# A New Approach to Calibrating Functional Complexity Weight in Software Development Effort Estimation

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Problem Formulation

- RQ1:
- Is the accuracy of the proposed CFCW algorithm better than that of the IFPUG FPA or NFFPCM methods?
- RQ2:
- Does the advanced CFCWO algorithm outperform the CFCW algorithm?
- RQ3:
- How accurate is the estimation for each sector compared to an ungrouped dataset?

#### Contributions

- In the first phase, a new CFCW algorithm for the calibration of functional complexity weight is proposed;
- In the second phase, the result from the first phase is optimised by using a voting regressor to estimate the final software effort—the CFCWO algorithm is proposed;
- The IFPUG FPA method is compared to the CFCW algorithm for ungrouped data and data grouped by IS;
- The CFCW algorithm is compared to the CFCWO algorithm for ungrouped data and data grouped by IS.

## 3. Related Work

## 4. Background

#### 4.1. IFPUG FPA

#### 4.2. Bayesian Ridge Regression Model

#### 4.3. Voting Regressor Model

## 5. Research Methodology

#### 5.1. Experimental Setup

- We selected records with the IFPUG counting approach (including IFPUG Old and IFPUG 4+);
- Only the records where the data quality rating is A or B has been selected;
- The development type was new development;
- The rows with an empty value of base functional components were eliminated;
- Rows with empty values in the industry sector column were removed;
- The rows with empty values in normalised productivity delivery rate and summary work effort (SWE) were also erased;
- We filled the VAF blank cells with the values obtained from Equation (3).

- Bayesian ridge regression (Section 4.2) is employed;
- CFCW elicits the complexity weights for the EI, EO, EQ, EIF, and ELF variables using Bayesian ridge regression;
- The UFP is calculated by using a newly estimated complexity weight for each of the variables (EI, EO, EQ, EIF, and ELF);
- Estimated effort is obtained by multiplying UFP by the VAF and, finally, by multiplying by PF.

- Effort from CFCW (calibration phase) is used as input;
- The voting regressor (Section 4.3) algorithm is employed;
- Voting regressor is an ensemble model, consisting of four estimators (Table 2);
- CFCWO optimises estimated effort by CFCW by minimising the error to SWE (know effort from dataset).

#### Tested Models

- CFCW—effort is computed using the IFPUG approach; complexity weights are estimated by Bayesian ridge regression; PF (PDR) is the mean from all ISs or based on each IS;
- CFCWO—effort is estimated using a trained voting regressor, where the regressor is the effort value from CFCW, and the dependent variable is the SWE value (from the dataset); again, variant for all sectors and per sector were tested.

- IFPUG FPA [37]—effort is computed using the IFPUG approach; IFPUG-based complexity weights and PF (PDR) from the dataset (mean from all sectors or based on each sector);
- NFFPCM [18]—effort is computed using IFPUG approach; complexity weight from the study of Xia et al. and PF (PDR) from the dataset (mean from all sectors or based on each sector).

#### 5.2. Evaluation Criteria

## 6. Results and Discussion

- RQ1:
- Is the accuracy of the proposed CFCW algorithm better than that of the standard IFPUG FPA or NFFCPM methods?

- RQ2:
- Does the advanced CFCWO algorithm outperform the CFCW algorithm?

- RQ3:
- How accurate is the estimation for each sector compared to an ungrouped dataset?

