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Applied Sciences
, Volume 11 (19) – Sep 22, 2021

/lp/multidisciplinary-digital-publishing-institute/a-novel-ucp-model-based-on-artificial-neural-networks-and-orthogonal-gREcizHQzJ

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Article A Novel UCP Model Based on Artificial Neural Networks and Orthogonal Arrays 1, 1 2 1 Nevena Rankovic *, Dragica Rankovic , Mirjana Ivanovic and Ljubomir Lazic School of Computing, Union University, 11000 Belgrade, Serbia; nrankovic@raf.rs (N.R.); drankovic@raf.rs (D.R.); ljlazic@raf.rs (L.L.) Faculty of Sciences, University of Novi Sad, 21000 Novi Sad, Serbia; mira@dmi.uns.ac.rs * Correspondence: nrankovic@raf.rs Abstract: Adequate estimation is a crucial factor for the implementation of software projects within set customer requirements. The use of Case Point Analysis (UCP) is the latest and most accurate method for estimating the effort and cost of realizing software products. This paper will present a new, improved UCP model constructed based on two different artificial neural network (ANN) ar- chitectures based on Taguchi Orthogonal Vector Plans. ANNs are an exceptional artificial intelli- gence tool that have been proven to be reliable and stable in this area of software engineering. The Taguchi method of Orthogonal Vector Plans is an optimization method that reduces the number of iterations required, which significantly shortens estimation time. The goal is to construct models that give a minimum magnitude relative error (MRE) value concerning previous approaches and techniques. A minimum number of iterations (less than six) and a minimum value of MMRE (less than 10%) have been achieved. The obtained results significantly improve the accuracy and reliabil- ity of estimating the effort and cost involved in the implementation of software projects. Keywords: software development estimation; Use Case Point Analysis; orthogonal array-based ex- periment; artificial neural networks design Citation: Rankovic, N.; Rankovic, D.; Ivanovic, M.; Lazic, L. A Novel UCP Model Based on Artificial Neural Networks and Orthogonal Arrays. 1. Introduction Appl. Sci. 2021, 11, 8799. https://doi.org/10.3390/app11198799 The most important activity in the software development process is the assessment of effort, which includes assessing the time and money required for the software project Academic Editor: Rocco Furferi to be successfully completed. Project development time is critical, both for project clients and project implementers. The amount of money required for investment in a project in- Received: 22 August 2021 fluences whether the project will be started or not, and whether it will end within the set Accepted: 17 September 2021 framework. Software companies use a variety of software tools and services to meet cus- Published: 22 September 2021 tomer requirements. Many methods measure the size of software, its complexity, and the time needed to build it. All methods can be divided according to whether they are para- Publisher’s Note: MDPI stays neu- metric or nonparametric [1–3]. Within this division, there are several different ap- tral with regard to jurisdictional proaches, three of which are the most commonly used: claims in published maps and institu- tional affiliations. 