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Analysis of Measurement Convertibility from FP to CFP: Conversion Formula and Monotonicity Condition

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 657))

Abstract

The article describes the basics of two software measurement methods: the FPA method, which represents the first generation of Functional Size Measurement (1G FSM), and the COSMIC method, which represents the second generation of such methods (2G FSM). It discusses both the similarities and the observed differences between the two methods. The discussions also include the influence of the increasing variable values on measurement results in both measures.

Drawing upon previous research, an analysis was performed into the relation between the results obtained from both measurement methods. The hypothesis on a linear relation was tested by employing a statistical method of linear regression analysis to investigate a sample of 13 software requirements specification documents. The obtained result was verified on a group of 14 different applications, by using the MMRE and Pred(0.25) measures.

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Notes

  1. 1.

    One of the examples is the Software Productivity Research (SPR) indicator method for effort estimation, which allows calculating the requisite effort N = S/P, where N is requisite effort, S is estimated software size in FP, and P is mean productivity of the project team in FPs per person-month, as calculated for a given programming language.

  2. 2.

    The values of constant weighing numbers were experimentally determined by Allan Albrecht and have been since used in unchanged form (Table 4).

References

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Correspondence to Grażyna Hołodnik-Janczura .

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Hołodnik-Janczura, G. (2018). Analysis of Measurement Convertibility from FP to CFP: Conversion Formula and Monotonicity Condition. In: Wilimowska, Z., Borzemski, L., Świątek, J. (eds) Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017. ISAT 2017. Advances in Intelligent Systems and Computing, vol 657. Springer, Cham. https://doi.org/10.1007/978-3-319-67223-6_21

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  • DOI: https://doi.org/10.1007/978-3-319-67223-6_21

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