## 7. Threats to Validity

## 8. Conclusions and Future Work

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Standish Group Report. Available online: https://www.standishgroup.com (accessed on 20 September 2021).
- Vera, T.; Ochoa, S.F.; Perovich, D. Survey of Software Development Effort Estimation Taxonomies; Technical Report; Computer Science Department, University of Chile: Santiago, Chile, 2017. [Google Scholar]
- Khan, B.; Khan, W.; Arshad, M.; Jan, N. Software cost estimation: Algorithmic and non-algorithmic approaches. Int. J. Data Sci. Adv. Anal.
**2020**, 2, 1–5. [Google Scholar] - Faria, P.; Miranda, E. Expert judgment in software estimation during the bid phase of a project—An exploratory survey. In Proceedings of the 2012 Joint Conference of the 22nd International Workshop on Software Measurement and the 2012 Seventh International Conference on Software Process and Product Measurement, IEEE, Assisi, Italy, 17–19 October 2012; pp. 126–131. [Google Scholar]
- Azzeh, M.; Nassif, A.B. Analogy-based effort estimation: A new method to discover set of analogies from dataset characteristics. IET Softw.
**2015**, 9, 39–50. [Google Scholar] [CrossRef] [Green Version] - Putnam, L.H. A general empirical solution to the macro software sizing and estimating problem. IEEE Trans. Softw. Eng.
**1978**, 4, 345–361. [Google Scholar] [CrossRef] - Boehm, B. Software Engineering Economics; Prentice-Hall: Englewood Cliffs, NJ, USA, 1981. [Google Scholar]
- Albrecht, A.J. Measuring application development productivity. In Proceedings of the IBM Applications Development Symposium, Monterey, CA, USA, 14–17 October 1979; p. 83. [Google Scholar]
- IFPUG. Function Point Counting Practices; Manual, Release 4.3.1; International Function Point Users Group: Westerville, OH, USA, 2010. [Google Scholar]
- ISO/IEC 19761:2011; Software Engineering—COSMIC: A Functional Size Measurement Method. International Organization for Standardization: Geneva, Switzerland, 2011.
- ISO/IEC 29881:2010; Information Technology—Systems and Software Engineering—FiSMA 1.1 Functional Size Measurement Method. International Organization for Standardization: Geneva, Switzerland, 2010.
- ISO/IEC 20968:2002; Software Engineering—MK II Function Point Analysis—Counting Practices Manual. International Organization for Standardization: Geneva, Switzerland, 2002.
- ISO/IEC 24570:2005; Software Engineering—NESMA Functional Size Measurement Method Version 2.1—Definitions and Counting Guidelines for the Application of Function Point Analysis. International Organization for Standardization: Geneva, Switzerland, 2005.
- Kitchenham, B.; Mendes, E. Why comparative effort prediction studies may be invalid. In Proceedings of the 5th International Conference on Predictor Models in Software Engineering—PROMISE’09, Vancouver, BC, Canada, 18 May 2009; ACM Press: New York, NY, USA, 2009; pp. 1–5. [Google Scholar]
- Peter, R.H. Practical Software Project Estimation; McGraw-Hill: New York, NY, USA, 2011. [Google Scholar]
- Hai, V.V.; Nhung, H.L.T.K.; Hoc, H.T. A review of software effort estimation by using functional points analysis. In Computational Statistics and Mathematical Modeling Methods in Intelligent Systems; Silhavy, R., Silhavy, P., Prokopova, Z., Eds.; Springer: Cham, Switzerland, 2019; pp. 408–422. [Google Scholar]
- Al-Hajri, M.A.; Abdul Ghani, A.A.; Sulaiman, M.N.; Selamat, M.H. Modification of standard Function Point complexity weights system. J. Syst. Softw.
**2005**, 74, 195–206. [Google Scholar] [CrossRef] - Xia, W.; Capretz, L.F.; Ho, D.; Ahmed, F. A new calibration for Function Point complexity weights. Inf. Softw. Technol.
**2008**, 50, 670–683. [Google Scholar] [CrossRef] [Green Version] - Shukla, S.; Kumar, S. Applicability of neural network based models for software effort estimation. In Proceedings of the 2019 IEEE World Congress on Services (SERVICES), Milan, Italy, 8–13 July 2019; IEEE: Milan, Italy, 2019; pp. 339–342. [Google Scholar]
- Shukla, S.; Kumar, S.; Bal, P.R. Analyzing effect of ensemble models on multi-layer perceptron network for software effort estimation. In Proceedings of the 2019 IEEE World Congress on Services (SERVICES), Milan, Italy, 8–13 July 2019; IEEE: Milan, Italy, 2019; pp. 386–387. [Google Scholar]