1. An approach based on analyzing the number of source code lines and estimating the effort required to implement the project. The most commonly used model of this ap- proach is the Constructive Cost Model (COCOMO2000) [4,5]. 2. An approach based on the analysis of functional points to estimate the magnitude of Copyright: © 2021 by the authors. Li- the functionality of the software being developed. Within this approach, two models censee MDPI, Basel, Switzerland. were initially distinguished: IFPUG (created by the International Function Point Us- This article is an open access article ers Group) [6] and Mk II (Mark II) [7]. Subsequently, within the IFPUG model, the distributed under the terms and con- following were developed: NESMA NESMA (created by the Netherlands Software ditions of the Creative Commons At- Metrics Association) [8], IFPUG 4.1, and COSMIC FP (the COmmon Software Meas- tribution (CC BY) license (http://crea- urement International Consortium function point) [9]. tivecommons.org/licenses/by/4.0/). Appl. Sci. 2021, 11, 8799. https://doi.org/10.3390/app11198799 www.mdpi.com/journal/applsci Appl. Sci. 2021, 11, 8799 2 of 19 3. An approach based on the analysis of users and use cases for the assessment of effort. Within this approach, the most commonly used models are: COBRA (COnstraint- Based Reconstruction and Analysis) [10] and UCP (Use Case Point Analysis) [11]. 1.1. Use Case Point Analysis The UCP method is most often used for estimating the real size of a software project. This method of estimating the effort required to implement a particular system considers the use cases of the system. It analyzes system users and different scenarios to adequately assess the effort required. It uses twenty-one parameters for assessment, of which thirteen parameters are technical characteristics of the system, and the remaining eight are envi- ronmental factors [12–15]. The technical characteristics of the system being evaluated are as follows: distributed system, system response time, efficiency, complexity of internal processes, code reuse, ease of installation, ease of use, transfer to other platforms, system maintenance, compet- itiveness, parallel processing, security requirements, access to external systems, and end- user training. The following environmental factors are assessed: compliance with the used devel- opment process, experience with applications, experience in object-oriented technologies, ability of the chief analyst, team motivation, stability requirements, adaptation of working hours of team members, and complexity of the programming language. The system users and use cases are used together to determine the real size of the UCP method. The users of the system are divided into three groups: simple (depending on the interaction with the system, they are assigned a weight factor of (1); medium (de- pending on internal/external communication, they are assigned a weight factor of (2); and complex (depending on the complexity of interactions, they are assigned a weight factor of (3). There are also three categories of use cases that are defined based on the number of transactions executed (number of users and system messaging): simple (for less than three transactions, with weighting factor of five assigned); medium (from 4 to 7 transactions, with weighting factor of 10 assigned); and complex (more than eight transactions, with a weighting factor of 15 assigned). The size of the system is defined based on a six-dimensional