Abstract : CONTEXT – Global Software Development (GSD) is a modern software engineering paradigm adopted by many client organisations in developed countries to get high quality product at low cost in low wage countries. Production of high quality software is considered as one of the key factor in the rapid growth of GSD. However GSD projects have put new challenges to practitioners and researchers. In order to address these challenges Software Quality Metrics (SQMs) are frequently used in organisations to fabricate high quality products. OBJECTIVE - The objective of this SLR protocol is to identify and assess strengths and weaknesses of the existing SQMs used in GSD to assist vendor organisations in choosing appropriate SQMs for measuring software quality. METHOD – Systematic Literature Review (SLR) will be used for the identification of the existing SQMs in GSD. SLR is based on a structured protocol, and is therefore, different from ordinary review. EXPECTED OUTCOME – We have developed the SLR protocol and are currently in process of its implementation. The expected outcomes of this review will be the identification of different SQMs for GSD along with their SWOT analysis to assist vendor organisations in choosing appropriate SQMs at the right time to produce a high quality product.
Keywords - Software Quality Metrics, Global Software Development, Systematic Literature Review Protocol
Keywords - Software Quality Metrics, Global Software Development, Systematic Literature Review Protocol
[1] S. U. Khan, "Software outsourcing vendors‟ readiness model (SOVRM)," Keele University UK, 2010.
[2] IEEE, ",IEEE Std 610.12-1990-IEEE Standards Glossary of Software Engineering Terminology, IEEE Computer Society," IEEE Std 610.12-1990, 1991.
[3] S. R. Pressman, Software Engineering : A practitioner's Approach, Sixth Edition ed.
[4] N. Fenton, "Software Measurement: A Necessary Scientific Basis," IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, NO. 3. MARCH 1994, vol. VOL. 20, 1994.
[5] T. DeMacri, "Controlling Software Projects," 1982.
[6] "A Software Metrics Primer," 2007.
[7] S. L. P. Fenton Norman E, Software Metrics: a Rigorous and Practical Approach, 2nd Edition ed, 1997.
[8] H. Zuse, Bollmann-Sdorra, and Peter, "Measurement Theory and Software Measures," presented at Proceedings of the BCS-FACS Workshop on Formal Aspects of Measurement, London, 1991.
[9] E. M. Everald, "Software Metrics SEI Curriculum Module SEI-CM-12-1.1," Seattle University Seattle, Washington, Washington December 1988.
[10] L. L. Westfall, "Twelve Step to Useful Software Metrics," presented at Proceedings of the Seventeenth Annual Pacific Northwest Software Quality Conference, 2005.
[11] J. Capers, "STRENGTHS AND WEAKNESSES OF SOFTWARE METRICS," Software Productivity Research LLC, 2006.
[12] M. Xenos, "Software Metrics and Measuremnts," Encylopedia of E-Commerce,E-Government and Mobile Commerce, pp. 8, 2006.
[13] J. Magne, "Software quality measurement."
[14] S. F.Norman "Knowledge Requirements for Software Quality Measurement," Empricial Software Engineering, 2001.
[15] Z. Dave, "Measuring Software Product Quality: the ISO 25000 Series and CMMI," European SEPG, 2004.
[2] IEEE, ",IEEE Std 610.12-1990-IEEE Standards Glossary of Software Engineering Terminology, IEEE Computer Society," IEEE Std 610.12-1990, 1991.
[3] S. R. Pressman, Software Engineering : A practitioner's Approach, Sixth Edition ed.
[4] N. Fenton, "Software Measurement: A Necessary Scientific Basis," IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, NO. 3. MARCH 1994, vol. VOL. 20, 1994.
[5] T. DeMacri, "Controlling Software Projects," 1982.
[6] "A Software Metrics Primer," 2007.
[7] S. L. P. Fenton Norman E, Software Metrics: a Rigorous and Practical Approach, 2nd Edition ed, 1997.
[8] H. Zuse, Bollmann-Sdorra, and Peter, "Measurement Theory and Software Measures," presented at Proceedings of the BCS-FACS Workshop on Formal Aspects of Measurement, London, 1991.
[9] E. M. Everald, "Software Metrics SEI Curriculum Module SEI-CM-12-1.1," Seattle University Seattle, Washington, Washington December 1988.
[10] L. L. Westfall, "Twelve Step to Useful Software Metrics," presented at Proceedings of the Seventeenth Annual Pacific Northwest Software Quality Conference, 2005.
