A Relational Database Complexity Computing Model

Xue Yun  Lin

doi:10.4028/www.scientific.net/KEM.474-476.1464

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HomeKey Engineering MaterialsKey Engineering Materials Vols. 474-476A Relational Database Complexity Computing Model

A Relational Database Complexity Computing Model

Abstract:

In this paper we have proposed a model to compute the complexity of small relational database application developed in MS Access. The model was built using different database objects. These objects were categorized based upon the basis of weights assigned to their complexity determining factors. We have implemented our model in a tool called SMARtS. We used our model to compute complexity of eight database projects. To evaluate our model, we analyzed the calculated complexity and actual effort. We found that our model successfully captured the complexity of different database projects. Correlation analysis has shown that complexities computed by SMARtS and the actual efforts are strongly correlated.

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Key Engineering Materials (Volumes 474-476)

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1464-1469

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https://doi.org/10.4028/www.scientific.net/KEM.474-476.1464

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Online since:

April 2011

Authors:

Xue Yun Lin

Keywords:

Complexity Computing Model, Complexity Weight, Relational Database

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References

[1] Sana Abiad, Ramzi A Haraty, and Nashat Mansour., (2000) Software Metrics for Small Database Applications", ACM SAC, 00 Como, Italy, March 19-21 2000, PP. 866-870.

DOI: 10.1145/338407.338579

Google Scholar

[2] Justus S, Lyakutti K, ( 2007) Assessing the Object-level Behavioral Complexity in Object Relational Databases, IEEE International Conference on Software-Science, Technology and Engineering. 30-31 Oct. 2007 PP. 48 - 56.

DOI: 10.1109/swste.2007.12

Google Scholar

[3] Microsoftfice Online viewed on19 November 2008, www. office. microsoft. com/en- us/access/HA102330311033. aspx?pid=CH100645701033.

Google Scholar

[4] Elmasri and Navathe. (2006) Fundamentals of Database Systems, Addison Wesley; 5th edition.

Google Scholar

[5] Kautz K. 1999, Making sense of measurement for small organizations, IEEE Software, March / April 1999, pp.14-20.

DOI: 10.1109/52.754047

Google Scholar

[6] Stephen G MacDonell, Martin J. Shepperd, and Philip J. Sallis (1997) Metrics for Database Systems: An Empirical Study, IEEE International Symposium on Software Metrics, 1997, PP 99-107.

DOI: 10.1109/metric.1997.637170

Google Scholar

[7] Mario Piattini, Coral Calero and Marcela Genero, (2001) Table Oriented Metrices For Relational Databases, Software Quality Journal, 9, 79–97, (2001).

DOI: 10.1023/a:1016670717863

Google Scholar

[8] Albrecht A.E. and Gaffney J.E. 1983, Software function, lines of code, and development effort prediction: a software science validation, IEEE Transactions on Software Engineering, 9(6), pp.639-647.

DOI: 10.1109/tse.1983.235271

Google Scholar