vector whose elements represent the complexity of the previously mentioned users and the cases of users in the system. The estimated value is calculated based on the formulas established by G. Karner [16]: UAW (Unadjusted Actor Weight)—this input value is a functional point that can de- termine the level of complexity of system users. Users can be simple system operators or other external systems. Each user is ranked according to their level of complexity and can be: Simple, Average, and Complex (1)–(4). SimpleA = ∑(𝑜𝑟𝑆𝑖𝑚𝑝𝑙𝑒𝐴𝑐𝑡 ) ∗ 𝑖𝑚𝑝𝑙𝑒𝑊𝑒𝑖𝑔ℎ𝑡 𝑆 , where SimpleWeight = 1; (1) ∑( ) AverageA = 𝐴 𝑜𝑟𝑐𝑡 ∗ 𝐴 ℎ𝑡 𝑔𝑣𝑒𝑟𝑎𝑔𝑒𝑊𝑒𝑖 , where AverageWeight = 2; (2) ComplexA = ∑(𝑐𝑡𝑜𝑟𝑒𝑥𝐴𝐶𝑜𝑚𝑝𝑙 ) ∗𝐶𝑒ℎ 𝑖𝑔𝑥𝑊𝑙𝑜𝑝𝑚𝑒 𝑡 , where ComplexWeight = 3; (3) UAW = SimpleA + AverageA + ComplexA (4) UUCW (Unadjusted Use Case Weight)—this input value is a functional point that can determine the level of complexity of use cases. Each use case is ranked according to its level of complexity and can be: Simple, Average and Complex (5)–(8). ∑( ) SimpleUUCW = ∗ 𝑔ℎ𝑡 𝑖𝑚𝑝𝑙𝑒𝑊𝑒𝑖𝑆 , where SimpleWeight = 5, (transactions ≤ 3, analysis classes < 5) (5) 𝑆𝑖𝑚𝑝𝑙𝑒𝑈𝐶𝑊 𝑣𝑒𝑟𝑎𝑔𝑒𝐴 Appl. Sci. 2021, 11, 8799 3 of 19 AverageUUCW = ∑(𝐴 𝐶𝑊 ) ∗ 𝐴 𝑣𝑒𝑟𝑎𝑔𝑒𝑊𝑒𝑖𝑔ℎ𝑡 , where AverageWeight = 10, (4 ≤ transactions ≤ 7, 5 ≤ analysis classes ≤ 10) (6) ∑( ) ComplexUUCW = 𝑊𝑒𝑥𝑈𝐶𝐶𝑜𝑚𝑝𝑙 ×𝐶𝑒ℎ 𝑖𝑔𝑥𝑊𝑙𝑜𝑝𝑚𝑒 𝑡 , where ComplexWeight = 15; (transactions > 7, analysis classes ≥ 10) (7) UUCW = SimpleUUCW + AverageUUCW + ComplexUUCW (8) UUCP (Unadjusted Use Case Points) is determined by following Equation (9): UUCP = UUCW + UAW (9) TCF (Technical Complexity Factor) is an estimate of the technical complexity of the system and can be described by the following Equations (10) and (11): TCF = 0.6 + (0.01 × FactorT) (10) FactorT = ∑ 𝑊𝑒𝑖𝑔ℎ𝑡 ∗ 𝐴 , (11) where AssignedValue is from 0 to 5 and represents a technical factor of the estimated pro- cess. ECF (Environmental Complexity Factor) is one of the factors affecting the size of the project expressed in Use Case points. It is calculated according to the following Equations (12) and (13): ECF = 1.4 + (−0.03 × FactorE) (12) FactorE = ∑ 𝑖𝑔ℎ𝑡 𝑊𝑒 ∗ 𝐴 , (13) where AssignedValue is from 0 to 5 and represents an environmental factor of the esti- mated process. AUCP (Adjusted Use Case Point) is the final size of the system expressed in Use Case points and is calculated as follows (14): AUCP = UUCP × TCF × ECF (14) Real effort is represented, via the UCP approach, as a six-dimensional vector, where its value is calculated as the norm of the vector as follows (15), (16): UCP = (UAW, UUCW, UUCP, TCF, ECF, AUCP) (15) (16) 𝑈𝐶𝑃 = 𝑈𝐴𝑊 + 𝑈𝑈𝐶𝑊 + 𝑈𝑈𝐶𝑃 + 𝑇𝐶𝐹 + 𝐸𝐶𝐹 + 𝐴 𝑈𝐶𝑃 Real effort is represented, via the UCP approach, as a four-dimensional vector, where its value is calculated as the norm of the vector as follows (17), (18): UCP = (UAW, UUCW, TCF, ECF) (17) 𝑈𝐶𝑃 = 𝑈𝐴𝑊 + 𝑈𝑈𝐶𝑊 + 𝑇𝐶𝐹 + 𝐸𝐶𝐹 (18) where UUCP= UAW+ UUCW, and AUCP = UUCP × TXF × ECF. In both cases, Real Effort is obtained as the norm of the UCP vector and represents the real functional size or number of points of use cases. This method is currently most commonly used to assess effort [17], although it is not standardized within ISO standards such as the previous two. 