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Reseach Article

Software Project Contacts by GRGA Scheduling and EVM

by Dinesh. B. Hanchate, Rajankumar S. Bichkar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 97 - Number 13
Year of Publication: 2014
Authors: Dinesh. B. Hanchate, Rajankumar S. Bichkar
10.5120/17064-7502

Dinesh. B. Hanchate, Rajankumar S. Bichkar . Software Project Contacts by GRGA Scheduling and EVM. International Journal of Computer Applications. 97, 13 ( July 2014), 1-26. DOI=10.5120/17064-7502

@article{ 10.5120/17064-7502,
author = { Dinesh. B. Hanchate, Rajankumar S. Bichkar },
title = { Software Project Contacts by GRGA Scheduling and EVM },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 97 },
number = { 13 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume97/number13/17064-7502/ },
doi = { 10.5120/17064-7502 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:23:58.815110+05:30
%A Dinesh. B. Hanchate
%A Rajankumar S. Bichkar
%T Software Project Contacts by GRGA Scheduling and EVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 97
%N 13
%P 1-26
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The well planned SPLC (Software project life cycle) doesn't give certainty about the completion of project in time and budget. The PMP (Project Management Processes), pregnant processes, rework, some float types and review process, always, put stumbling block for completion of project in Time. Apart from this defined process, there is need of genius decision making process. The various planned schedule, redundancy and contingency target schedules can be used as input for decision making processes and the solutions to be adapted during the stages of SPLC. This paper gives various schedule types with respect to software project contracts. These schedule types are the outputs of GRGA (Gene Repair Genetic algorithm) along the utilisation of EVM (Earned Value Management) concepts. The GRGA (Gene Repair based Genetic Algorithm) approach gives choice to change the constraints and or features as objective components in objective function. In this paper, we present different schedules as the outcome to evaluate the effectiveness of genetic operator GeneRepair. This operator is developed to correct invalid schedule generated following crossover and mutation. Following implementation and testing of GA with GeneRepair, we found a significant positive side in our results in speed and accuracy also. we have been able to generate very good results in an efficient manner, in terms of both time and number of evaluations using GeneRepair with traditional crossover and mutation operators.