- Priya, V.A.G.; Anitha, K.; Varadarajan, V. Estimating software development efforts using a random forest-based stacked ensemble approach. Electronics
**2021**, 10, 1195. [Google Scholar] [CrossRef] - Idri, A.; Hosni, M.; Abran, A. Systematic literature review of ensemble effort estimation. J. Syst. Softw.
**2016**, 118, 151–175. [Google Scholar] [CrossRef] - Idri, A.; Hosni, M.; Abran, A. Systematic mapping study of ensemble effort estimation. In Proceedings of the 11th International Conference on Evaluation of Novel Software Approaches to Software Engineering, Rome, Italy, 27–28 April 2016; pp. 132–139. [Google Scholar]
- International Software Benchmarking Standards Groupm. ISBSG Repository August 2020 R1. Available online: https://www.isbsg.org (accessed on 20 September 2021).
- Silhavy, P.; Silhavy, R.; Prokopova, Z. Categorical variable segmentation model for software development effort estimation. IEEE Access
**2019**, 7, 9618–9626. [Google Scholar] [CrossRef] - Pospieszny, P.; Czarnacka-Chrobot, B.; Kobylinski, A. An effective approach for software project effort and duration estimation with machine learning algorithms. J. Syst. Softw.
**2018**, 137, 184–196. [Google Scholar] [CrossRef] - Jayakumar, K.R.; Abran, A. Estimation models for software functional test effort. J. Softw. Eng. Appl.
**2017**, 10, 338–353. [Google Scholar] [CrossRef] [Green Version] - Wei, K.T.; Selamat, M.H.; Ghani, A.A.A.; Abdullah, R. Exponential Effort Estimation Model Using Unadjusted Function Points. In Proceedings of the 5th International Conference on New Trends in Information Science and Service Science, Macao, China, 24–26 October 2011; IEEE: Macao, China, 2011; pp. 111–115. [Google Scholar]
- Misra, S.; Adewumi, A.; Fernandez-Sanz, L.; Damasevicius, R. A Suite of Object Oriented Cognitive Complexity Metrics. IEEE Access
**2018**, 6, 8782–8796. [Google Scholar] [CrossRef] - Dewi, R.S.; Subriadi, A.P.; Sholiq, S. A modification complexity factor in function points method for software cost estimation towards public service application. Procedia Comput. Sci.
**2017**, 124, 415–422. [Google Scholar] [CrossRef] - Leal, L.Q.; Fagundes, R.A.A.; de Souza, R.M.C.R.; Moura, H.P.; Gusmao, C.M.G. Nearest-neighborhood linear regression in an application with software effort estimation. In Proceedings of the 2009 IEEE International Conference on Systems, Man and Cybernetics, San Antonio, TX, USA, 11–14 October 2009; pp. 5030–5034. [Google Scholar]
- Hamza, H.; Kamel, A.; Shams, K. Software effort estimation using artificial neural networks: A survey of the current practices. In Proceedings of the 2013 10th International Conference on Information Technology: New Generations, Las Vegas, NV, USA, 15–17 April 2013; IEEE: Las Vegas, NV, USA, 2013; pp. 731–733. [Google Scholar]
- Lenarduzzi, V.; Morasca, S.; Taibi, D. Estimating software development effort based on phases. In Proceedings of the 2014 40th EUROMICRO Conference on Software Engineering and Advanced Applications, Verona, Italy, 27–29 August 2014; IEEE: Verona, Italy, 2014; pp. 305–308. [Google Scholar]
- Prokopova, Z.; Silhavy, P.; Silhavy, R. Influence analysis of selected factors in the function point work effort estimation. In Intelligent Systems in Cybernetics and Automation Control Theory; Silhavy, R., Silhavy, P., Prokopova, Z., Eds.; Springer: Cham, Germany, 2019; pp. 112–124. [Google Scholar]
- Hammad, M.; Alqaddoumi, A. Features-level software effort estimation using machine learning algorithms. In Proceedings of the 2018 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), Sakhier, Bahrain, 18–20 November 2018; IEEE: Sakhier, Bahrain, 2018; pp. 1–3. [Google Scholar]
- Abdellatif, T.M. A comparison study between soft computing and statistical regression techniques for software effort estimation. In Proceedings of the 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE), Quebec, QC, Canada, 13–16 May 2018; IEEE: Quebec, QC, Canada, 2018; pp. 1–5. [Google Scholar]
- IFPUG. Announcing the New Business Applications Committee. Available online: https://www.ifpug.org (accessed on 20 September 2021).
- ISO/IEC 20926:2009; Software and Systems Engineering—Software Measurement—IFPUG Functional Size Measurement Method. International Organization for Standardization: Geneva, Switzerland, 2009.