𝑠𝑠𝑖𝑔𝑛𝑒𝑑𝑉𝑎𝑙𝑢𝑒 𝑠𝑠𝑖𝑔𝑛𝑒𝑑𝑉𝑎𝑙𝑢𝑒 𝑣𝑒𝑟𝑎𝑔𝑒𝑈 Appl. Sci. 2021, 11, 8799 4 of 19 1.2. Taguchi Orthogonal Arrays This paper aims to present a new, improved UCP model constructed using artificial neural networks based on Taguchi’s orthogonal vector plans. ANNs represent a tremen- dous artificial intelligence tool and are often used in combination with parametric meth- ods [18,19]. Using various ANN architectures, we can arrive at a fast, accurate, and reliable estimate of the effort and cost required to develop a project. Each ANN architecture is based on robust design methods, i.e., Taguchi methods of orthogonal vector plans. A ro- bust method design implies meeting the prescribed criteria when planning and imple- menting software. Taguchi’s orthogonal vector plans are based on a unique set of Latin Squares [20,21]. The discovery of orthogonal vector plans and their application minimizes crucial parameters for the project’s successful development. The impact parameters are not duplicated, which achieves a much faster estimation of the efforts and costs of a par- ticular project. This design method is based on a factorial experiment realized only with all possible experimental combinations of parameter values. The construction of, e.g., the artificial neural ANN-L36prim architecture using Taguchi’s orthogonal plan achieves a higher convergence rate, reducing the time and number of iterations required to achieve the minimum MMRE. The number of iterations required for the implementation of the Full Factorial Plan (FFP) within a robustly designed experiment is N = L (for example, when three levels with 16 parameters are used according to FFP, it is necessary to execute 11 4 1 N = 3 2 3 = 8,503,056.00 experiments). Using the Taguchi orthogonal vector plan with 16 parameters (weight coefficients) on three levels, only 36 experiments are necessary. The Taguchi method of robust design reduces experiments by 99.99% (0.9999957662... = 1 − (36/8 503 056)). It is expected that the new, improved UCP approach constructed based on different ANN architectures that are in line with the Taguchi Orthogonal Plans will give better results than the previously proposed UCP model. This article is structured as follows: Section 2 provides an overview of previous stud- ies that applied UCP for effort estimation in software projects. Section 3 explains the new, improved UCP model with the methodology used. Section 4 discusses the obtained re- sults. The concluding remarks are given in the last section. 2. Related Work The UCP method is the latest and the most widespread method for estimating the effort and costs involved in the realization of software products. The most significant ad- vantage of this method is that the lowest values of relative error in estimation are ob- tained—between 20% and 35%. The best result achieved by this method is an error value of about 10% [22]. Many researchers [17,23–25] have combined this method with other parametric models and models of artificial intelligence. In a previous study [26], the UCP method was used for the estimation of size and effort for mobile applications. Android mobile applications were considered as a case study, and modified UCP was also pro- posed. The authors of [14] proposed a framework for UCP-based techniques to promote reusability in the development of software applications. The results showed that the framework met five quality attributes, and that it can be used in the early stages of soft- ware development. In [27], a systematic review of studies with the best practices in terms of use case point (UCP) and expert