Experimental validation of new software engineering

Authors

  • N. Mownika
  • VijaySree
  • P.Hema sai

DOI:

https://doi.org/10.46243/jst.2022.v7.i02.pp384-394

Keywords:

Technology transfer, Survey, Software engineering, Models, Experimentation

Abstract

When to apply a new technology in an organization is a critical decision for every software development organization. Earlier work defines a set of methods that the research community uses when a new technology is developed. This chapter presents a discussion of the set of methods that industrial organizations use before adopting a new technology. First there is a brief definition of the earlier research methods and then a definition of the set of industrial methods. A comparison of the two sets leads into the perspectives of these methods of experts in the research and industrial community via surveys made to those communities.

 

 

References

Adrion W. R., Research methodology in software engineering, Summary of the Dagstuhl Workshop on Future Directions in

Software Engineering, W. Tichy (Ed.), ACM SIGSOFT Software Engineering Notes, 18, 1, (1993)

Basili V., F. McGarry, R. Pajerski, M. Zelkowitz, Lessons learned from 25 years of process improvement: The rise and fall of the

NASA Software Engineering Laboratory, IEEE Computer Society and ACM International Conf. on Soft. Eng., Orlando FL, May 2002

Basili V., M. Zelkowitz, F. McGarry, J. Page, S. Waligora and R Pajerski, SEL‘s software process improvement program, IEEE

Software 12, 6 (1995) 83-87

Berry M. and G. Linoff, Mastering Data Mining, John Wiley & Sons, 2000

Brooks, F. No Silver Bullet: Essence and Accidents of Software Engineering, IEEE Computer (1987), 10-19

Brown A. W. and K. C. Wallnau, A framework for evaluating software technology, IEEE Software, (September, 1996) 39-49

Campbell D. and J. Stanley, Experimental and quasi-experimental designs for research, Rand McNally, Chicago, (1963)

Daly, J., K. El Emam, and J. Miller, Multi-method research in software engineering, 1997 IEEE Workshop on Empirical Studies

of Software Maintenance (WESS ‗97) Bari, Italy, October 3, 1997

Fenton N., S.L.Pfleeger, and R. L. Glass,Science and substance: A challenge to software engineering, IEEE Software, Vol. 11

Kitchenham B. A., Evaluating software engineering methods and tool, ACM SIGSOFT Software Engineering Notes

Miller J., Can Software Engineering Experiments be safely combined?, IEEE Symposium on Software Metrics (METRICS‘99)

Bethesda, MD (November 1999)

Redwine S. and W. Riddle, Software technology maturation, IEEE/ACM International Conference on Software

Engineering, London, UK, (August, 1985) 189-200

Scargle J.D., Publication Bias (The "File-Drawer Problem") in Scientific Inference, Sturrock Symposium, Stanford University

Stanford, CA, (March, 1999)

Shaw M., Keynote presentation, International Conference on Software Engineering, Toronto, Canada, May 2001

(http://www.cs.cmu.edu/~shaw)

Snow63] Snow, C.P., The two cultures and the scientific revolution, New York: Cambridge University Press, 1963

Thorngate W., ―In General‖ vs ―It Depends‖: Some comments on the Gergen-Schlenker debate. Personality and Social

Psychology Bulletin 2, 1976, 404-410

Tichy W. F., P. Lukowicz, L. Prechelt, and E. A. Heinz, Experimental evaluation in computer science: A quantitative study, J. of

Systems and Software Vol. 28, No. 1, 1995 9-18

Tichy, W., Should computer scientists experiment more?, IEEE Computer, 31, 5, 1998, 32-40

Zelkowitz M. V., Yeh R. T., Hamlet R. G., Gannon J. D., Basili V. R., Software engineering practices in the United States

and Japan, IEEE Computer 17, 6 (1984) 57-66

Zelkowitz M. V., Software Engineering technology infusion within NASA, IEEE Trans. on Eng. Mgmt. 43, 3 (August, 1996)

Zelkowitz M. and D. Wallace, Experimental validation in software engineering, Information and Software Technology, Vol

Zelkowitz M. and D. Wallace, Experimental models for validating technology, IEEE Computer, 31, 5, 1998

Published

2022-04-27

How to Cite

N. Mownika, VijaySree, & P.Hema sai. (2022). Experimental validation of new software engineering. Journal of Science & Technology , 7(2), 384–394. https://doi.org/10.46243/jst.2022.v7.i02.pp384-394