- Azzeh, M.; Nassif, A.B. Analyzing the relationship between project productivity and environment factors in the use case points method. J. Softw. Evol. Process
**2017**, 29, e1882. [Google Scholar] [CrossRef] [Green Version] - Azzeh, M.; Nassif, A.B.; Banitaan, S. Comparative analysis of soft computing techniques for predicting software effort based use case points. IET Softw.
**2018**, 12, 19–29. [Google Scholar] [CrossRef] - MacKay, D.J.C. Bayesian interpolation. Neural Comput.
**1992**, 4, 415–447. [Google Scholar] [CrossRef] - Michael, E.T. Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res.
**2001**, 1, 211–244. [Google Scholar] - Park, S.Y.; Bera, A.K. Maximum entropy autoregressive conditional heteroskedasticity model. J. Econom.
**2009**, 150, 219–230. [Google Scholar] [CrossRef] - Kocaguneli, E.; Menzies, T.; Keung, J.W. On the value of ensemble effort estimation. IEEE Trans. Softw. Eng.
**2012**, 38, 1403–1416. [Google Scholar] [CrossRef] [Green Version] - Kocaguneli, E.; Kultur, Y.; Bener, A. Combining multiple learners induced on multiple datasets for software effort prediction. Int. Symp. Softw. Reliab. Eng.
**2009**, 17, 25–49. [Google Scholar] - An, K.; Meng, J. Voting-averaged combination method for regressor ensemble. In Advanced Intelligent Computing Theories and Applications; Huang, D.S., Zhao, Z., Bevilacqua, V., Figueroa, J.C., Eds.; Springer: Berlin/Heidelberg, Germany, 2010; pp. 540–546. [Google Scholar]
- Witten, I.H.; Frank, E.; Hall, M.A.; Pal, C.J. Data Mining: Practical Machine Learning Tools and Techniques; Morgan Kaufmann: Amsterdam, The Netherlands, 2017. [Google Scholar]
- Azzeh, M.; Nassif, A.B.; Minku, L.L. An empirical evaluation of ensemble adjustment methods for analogy-based effort estimation. J. Syst. Softw.
**2015**, 103, 36–52. [Google Scholar] [CrossRef] [Green Version] - Lichtenberg, J.M.; Şimşek, Ö. Simple regression models. Proc. Mach. Learn.
**2016**, 58, 13–25. [Google Scholar] - Upton, G.; Cook, I. Understanding Statistics; Oxford University Press: Oxford, UK, 1996. [Google Scholar]
- Zwillinger, D.; Kokoska, S. CRC Standard Probability and Statistics Tables and Formulae; Chapman & Hall/CRC Press: Boca Raton, FL, USA, 2000. [Google Scholar]
- Foss, T.; Stensrud, E.; Kitchenham, B.; Myrtveit, I. A simulation study of the model evaluation criterion mmre. IEEE Trans. Softw. Eng.
**2003**, 29, 985–995. [Google Scholar] [CrossRef] [Green Version] - Kitchenham, B.A.; Pickard, L.M.; MacDonell, S.G.; Shepperd, M.J. What accuracy statistics really measure. IEE Proc. Softw.
**2001**, 148, 81–85. [Google Scholar] [CrossRef] [Green Version] - Hardin, J.; Hardin, J.; Hilbe, J.; Hilbe, J. Generalized Linear Models and Extensions; Stata Press: College Station, TX, USA, 2007. [Google Scholar]
- Gupta, H.V.; Kling, H.; Yilmaz, K.K.; Martinez, G.F. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J. Hydrol.
**2009**, 377, 80–91. [Google Scholar] [CrossRef] [Green Version] - Shepperd, M.; MacDonell, S. Evaluating prediction systems in software project estimation. Inf. Softw. Technol.
**2012**, 54, 820–827. [Google Scholar] [CrossRef] [Green Version] - Kaiser, M.; Ullrich, C. Estimation accuracy in large is programs insights from a descriptive case study. In Proceedings of the 22st European Conference on Information Systems, Tel Aviv, Israel, 9–11 June 2014; pp. 1–14. [Google Scholar]
- Anderson, D.R.; Sweeney, D.J.; William, T.A. Statistics for Business and Economics, 14th ed.; Thomson South-Western, Cengage Learning: Boston, MA, USA, 2009. [Google Scholar]
- Ross, A.; Willson, V.L.. Paired Samples t-Test. In Basic and Advanced Statistical Tests; SensePublishers: Rotterdam, The Netherlands, 2017; pp. 17–19. [Google Scholar]
- Chai, T.; Draxler, R.R. Root mean square error (RMSE) or mean absolute error (MAE)?—Arguments against avoiding RMSE in the literature. Geosci. Model Dev.
**2014**, 7, 1247–1250. [Google Scholar] [CrossRef] [Green Version] - Todros, K.; Tabrikian, J. On order relations between lower bounds on the MSE of unbiased estimators. In Proceedings of the 2010 IEEE International Symposium on Information Theory, Austin, TX, USA, 13–18 June 2010; IEEE: Austin, TX, USA, 2010; pp. 1663–1667. [Google Scholar]

