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Factor for varying the complexity.
Calculate for the adjusted functional point:
The scale starting from 0 to 5 will measure the value of complexity adjustment factor.
So. according to the value the table can be structured below:
FP = total UFP * (0.65 + (0.01 * Total complexity adjustment value))
FP = 61 * (0.65 + (0.01*20))
FP = 61 * (0.65 +0.2)
FP = 61 * 0.85
FP = 51.85
So, the total Adjusted function point is 51.85.
Given, 1 person day = 8 person hours
Productivity rate in person hours = 54.4 person hours
Productivity rate in person day = (54.4 person hours / 8 person hours) * 1 person day
Productivity rate in person day = 6.8 person days.
The total UFP has been calculated which is 53.
2. Complexity factor
So the total complexity adjustment value is 17
TCF = 0.6 + (TF / 100)
TCF = 0.6 + (17/100)
TCF = 0.6 + 0.17
TCF = 0.77
Therefore technical complexity factor is 0.77
3. The productivity rate from question 4 is 6.8 person days.
Let assume, Total line of codes (LOC) = 30000
Effort = LOC / Productivity rate
Effort = 30000 / 6.8 person days
Effort = 4411.76 person days
Let assume, total number of developers = 4
Therefore, Total time required to implement the online DVD rental system = 4411.76 / 4
Total required time = 1102.94 days.
4. In the above section we have assumed the line of codes and total numbers of developers who are involved to make the project complete (Bel and Seb?, 2021). There are almost 30000 lines of code which were completed by four programmers.
The designers have come to make the actual implementation of the online DVD rental system in the Specification B, but the designers have got some different results that have been calculated above in Question 5. There are some reasons to get different results in the project.
As all the calculators were in a normal assumption and everything depends upon the calculation of UFP so this is the one reason that they have got a different result from the assumed result.
And the second point behind the different result is the designers have made some mistake on the coding file and at the first time they have made the wrong calculation when this has been assumed (Choetkiertikul et al. 2018). And after doing it in the proper way they have got the proper result when the entire work is done in a practical way.
The technique of effort estimation is used to do the measurement of estimation effort which is the Albrecht’s technique of prediction. This is a function-oriented procedure to make the research complete. But in the current project the programmers have taken a completely different technique to make the project which is the Delphi technique
Delphi technique is used to provide a different kind of analysis and some more information on the complex issues.
The expert is completely involved into the proper solution in the conflict and used to facilitate the implementations.
This is generally a tedious and a long process as this requires going over more than one time in order to obtain the desired outcome (Erlingsson et al. 2020).
This is necessary to get good communication in order to economize the reception of the answers.
Bel, G. and Seb?, M., 2021. Does inter-municipal cooperation really reduce delivery costs? An empirical evaluation of the role of scale economies, transaction costs, and governance arrangements. Urban Affairs Review, 57(1), pp.153-188.
Choetkiertikul, M., Dam, H.K., Tran, T., Pham, T., Ghose, A. and Menzies, T., 2018. A deep learning model for estimating story points. IEEE Transactions on Software Engineering, 45(7), pp.637-656.
Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Song, S., Talwar, K. and Thakurta, A., 2020. Encode, shuffle, analyze privacy revisited: Formalizations and empirical evaluation. arXiv preprint arXiv:2001.03618.
Ezzat, A., Wu, M., Li, X.L. and Kwoh, C.K., 2019. Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey. Briefings in bioinformatics, 20(4), pp.1337-1357.
Huang, J., Osorio, C. and Sy, L.W., 2019. An empirical evaluation of deep learning for ICD-9 code assignment using MIMIC-III clinical notes. Computer methods and programs in biomedicine, 177, pp.141-153.
Mendis, C., Renda, A., Amarasinghe, S. and Carbin, M., 2019, May. Ithemal: Accurate, portable and fast basic block throughput estimation using deep neural networks. In International Conference on machine learning (pp. 4505-4515). PMLR.
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