judgment-based effort estimation techniques was given. The authors of [28] presented the results of four different models that include the UCP method and Neuro-Fuzzy logic. It was concluded that the Neuro-fuzzy logic model using revised use case points and modified environmental gives the best fitting accuracy at an early stage compared to other models. In another relevant study [29], the authors compared the benefits of statistical analyses of effort estimation approaches for seven real- world software development projects. In addition, they contrasted a conventional Use Case points method with iUCP, an HCI (Human-centric)-enhanced model. Furthermore, they proposed an enhancement of the original iUCP effort estimation formula. Appl. Sci. 2021, 11, 8799 5 of 19 The critical decisions that defined the new, improved model within the UCP ap- proach were as follows: • Examination of the influence of two linearly dependent input values (UUCP and AUCP) on the change in the MMRE value; • Comparative analysis of two different architectures of artificial neural networks and the obtained results; • Division of the used dataset to a scale of 70:30, i.e., 70 projects from the selected da- taset were used for the training process, while 30 were used for the testing process; • Finding the most efficient methods of encoding and decoding input values, such as the fuzzification method; • The requirement of a minimum number of performed experiments; • Testing and validation on other datasets. 3. New, Improved UCP-Our Approach For the new, improved UCP model, the following architectures and corresponding orthogonal vector plans are used: 1. UCP and ANN-L16 The first proposed architecture is denoted as ANN-L16. It consists of six input values, one hidden layer with two nodes, one output, and a total number of fifteen weighting factors (𝑊𝑖 , i = 1, 15), whose initial values are from the interval [−1, 1]. The Taguchi Or- thogonal Array used in the construction of this proposed architecture contains two levels, L1 and L2 (Figure 1; Table 1) [2,3,30,31]. Figure 1. ANN architecture with one hidden layer (ANN-L16). Table 1. Taguchi Orthogonal Array (L16 = 2 ). ANN-L16 W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 W13 W14 W15 ANN1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 ANN2 L1 L1 L1 L1 L1 L1 L1 L2 L2 L2 L2 L2 L2 L2 L2 ANN3 L1 L1 L1 L2 L2 L2 L2 L1 L1 L1 L1 L2 L2 L2 L2 ANN4 L1 L1 L1 L1 L2 L2 L2 L2 L2 L2 L2 L1 L1 L1 L1 ANN5 L1 L2 L2 L1 L1 L2 L2 L1 L1 L2 L2 L1 L1 L2 L2 ANN6 L1 L2 L2 L1 L1 L2 L2 L2 L2 L1 L1 L2 L2 L1 L1 ANN7 L1 L2 L2 L2 L2 L1 L1 L1 L1 L2 L2 L2 L2 L1 L1 ANN8 L1 L2 L2 L2 L2 L1 L1 L2 L2 L1 L1 L1 L1 L2 L2 Appl. Sci. 2021, 11, 8799 6 of 19 ANN9 L2 L1 L2 L1 L2 L1 L2 L1 L2 L1 L2 L1 L2 L1 L2 ANN10 L2 L1 L2 L1 L2 L1 L2 L2 L1 L2 L1 L2 L1 L2 L1 ANN11 L2 L1 L2 L2 L1 L2 L1 L1 L2 L1 L2 L2 L1 L2 L1 ANN12 L2 L1 L2 L2 L1 L2 L1 L2 L1 L2 L1 L1 L2 L1 L2 ANN13 L2 L2 L1 L1 L2 L2 L1 L1 L2 L2 L1 L1 L2 L2 L1 ANN14 L2 L2 L1 L1 L2 L2 L1 L2 L1 L1 L2 L2 L1 L1 L2 ANN15 L2 L2 L1 L2 L1 L1 L2 L1 L2 L2 L1 L2 L1 L1 L2 ANN16 L2 L2 L1 L2 L1 L1 L2 L2 L1 L1 L2 L1 L2 L2 L1 2. UCP and ANN-L36prim The second proposed architecture is denoted as ANN-L36prim. It consists of four input values, one hidden layer with three nodes, one output, and a total number of sixteen weighting factors (𝑊𝑖 , i = 1, 16), whose initial values are from the interval [−1, 0, 1]. The Taguchi Orthogonal Array used in the construction of this proposed architecture is com- bined, where the first eleven parameters and the last sixteenth parameter are with three levels, L1, L2, and L3, while the remaining four parameters are with two levels, L1 and L2 (Figure 2; Table 2) [2,3,30,31]. Figure 2. ANN architecture with one hidden layer (ANN-L36prim). 