Component | ||||||
---|---|---|---|---|---|---|

EI | EO | EQ | EIF | ILF | ||

Complexity weight | Low | 3 | 4 | 3 | 5 | 7 |

Average | 4 | 5 | 4 | 7 | 10 | |

High | 6 | 7 | 6 | 10 | 15 |

Algorithm | Implementation | Parameters |
---|---|---|

Random forests | sklearn.ensemble.RandomForestRegressor | n_estimators = 200, random_state = 0 |

Bayesian ridge | sklearn.linear_model. BayesianRidge | n_iter = 300 |

ANN | sklearn.neural_network. MLPRegressor | tol = 0.00001, max_iter=10000, momentum = 0.000001 |

Lasso | sklearn.linear_model. Lasso | alpha = 0.01, selection = ‘random’, random_state = 63 |

IFPUG FPA | All Sectors | Banking | Communication | Financial | Government | Insurance | Manufacturing | Service Industry | Others | ||
---|---|---|---|---|---|---|---|---|---|---|---|

EI | Low | 3 | 3.48 | 1.73 | 1.10 | 3.52 | 0.41 | 2.09 | 3.49 | 3.95 | 3.17 |

Average | 4 | 1.17 | 1.21 | 3.97 | 3.98 | 7.76 | 4.18 | 0.99 | 5.50 | 1.33 | |

High | 6 | 8.35 | 7.90 | 3.86 | 4.64 | 7.04 | 4.15 | 8.54 | 6.72 | 9.55 | |

EO | Low | 4 | 3.49 | 3.06 | 3.45 | 4.9 | 2.47 | 3.78 | 3.58 | 3.83 | 3.03 |

Average | 5 | 4.42 | 5.37 | 2.54 | 2.01 | 5.59 | 4.78 | 5.28 | 4.74 | 4.20 | |

High | 7 | 4.94 | 8.09 | 7.48 | 7.06 | 6.09 | 4.80 | 6.89 | 6.37 | 7.20 | |

EQ | Low | 3 | 3.79 | 0.71 | 1.87 | 3.35 | 2.98 | 3.94 | 2.00 | 2.42 | 2.81 |

Average | 4 | 5.94 | 4.39 | 3.38 | 3.82 | 4.38 | 5.70 | 4.81 | 4.26 | 6.31 | |

High | 6 | 2.28 | 9.48 | 4.22 | 5.86 | 2.57 | 6.42 | 5.78 | 4.81 | 3.34 | |

ILF | Low | 5 | 5.13 | 6.26 | 4.77 | 4.28 | 8.22 | 4.96 | 5.52 | 3.07 | 3.20 |

Average | 7 | 7.22 | 2.24 | 10.60 | 8.92 | 5.31 | 4.14 | 7.99 | 3.53 | 9.93 | |

High | 10 | 9.58 | 15.96 | 9.72 | 8.22 | 7.24 | 10.72 | 6.38 | 5.82 | 11.44 | |

EIF | Low | 7 | 7.13 | 2.23 | 7.22 | 8.97 | 6.20 | 11.54 | 8.55 | 5.82 | 6.61 |

Average | 10 | 7.04 | 10.16 | 9.72 | 12.46 | 12.84 | 2.48 | 1.88 | 17.32 | 4.40 | |

High | 15 | 15.75 | 24.9 | 7.68 | 9.48 | 18.33 | 9.24 | 22.41 | 16.59 | 12.90 |

MAE | Improvement of CFCW vs. IFPUG FPA (%) | Improvement of CFCW vs. NFFPCM (%) | Improvement of CFCWO vs. CFCW (%) | ||||
---|---|---|---|---|---|---|---|

