Using Actors and Use Cases for Software Size Estimation

Software size estimation represents a complex task, which is based on data analysis or on an algorithmic estimation approach. Software size estimation is a nontrivial task, which is important for software project planning and management. In this paper, a new method called Actors and Use Cases Size Estimation is proposed. The new method is based on the number of actors and use cases only. The method is based on stepwise regression and led to a very significant reduction in errors when estimating the size of software systems compared to Use Case Points-based methods. The proposed method is independent of Use Case Points, which allows the elimination of the effect of the inaccurate determination of Use Case Points components, because such components are not used in the proposed method.

[1]  Emilia Mendes,et al.  Investigating the use of moving windows to improve software effort prediction: a replicated study , 2017, Empirical Software Engineering.

[2]  Da Yang,et al.  COCOMO-U: An Extension of COCOMO II for Cost Estimation with Uncertainty , 2006, SPW/ProSim.

[3]  Zdenka Prokopova,et al.  Analysis and selection of a regression model for the Use Case Points method using a stepwise approach , 2017, J. Syst. Softw..

[4]  Zdenka Prokopova,et al.  Applied Least Square Regression in Use Case Estimation Precision Tuning , 2015, CSOC.

[5]  Miroslaw Ochodek,et al.  HAZOP-based identification of events in use cases , 2013, Empirical Software Engineering.

[6]  Zdenka Prokopova,et al.  Evaluation of Data Clustering for Stepwise Linear Regression on Use Case Points Estimation , 2017, CSOC.

[7]  Martin J. Shepperd,et al.  Estimating Software Project Effort Using Analogies , 1997, IEEE Trans. Software Eng..

[8]  Fadi Almasalha,et al.  Pareto efficient multi-objective optimization for local tuning of analogy-based estimation , 2016, Neural Computing and Applications.

[9]  Alain Abran,et al.  Analogy-based software development effort estimation: A systematic mapping and review , 2015, Inf. Softw. Technol..

[10]  Stephen G. MacDonell,et al.  Evaluating prediction systems in software project estimation , 2012, Inf. Softw. Technol..

[11]  Miroslaw Ochodek,et al.  Simplifying effort estimation based on Use Case Points , 2011, Inf. Softw. Technol..

[12]  Danny Ho,et al.  Fuzzy Rules for Risk Assessment and Contingency Estimation Within COCOMO Software Project Planning Model , 2014 .

[13]  Hugo Ribeiro,et al.  On the refinement of use case models with variability support , 2011, Innovations in Systems and Software Engineering.

[14]  Zdenka Prokopova,et al.  Evaluating subset selection methods for use case points estimation , 2017, Inf. Softw. Technol..

[15]  Fatih Yücalar,et al.  A case study for the software size estimation through MK II FPA and FP methods , 2016, Int. J. Comput. Appl. Technol..

[16]  Tomás Urbánek,et al.  On the Value of Parameters of Use Case Points Method , 2015, CSOC.

[17]  Sergey Diev.  Use cases modeling and software estimation: applying use case points , 2006, SOEN.

[18]  Danny Ho,et al.  Towards an early software estimation using log-linear regression and a multilayer perceptron model , 2013, J. Syst. Softw..

[19]  Nasser Ghasem-Aghaee,et al.  Fuzzy Emotional COCOMO II Software Cost Estimation (FECSCE) using Multi-Agent Systems , 2011, Appl. Soft Comput..

[20]  Wolfgang Emmerich,et al.  Literate Modelling - Capturing Business Knowledge with the UML , 1998, UML.

[21]  Jim Arlow.  Use cases, UML visual modelling and the trivialisation of business requirements , 2008, Requirements Engineering.

[22]  Zdenka Prokopova,et al.  Improving Algorithmic Optimisation Method by Spectral Clustering , 2017, CSOC.

[23]  Lefteris Angelis,et al.  Visual comparison of software cost estimation models by regression error characteristic analysis , 2010, J. Syst. Softw..

[24]  Dov Dori.  SysML: Foundations and Diagrams , 2016 .

[25]  Zdenka Prokopova,et al.  Categorical Variable Segmentation Model for Software Development Effort Estimation , 2019, IEEE Access.

[26]  Alain Abran,et al.  Software Development Effort Estimation Using Regression Fuzzy Models , 2019, Comput. Intell. Neurosci..

[27]  Zdenka Prokopova,et al.  Algorithmic Optimisation Method for Improving Use Case Points Estimation , 2015, PloS one.

[28]  Radek Silhavy.  Use Case Points Benchmark Dataset , 2017 .

[29]  Miroslaw Ochodek,et al.  Improving the reliability of transaction identification in use cases , 2011, Inf. Softw. Technol..