11 4 1 Table 2. Taguchi Orthogonal Array (L36prim = 3 2 3 ). ANN-L36prim W 1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 W13 W14 W15 W16 ANN1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 ANN2 L2 L2 L2 L2 L2 L2 L2 L2 L2 L2 L2 L2 L1 L1 L1 L1 ANN3 L3 L3 L3 L3 L3 L3 L3 L3 L3 L3 L3 L3 L1 L1 L1 L1 ANN4 L1 L1 L1 L1 L2 L2 L2 L2 L3 L3 L3 L3 L1 L2 L2 L1 ANN5 L1 L1 L1 L1 L3 L3 L3 L3 L2 L2 L2 L2 L1 L2 L2 L1 ANN6 L3 L3 L3 L3 L1 L1 L1 L1 L2 L2 L2 L2 L1 L2 L2 L1 ANN7 L1 L1 L2 L3 L1 L2 L3 L3 L1 L1 L1 L3 L2 L1 L2 L1 ANN8 L2 L2 L3 L1 L2 L3 L1 L1 L2 L3 L3 L1 L2 L1 L2 L1 ANN9 L3 L3 L1 L2 L3 L1 L2 L2 L3 L1 L1 L2 L2 L1 L2 L1 ANN10 L1 L1 L3 L2 L1 L3 L2 L3 L2 L1 L3 L2 L2 L2 L1 L1 ANN11 L2 L2 L1 L3 L2 L1 L3 L1 L3 L2 L1 L3 L2 L2 L1 L1 ANN12 L3 L3 L2 L1 L3 L2 L1 L2 L1 L3 L2 L1 L2 L2 L1 L1 ANN13 L1 L2 L3 L1 L3 L2 L1 L3 L3 L2 L1 L2 L1 L1 L1 L2 ANN14 L2 L3 L1 L2 L1 L3 L2 L1 L1 L3 L2 L3 L1 L1 L1 L2 ANN15 L3 L1 L2 L3 L2 L1 L3 L2 L2 L1 L3 L1 L1 L1 L1 L2 Appl. Sci. 2021, 11, 8799 7 of 19 ANN16 L1 L2 L3 L2 L1 L1 L3 L2 L3 L3 L2 L1 L1 L2 L2 L2 ANN17 L2 L3 L1 L3 L2 L2 L1 L3 L1 L1 L3 L2 L1 L2 L2 L2 ANN18 L3 L1 L2 L1 L3 L3 L2 L1 L2 L2 L1 L3 L1 L2 L2 L2 ANN19 L1 L2 L1 L3 L3 L3 L1 L2 L2 L1 L2 L3 L2 L1 L2 L2 ANN20 L2 L3 L2 L1 L1 L1 L2 L3 L3 L2 L3 L1 L2 L1 L2 L2 ANN21 L3 L1 L3 L2 L2 L2 L3 L1 L1 L3 L1 L2 L2 L1 L2 L2 ANN22 L1 L2 L2 L3 L3 L1 L2 L1 L1 L3 L3 L2 L2 L2 L1 L2 ANN23 L2 L3 L3 L1 L1 L2 L3 L2 L2 L1 L1 L3 L2 L2 L1 L2 ANN24 L3 L1 L1 L2 L2 L3 L1 L3 L3 L2 L2 L1 L2 L2 L1 L2 ANN25 L1 L3 L2 L1 L2 L3 L3 L1 L3 L1 L2 L2 L1 L1 L1 L3 ANN26 L2 L1 L3 L2 L3 L1 L1 L2 L1 L2 L3 L3 L1 L1 L1 L3 ANN27 L3 L2 L1 L3 L1 L2 L2 L3 L2 L3 L1 L1 L1 L1 L1 L3 ANN28 L1 L3 L2 L2 L2 L1 L1 L3 L2 L3 L1 L3 L1 L2 L2 L3 ANN29 L2 L1 L3 L3 L3 L2 L2 L1 L3 L1 L2 L1 L1 L2 L2 L3 ANN30 L3 L2 L1 L1 L1 L3 L3 L2 L1 L2 L3 L2 L1 L2 L2 L3 ANN31 L1 L3 L3 L3 L2 L3 L2 L2 L1 L2 L1 L1 L2 L1 L2 L3 ANN32 L2 L1 L1 L1 L3 L1 L3 L3 L3 L3 L2 L2 L2 L1 L2 L3 ANN33 L3 L2 L2 L2 L1 L2 L1 L1 L3 L1 L3 L3 L2 L1 L2 L3 ANN34 L1 L3 L1 L2 L3 L2 L3 L1 L2 L2 L3 L1 L2 L2 L1 L3 ANN35 L2 L1 L2 L3 L1 L3 L1 L2 L3 L3 L1 L2 L2 L2 L1 L3 ANN36 L3 L2 L3 L1 L2 L1 L2 L3 L1 L1 L2 L3 L2 L2 L1 L3 The experiment presented in this paper consists of three parts: 1. Training of two different ANN architectures constructed according to the corre- sponding Taguchi orthogonal vector plans (ANN-L16 and ANN36prim); 2. Testing of the ANN that gave the best results (the lowest MMRE value) in the first part of the experiment, for two proposed architectures on the same dataset; 3. Validation of the ANN that gave the best results (the lowest MMRE value) in the first part of the experiment, for each selected architecture, but using different datasets. 