[11] J. Capers, "STRENGTHS AND WEAKNESSES OF SOFTWARE METRICS," Software Productivity Research LLC, 2006.
[12] M. Xenos, "Software Metrics and Measuremnts," Encylopedia of E-Commerce,E-Government and Mobile Commerce, pp. 8, 2006.
[13] J. Magne, "Software quality measurement."
[14] S. F.Norman "Knowledge Requirements for Software Quality Measurement," Empricial Software Engineering, 2001.
[15] Z. Dave, "Measuring Software Product Quality: the ISO 25000 Series and CMMI," European SEPG, 2004.
- Citation
- Abstract
- Reference
- Full PDF
CONTXEXT:-Software outsourcing partnership (SOP) is a relationship between client and vendor
organizations for shared goals. A SOP is different than ordinary outsourcing contractual relationship. Usually
a successful outsourcing relationship may lead to outsourcing partnership.
OBJECTIVE:-The objective of this research is to identify factors via systematic literature review (SLR), that are significant to be developed by outsourcing vendor organization which lead them to convert existing outsourcing contractual relationship into outsourcing partnership with client organization.
METHOD: -SLR will be used for the aforementioned objective. SLR is based on a structured protocol and is more thorough than ordinary review.
EXPECTED OUTCOMES: - We have developed a SLR protocol for the SOP. The anticipated outcome of this review will be a list of critical success factors (CSFs) and critical risks (CRs) which can have a positive or a negative role in building or converting the existing outsourcing relationship into outsourcing partnership.
Keywords: Client-Vendor Relationship, Software Outsourcing partnership, Systematic Literature Review Protocol
OBJECTIVE:-The objective of this research is to identify factors via systematic literature review (SLR), that are significant to be developed by outsourcing vendor organization which lead them to convert existing outsourcing contractual relationship into outsourcing partnership with client organization.
METHOD: -SLR will be used for the aforementioned objective. SLR is based on a structured protocol and is more thorough than ordinary review.
EXPECTED OUTCOMES: - We have developed a SLR protocol for the SOP. The anticipated outcome of this review will be a list of critical success factors (CSFs) and critical risks (CRs) which can have a positive or a negative role in building or converting the existing outsourcing relationship into outsourcing partnership.
Keywords: Client-Vendor Relationship, Software Outsourcing partnership, Systematic Literature Review Protocol
[1] J. N. Lee, Huynh, M.Q., Kwok, C.W., and Pi S.M., "The evolution of outsourcing research: What is the next issue?,," presented at
Proceeding of the Thirty-third Hawaii International Conference on Systems Sciences, Maui in Hawaii, 2000.
[2] W. F. a. N. McFarlan, R.L., "How to Manage an IT Outsourcing Alliance.," Sloan Management Review, vol. 36(2), pp. 9-23, 1995.
[3] M. A. Zviran, Niv Armoni,Aviad, "Building outsourcing relationship across the global community: the UPS-Motorola experience,"
Strategic information System, vol. 10, pp. 313-333, 2001.
[4] R. M. L.M. Applegate, E., "Kodak, Managing information systems through strategic alliances,," Harvard Business School Press,
Boston, Boston 9-192-030,, 1991.
[5] B. F. Yang, Hongjiao Zuo,Meiyun, "A Case Study of Disaster Backup Outsourcing of SDB and Hi Sun," ICEC, Xi'an,China August
15 2005.
[6] B. I. D.R. Lasher, S.L. Jarvenpaa,, "USAA-IBM partnerships in information technology: managing the image project," MIS
Quarterly, vol. 15, pp. 551-565., 1991.
[7] K. D. L.M. Applegate, "Xerox: outsourcing global information technology resources," Harvard Business School Press, Boston 9 -
195-158, 1995.
[8] S. U Khan, M Niazi Ahmad,Rashid, "Factors influencing clients in the selection of offshore software outsourcing vendors,"
Journal of System and Software, 2010.
[9] J. T. V.Grover, M.Cheon, "The effect of service quality and partnership on the outsourcing of information system," Journal of
Management information system, 1996.
[10] D. M. Lambert, M.A. Emmelhainz, and J.T. Gardner,, "Building Successful Logistics Partnerships," Journal of Business Logistics,
vol. 20, pp. 165-181, 1999.
[11] J. N. a. K. Lee, Y. G,, "Effect of Partnership Quality on IS Outsourcing Success: Conceptual Framework and Empirical Validation,"
Journal of Management Information Systems, vol. 15, pp. 29-61, 1999.