IFPUG FPA | NFFPCM | CFCW | CFCWO | ||||

All Sectors | 678.72 | 758.42 | 641.68 | 565.37 | 5.46 | 15.39 | 11.89 |

Banking | 463.96 | 493.09 | 301.02 | 244.76 | 35.12 | 38.95 | 18.69 |

Communication | 422.76 | 322.31 | 204.82 | 191.73 | 51.55 | 36.45 | 6.39 |

Financial | 210.16 | 569.07 | 169.11 | 131.60 | 19.53 | 70.28 | 22.18 |

Government | 513.30 | 1012.22 | 480.05 | 450.62 | 6.48 | 52.57 | 6.13 |

Insurance | 417.08 | 422.45 | 325.86 | 305.46 | 21.87 | 22.86 | 6.26 |

Manufacturing | 207.29 | 398.12 | 192.84 | 167.97 | 6.97 | 51.56 | 12.90 |

Service Industry | 319.78 | 1051.87 | 294.48 | 257.72 | 7.91 | 72.00 | 12.48 |

Others | 344.90 | 619.79 | 278.00 | 247.53 | 19.40 | 55.15 | 10.96 |

Mean (Sectors) | 362.40 | 611.12 | 280.77 | 249.67 | 22.52 | 49.98 | 11.08 |

IFPUG FPA (%) | NFFPCM (%) | CFCW (%) | CFCWO (%) | |
---|---|---|---|---|

Banking | 31.64 | 34.98 | 53.09 | 56.71 |

Communication | 37.71 | 57.50 | 68.08 | 66.09 |

Financial | 69.04 | 24.97 | 73.65 | 76.72 |

Government | 24.37 | −33.46 | 25.19 | 20.3 |

Insurance | 38.55 | 44.30 | 49.22 | 45.97 |

Manufacturing | 69.46 | 47.51 | 69.95 | 70.29 |

Service Industry | 52.89 | −38.69 | 54.11 | 54.42 |

Others | 49.18 | 18.28 | 56.68 | 56.22 |

Mean | 46.61 | 19.42 | 56.25 | 55.84 |

MAPE | Improvement of CFCW vs. IFPUG FPA (%) | Improvement of CFCW vs. NFFPCM (%) | Impprovement of CFCWO vs. CFCW (%) | ||||
---|---|---|---|---|---|---|---|

IFPUG FPA | NFFPCM | CFCW | CFCWO | ||||

All Sectors | 14.18 | 19.01 | 13.61 | 12.72 | 4.01 | 28.41 | 6.56 |

Banking | 10.63 | 13.43 | 7.36 | 6.28 | 30.76 | 45.20 | 14.67 |

Communication | 13.99 | 13.81 | 10.09 | 8.22 | 27.94 | 26.94 | 18.53 |

Financial | 9.08 | 24.48 | 7.91 | 7.15 | 12.89 | 67.69 | 9.61 |

Government | 7.40 | 19.21 | 7.32 | 7.02 | 1.08 | 61.89 | 4.10 |

Insurance | 11.17 | 12.75 | 9.52 | 8.99 | 14.77 | 25.33 | 5.57 |

Manufacturing | 11.19 | 19.73 | 10.42 | 9.59 | 6.88 | 47.19 | 7.97 |

Service Industry | 7.41 | 16.93 | 6.64 | 5.94 | 10.39 | 60.78 | 10.54 |

Others | 9.57 | 18.94 | 8.10 | 7.71 | 15.36 | 57.23 | 4.81 |

Mean (Sectors) | 10.06 | 17.41 | 8.42 | 7.61 | 16.26 | 49.03 | 9.59 |

IFPUG FPA (%) | NFFPCM (%) | CFCW (%) | CFCWO (%) | |
---|---|---|---|---|

Banking | 25.08 | 29.35 | 45.94 | 50.46 |

Communication | 1.30 | 27.35 | 25.92 | 35.38 |

Financial | 35.99 | −28.77 | 41.88 | 43.81 |

Government | 47.82 | −1.05 | 46.26 | 44.78 |

Insurance | 21.26 | 32.93 | 30.04 | 29.47 |

Manufacturing | 21.08 | −3.79 | 23.44 | 24.66 |

Service Industry | 47.73 | 10.94 | 51.24 | 53.77 |

Others | 32.5 | 0.37 | 40.52 | 39.21 |

Mean | 29.10 | 8.42 | 38.16 | 40.19 |

RMSE | Improvement of CFCW vs. IFPUG FPA (%) | Improvement of CFCW vs. NFFPCM (%) | Improvement of CFCWO vs. CFCW (%) | ||||
---|---|---|---|---|---|---|---|