References
  1. PMBOK A Guide to the Project Management Body of Knowledge. www. amazon. com.
  2. PMI India PMBOK Guide and Standards.
  3. Software asset management. www. software. one. com.
  4. Felician ALECU. Management of software development projects. Economics of Knowledge, 33, 2011.
  5. Konduru Sivaramakrishnan Anandasivam Gopal. On vendor preferences for contract types in offshore software projects: The case of fixed price vs. time and materials contracts.
  6. Rajib Mall Bob Hughes, Mike Cotterell. Software Project Management(SEI). Tata McGraw-Hill Education Pvt. Ltd, BookVistas (New Delhi, DEL, India), April 2011.
  7. Barry Bohem. Software Engineering economics. Prentice Hall PTR, New Jersey.
  8. Frederick P. Brooks Jr. The Mythical ManMonth. Addison- Wesley, 1995.
  9. Tao Zhang Carl K. Chang, Mark. J. Christensen. Genetic algorithms for project management. annals of Software Engineering, 2001.
  10. C. K. Chang and M. Christensen. A net practice for software project management. 1999.
  11. Bob Hughes & Mike Cotterell. Software Project Management. McGraw-Hill Publishing Company, BookVistas (New Delhi, DEL, India), 1999.
  12. Amit Kurmi Dinesh B. Hanchate. Design and analysis of algorithm. Technical Publication, Pune, 2007.
  13. Dinesh A. Zende Dinesh B. Hanchate. Clustering and classification of text by selection process and optimisation by genetica algorithm. ACITS PINNACLE'08, National Conference on Systematics, Informatics, Cybernatics, March 2008.
  14. Santosh A. Shinde Dinesh B. Hanchate, Shabina Sayyad. Defect classification as problem classification for quality control in the software project management by DTL. IEEE International Conference on Computer Engineering and Technology - ICCET, 2010.
  15. Digambar M. Padulkar Dinesh B. Hanchate, Santosh A. Shinde. Impact of risk factors in risk management by bayesian learning. ACM Proceedings of International Conference on Advances in Computing ICAC2008. , feb. 2008. http://itfrindia. org/2011ICCIC/program. php.
  16. Yogesh A. Thorat Dinesh B. Hanchate, Rajaram H. Ambole. Optimization of university course timetabling problem (uctp) using genetic algorithm: A survey. IEEE ICCIC2011, 2011. http://itfrindia. org/2011ICCIC/program. php.
  17. Dr. Rajankumar S. Bichkar Dinesh. B. Hanchate. Software project scheduling by AGA. International journal of computer Applications, 96, June 2014.
  18. Dr. Rajankumar S. Bichkar Dinesh. B. Hanchate. SPS by combination of crossover types and changeable mutation SGA. International journal of computer Applications, 94, May 2014.
  19. Vipul V. Bag Dinesh. B. Hanchate. Organizational behavior and its improvement by software engineering process models training. proceedings of National Conference on Best practices in Engineering Education ( NCBPEE 2007), feb 2007.
  20. S. Sahani E. Horowitz. Fundamentals of Computer Algorithms. Galgotia Publications, 1999.
  21. J. Franciso Chicano Enrique Alba. Sofware project management with gas. SceinceDirect, Information Sciences, 177, 2007.
  22. Jeff G. http://www. excella. com/blog/fixed-price-vs-time-andmaterials- better-software-development-contract-type/. 2002.
  23. M. R Garey and D. S. Johnson. Computers and Intractability. A Guide to the Theory of NP-Completeness. W. H Freeman and Company, New york, 1979.
  24. David Barnes Mark McCarville George G. Mitchell, Diarmuid ODonoghue. Generepair a repair operator for genetic algorithms. Proceedings of the GECCO-2003 Late Breaking Papers, July 2003.
  25. D. E. Goldberg. Genetic Algorithm in Search, Optimiza-tion and Machine Learning. Addison-Wesley, 1989.
  26. Dinesh B. Hanchate Gyankamal J. Chhajed, Mangesh Kulkarni. Object Oriented Modelling Desing. Pragati, Pune, 2007.
  27. Dinesh Bhagwan Hanchate. Analysis, mathematical modeling and algorithm for software project scheduling using BCGA. IEEE Intelligent Computing and Intelligent Systems (ICIS), Oct. 2011.
  28. J. H. Holland. Genetic Algorithms. Scientific american edition, 1992.
  29. Wei Huang and Lixin Ding. Project-scheduling problem with random time-dependent activity duration times. IEEE Transactions on engi-neering management, May 2011.
  30. Pankaj Jalote. Software project management in practice. Addison Wesley, New york, 2004.
  31. Pankaj Jalote. An Integrated Approach to Software Engineering. Narosa publishing House, New york, 2005.
  32. Stephen H. Kan. Metrics and Models in Software Qaulity Engineering. Low price edition edition, 2006.
  33. Dinesh B Hanchate Kishor N Vitekar. Software project planning using ant colony optimization (spp-aco). International Journal of Computer Science And Technology, Oct 2013. www. ijcst. com.
  34. Ying-Hong Liao and Chuen-Tsai Sun. An educational genetic algorithms learning tool. IEEE transactions in Education, 2001.
  35. Walt Lipke. Earned schedule application to small projects. PM WORLD TODAY, 2011.
  36. Gangadharrao Soundalyarao "G. S. " Maddala. Limited- Dependent and Qualitative Variables in Econometrics. Cambridge University Press, Cambridge, UK, 1983.
  37. Duncan McGillivray. User manual for garefl. http://www. reflectometry. org/danse.
  38. Vida Kianzad Michael Rinehart and Shuvra S. Battacharya. Modular genetic algorithm for scheduling task garphs. Technical report UMIACS-TR-2003-66.
  39. Vassil Smarkov Milena Karova, Julka Petkova. A genetic algorithm for project planning problem. International Scientific Conference Computer Science, 2008.
  40. Tom M. Mitchell. Machine Learning. The McGrawHill Companies, 1997.
  41. Himanshu Bhalchandra Dave Parag Himanshu Dave. Design and Analysis of Algorithms. Pearson Education, 2008.
  42. Urmila Shrikant Pawar and Dinesh Bhagwan Hanchate. Literature review on personnel scheduling. International Journal of Computer Engineering and Technology (IJCET), Spt 2013. www. iaeme. com.
  43. Roger S. Pressman. Software Engineering: A practitioners Ap-proach. McGrawHill, Inc. , New york, 1992.
  44. Yogita D. Sinkar Reshma R. Nazirkar, Dinesh B. Hanchate. Scheduling project with random and time-dependent activity. IEEE International conference on computation of power, energy, information and communication (ICCPEIC), April 2014.
  45. Anita Rosen. Effective IT Project Management: Using Teams to Get Projects Completed on Time and Under Budget. PHI, BookVistas (New Delhi, DEL, India), 2008.
  46. Priya V. Patil S. D. Joshi Santosh A. Shinde, Chudaman D. Sukate. Project management by dtl. National Conference on Artificial Intelligence (NCAI 2007), June 2007. xa. yimg. com/kq/groups/18445735/80921961/name/A.
  47. Kathy Schwalbe. It pm book. www. pmtext. com.
  48. Santosh A. Shinde. Software team formation for software project management by neural networks & DTL. International Conference on Innovative Computing, Information and Communication Technology (ICICT09), Dec. 2009.
  49. I Sommerville. Software Engineering. Addison Wesley, New york, 1998.
  50. Du Zhang and Je rey J. P. Tsai. Machine learning and software engineering. IEEE, Proceedings of the 14th International Conference on Tools with Arti cial Intelligence, 2002.
  51. Gabrielle Zimmerman. Contract types for software development. TurnLevel
Index Terms

Computer Science
Information Sciences

Keywords

PMP Software Contacts GRGA COCOMO SPSP (Software Project Scheduling problem) Constraints Optimization EVM.