[12] R. Kishore, Rao, H.R., Nam, K., Rajagopalan, S., and Chaudhury, A., "A relationship perspective on IT outsourcing,"
Communications of the ACM, vol. 46(12), pp. 87-92, 2003.
[13] V. a. F. Michell, G., "The IT outsourcing market-place: vendors and their selection," Journal of Information Technology, vol. 12,
pp. 223-237, 1997.
[14] R. a. B. Srinivasan, T.H., "Supplier Performance in Vertical Alliances: The Effects of Self-Enforcing Agreements and Enforceable
Contracts," Organization Science,, vol. 17(4), pp. 436-452, 2006.
[15] B. F. Yang, Hongjiao Zuo,Meiyun, "The Integration Mechanism of IT Outsourcing Partnership," 2005...........
Proceeding of the Thirty-third Hawaii International Conference on Systems Sciences, Maui in Hawaii, 2000.
[2] W. F. a. N. McFarlan, R.L., "How to Manage an IT Outsourcing Alliance.," Sloan Management Review, vol. 36(2), pp. 9-23, 1995.
[3] M. A. Zviran, Niv Armoni,Aviad, "Building outsourcing relationship across the global community: the UPS-Motorola experience,"
Strategic information System, vol. 10, pp. 313-333, 2001.
[4] R. M. L.M. Applegate, E., "Kodak, Managing information systems through strategic alliances,," Harvard Business School Press,
Boston, Boston 9-192-030,, 1991.
[5] B. F. Yang, Hongjiao Zuo,Meiyun, "A Case Study of Disaster Backup Outsourcing of SDB and Hi Sun," ICEC, Xi'an,China August
15 2005.
[6] B. I. D.R. Lasher, S.L. Jarvenpaa,, "USAA-IBM partnerships in information technology: managing the image project," MIS
Quarterly, vol. 15, pp. 551-565., 1991.
[7] K. D. L.M. Applegate, "Xerox: outsourcing global information technology resources," Harvard Business School Press, Boston 9 -
195-158, 1995.
[8] S. U Khan, M Niazi Ahmad,Rashid, "Factors influencing clients in the selection of offshore software outsourcing vendors,"
Journal of System and Software, 2010.
[9] J. T. V.Grover, M.Cheon, "The effect of service quality and partnership on the outsourcing of information system," Journal of
Management information system, 1996.
[10] D. M. Lambert, M.A. Emmelhainz, and J.T. Gardner,, "Building Successful Logistics Partnerships," Journal of Business Logistics,
vol. 20, pp. 165-181, 1999.
[11] J. N. a. K. Lee, Y. G,, "Effect of Partnership Quality on IS Outsourcing Success: Conceptual Framework and Empirical Validation,"
Journal of Management Information Systems, vol. 15, pp. 29-61, 1999.
[12] R. Kishore, Rao, H.R., Nam, K., Rajagopalan, S., and Chaudhury, A., "A relationship perspective on IT outsourcing,"
Communications of the ACM, vol. 46(12), pp. 87-92, 2003.
[13] V. a. F. Michell, G., "The IT outsourcing market-place: vendors and their selection," Journal of Information Technology, vol. 12,
pp. 223-237, 1997.
[14] R. a. B. Srinivasan, T.H., "Supplier Performance in Vertical Alliances: The Effects of Self-Enforcing Agreements and Enforceable
Contracts," Organization Science,, vol. 17(4), pp. 436-452, 2006.
[15] B. F. Yang, Hongjiao Zuo,Meiyun, "The Integration Mechanism of IT Outsourcing Partnership," 2005...........
- Citation
- Abstract
- Reference
- Full PDF
Abstract: Noises are the unwanted information in an image, so they should be removed before further
processing. Existing methods consider histogram based analysis which is globally varied one. A modified
statistical measured based automatic noise type recognition technique is proposed in this paper. This has 2
phases including training phase and testing phase. The key role involves deduction of noise samples using
filters like wiener, lee, median and extracts the statistical measures like kurtosis and skewness from samples.
Kurtosis and skewness values exhibit behavior based on noise type. By using the statistical information and
trained data we can classify the type of noise. Finally the noise type is identified and corresponding filter is
applied. Thus noise eliminated image would give the desirable results during further processing. Experimental
results show that the method is capable of accurately classifying the types of noise.
Keywords: Enhanced Noise Type Recognition, Kurtosis, Noise type identification, Skewness, Statistical features.