FPA | NFFPCM | CFCW | CFCWO | ||||

All Sectors | 1578.92 | 1766.29 | 1414.83 | 1066.47 | 10.39 | 19.90 | 24.62 |

Banking | 828.43 | 641.44 | 441.71 | 323.47 | 46.68 | 31.14 | 26.77 |

Communication | 585.70 | 430.01 | 268.52 | 254.77 | 54.15 | 37.55 | 5.12 |

Financial | 304.54 | 694.71 | 226.76 | 206.52 | 25.54 | 67.36 | 8.92 |

Government | 1259.87 | 1219.93 | 1149.40 | 1042.79 | 8.77 | 5.78 | 9.28 |

Insurance | 691.23 | 537.50 | 502.79 | 476.84 | 27.26 | 6.46 | 5.16 |

Manufacturing | 362.54 | 474.34 | 311.59 | 232.82 | 14.05 | 34.31 | 25.28 |

Service Industry | 461.49 | 1314.06 | 370.71 | 340.87 | 19.67 | 71.79 | 8.05 |

Others | 598.41 | 842.07 | 407.48 | 349.25 | 31.91 | 51.61 | 14.29 |

Mean (Sectors) | 636.52 | 769.26 | 459.87 | 403.42 | 27.75 | 38.25 | 12.28 |

IFPUG FPA (%) | NFFPCM (%) | CFCW (%) | CFCWO (%) | |
---|---|---|---|---|

Banking | 47.53 | 63.68 | 68.78 | 69.67 |

Communication | 62.91 | 75.65 | 81.02 | 76.11 |

Financial | 80.71 | 60.67 | 83.97 | 80.63 |

Government | 20.21 | 30.93 | 18.76 | 2.22 |

Insurance | 56.22 | 69.57 | 64.46 | 55.29 |

Manufacturing | 77.04 | 73.14 | 77.98 | 78.17 |

Service Industry | 70.77 | 25.60 | 73.80 | 68.04 |

Others | 62.10 | 52.33 | 71.20 | 67.25 |

Mean | 59.69 | 56.45 | 67.50 | 62.17 |

Pairs of Methods | CFCW VS. FPA | CFCWO VS. CFCW | CFCWO VS. NFFPCM | |
---|---|---|---|---|

MAE results | Mean MAE | 280.77 vs. 362.4 | 249.67 vs. 280.77 | 10.06 vs. 611.12 |

Mean p-value | 0.00787 | 0.00013 | 0.00156 | |

Statistical conclusion | >> | >> | >> | |

MAPE results | Mean MAPE | 8.42 vs. 10.06 | 7.61 vs. 8.42 | 636.52 vs. 17.41 |

Mean p-value | 0.00475 | 0.00124 | 0.00017 | |

Statistical conclusion | >> | >> | >> | |

RMSE results | Mean SE | 459.87 vs. 636.52 | 403.42 vs. 459.87 | 403.42 vs. 769.26 |

Mean p-value | 0.00215 | 0.00287 | 0.00445 | |

Statistical conclusion | >> | >> | >> |

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**MDPI and ACS Style**

Hai, V.V.; Nhung, H.L.T.K.; Prokopova, Z.; Silhavy, R.; Silhavy, P.
A New Approach to Calibrating Functional Complexity Weight in Software Development Effort Estimation. *Computers* **2022**, *11*, 15.
https://doi.org/10.3390/computers11020015

**AMA Style**

Hai VV, Nhung HLTK, Prokopova Z, Silhavy R, Silhavy P.
A New Approach to Calibrating Functional Complexity Weight in Software Development Effort Estimation. *Computers*. 2022; 11(2):15.
https://doi.org/10.3390/computers11020015

**Chicago/Turabian Style**

Hai, Vo Van, Ho Le Thi Kim Nhung, Zdenka Prokopova, Radek Silhavy, and Petr Silhavy.
2022. "A New Approach to Calibrating Functional Complexity Weight in Software Development Effort Estimation" *Computers* 11, no. 2: 15.
https://doi.org/10.3390/computers11020015