Keywords: Enhanced Noise Type Recognition, Kurtosis, Noise type identification, Skewness, Statistical features.
[1] Yixin Chen, Manohar Das (2007), "An Automated Technique for Image Noise Identification Using a Simple Pattern Classification Approach", IEEE International conference on Circuits and systems, pp.819-822.
[2] Lionel Beaurepaire, Kacem chedi and Benoit Vozel (1997), "Identification of the nature of noise and estimation of its statistical parameters by analysis of local histograms", IEEE International conference on Acoustics, Speech and Signal Processing, Volume: 4, pp. 2805-2808
[3] Dr. P. Subashini, Bharathi.P. T (2011), "Automatic Noise Identification in Images using Statistical Features", International Journal of Computer Science and Technology, Vol. 2, Issue 3, pp. 467-471.
[4] Raina , Shamik Tiwari ,Deepa Kumari ,Deepika Gupta (2012), "An approach for image noise identification using minimum distance classifier", International Journal of Scientific & Engineering Research Volume 3, Issue 4, pp 1-4
[5] Karibasappa K.G and K. Karibasappa (2010), "Identification and Removal of Impulsive noise using Hypergraph Model", IJCSE International Journal on Computer Science and Engineering, Vol. 02, No. 08, pp. 2666-2669.
[6] K. Chehdi, M. Sabri(1992), "A New Approach to identify the nature of noise affecting an image, IEEE on Accoustics, Speech, Signal Processing", Vol.3, pp. 285-288
[7] B. Vozel, K. Chehdi, L. Klaine, Vladimir V. Lukin, Sergey K. Abramov, "Imitation of its statistical parameters by using unsupervised variational classification", IEEE International Conference on Acoustics, Speech and Signal Processing, Vol. 2, pp. 841-844.
[8] T. Santhanam and S. Radhika (2010), "A Novel Approach to Classify Noises in Images Using Artificial Neural Network" , Journal of Computer Science, Vol 1, Pp. 506-510.
[9] Shamik Tiwari, Ajay Kumar Singh, V.P. Shukla (2011), "Statistical Moments based Noise Classification using Feed Forward Back Propagation Neural Network", Proc. International Journal of Computer Applications, Vol. 18, No. 2, pp. 36-40
[10] K. Chehdi(1993). "Automatic identification of noises for an optimal filtering", Proceedings IEEE CCSP 93, pp. 474-477.
[2] Lionel Beaurepaire, Kacem chedi and Benoit Vozel (1997), "Identification of the nature of noise and estimation of its statistical parameters by analysis of local histograms", IEEE International conference on Acoustics, Speech and Signal Processing, Volume: 4, pp. 2805-2808
[3] Dr. P. Subashini, Bharathi.P. T (2011), "Automatic Noise Identification in Images using Statistical Features", International Journal of Computer Science and Technology, Vol. 2, Issue 3, pp. 467-471.
[4] Raina , Shamik Tiwari ,Deepa Kumari ,Deepika Gupta (2012), "An approach for image noise identification using minimum distance classifier", International Journal of Scientific & Engineering Research Volume 3, Issue 4, pp 1-4
[5] Karibasappa K.G and K. Karibasappa (2010), "Identification and Removal of Impulsive noise using Hypergraph Model", IJCSE International Journal on Computer Science and Engineering, Vol. 02, No. 08, pp. 2666-2669.
[6] K. Chehdi, M. Sabri(1992), "A New Approach to identify the nature of noise affecting an image, IEEE on Accoustics, Speech, Signal Processing", Vol.3, pp. 285-288
[7] B. Vozel, K. Chehdi, L. Klaine, Vladimir V. Lukin, Sergey K. Abramov, "Imitation of its statistical parameters by using unsupervised variational classification", IEEE International Conference on Acoustics, Speech and Signal Processing, Vol. 2, pp. 841-844.
[8] T. Santhanam and S. Radhika (2010), "A Novel Approach to Classify Noises in Images Using Artificial Neural Network" , Journal of Computer Science, Vol 1, Pp. 506-510.
[9] Shamik Tiwari, Ajay Kumar Singh, V.P. Shukla (2011), "Statistical Moments based Noise Classification using Feed Forward Back Propagation Neural Network", Proc. International Journal of Computer Applications, Vol. 18, No. 2, pp. 36-40
[10] K. Chehdi(1993). "Automatic identification of noises for an optimal filtering", Proceedings IEEE CCSP 93, pp. 474-477.