3.1. Data Sets Used in the UCP Approach For the first and second part of the experiment, the Use Case Point Benchmark Da- taset by Radek Silhavy (UCP Benchmark Dataset) [32] was used. In contrast, in the third part, the combined datasets, composed of projects of different industrial companies, were used. The results in Table 3 indicate a more homogeneous structure of the projects used in all three parts of the experiment, which can be concluded based on the standard devi- ation results in Table 4. Table 3. Information on used datasets (UCP). Dataset Number of Projects Experiment Dataset_1 UCP Benchmark Dataset 50 Training Dataset_2 UCP Benchmark Dataset 21 Testing Dataset_3 Combined 18 Validation1 Dataset_4 Combined Industrial projects 17 Validation2 Table 4. Basic statistics about dataset (UCP). Std. Deviation Datasets N Min (PM) Max (PM) Mean (PM) (PM) Dataset_1 50 5775.0 7970.0 6506.940 653.0308 Dataset_2 21 6162.6 6525.3 6393.993 118.1858 Dataset_3 18 2692.1 3246.6 2988.392 233.2270 Appl. Sci. 2021, 11, 8799 8 of 19 Dataset_4 17 2176.0 3216.0 2589.400 352.0859 3.2. The Methodology Used within the Improved UCP Model The appropriate methodology was selected for the experimental part of the UCP ap- proach based on several trial experiments. The order of the experiment was constructed based on a robust design algorithm and is shown in Figure 3. Figure 3. Robust design algorithm for performing the experiment (UCP). Step 1: Input layer The input values of the first proposed architecture ANN-L16 are six input values, four of which are independent (UAW, UUCW, TCF, and ECF) and two dependent (UUCP and AUCP). The input values of the second proposed architecture ANN-L36prim are four independent input values: UAW, UUCW, TCF, and ECF. Step 2: All input values are transformed according to the following formula: The function 𝜇 𝐷 (𝑋 )∶ 𝑅 → [0, 1] translates the real values of input signals into coded values from the interval [0, 1] in the following way: 𝜇 (𝑋 ) = (𝑋 −𝑋 )/(𝑋 − 𝑋 ) [33,34], where D is the set of data on which the experiment is performed, Xi is the input value, Xmin is the smallest input value, and Xmax the greatest input value on the observed dataset. Step 3: The sigmoid function, as the activation function of the hidden layer, is used (19): (19) ,, The construction of the activation function is based on a combination of input values and corresponding weight coefficients 𝑊 for each of the listed ANN architectures. The hidden and output layer functions, for the ANN-L16 architecture, are as follows (20)–(22): ( ∙ ∙ ∙ ∙ ∙ ∙ ) 𝑌 =1/1 + 𝑒 (20) ( ∙ ∙ ∙ ∙ ∙ ∙ ) 𝑌 =1/1 + 𝑒 (21) ( ∙ ∙ ∙ ) − 𝐿16 = 1/1 + 𝑒 (22) 𝐸𝑠𝑡𝐸𝑓𝑓𝐴𝑁𝑁 Appl. Sci. 2021, 11, 8799 9 of 19 where Y1, Y2, and Y3 are the hidden layer functions and EstEffortANN-L16 represents the output function. The hidden and output layer functions for the ANN-L36prim architecture are as fol- lows (23)–(26): (23) 𝑌1 = (∙∙ ∙∙ ) 1+𝑒 (24) 𝑌2 = (∙∙ ∙∙ ) 1+𝑒 𝑌3 = (25) (∙∙∙ ∙) 1+𝑒 (26) − 𝐿36𝑝𝑟𝑖𝑚 = (∙∙ ∙ ∙ ) 1+𝑒 where Y1, Y2, and Y3 are the hidden layer functions and EstEffortANN-L36prim represents the output function. In the first proposed ANN-L16 architecture, an orthogonal vector plan of two levels L1 and L2, and the initial values of the weighting factors Wi that take the values from the interval [−1, 1], are used. The second proposed architecture has an orthogonal vector plan of three levels, L1, L2, and L3, and the initial values of the weighting factors Wi that take the values from the interval [−1, 0, 1]. For each subsequent iteration, new weight factor values must be calculated as follows (e.g., for ANN-L16 architecture) [2–4] (27): W1L1 = cost1 + cost2 +…+ cost8 W1L2 = cost9 + cost10 +…+ cost16 (27) W15L1 = cost1 + cost6 +…+ cost16 W15L2 = cost2 + cost3 +…+ cost15 where cost(𝑖 ) = Σ 𝑀𝑅𝐸( 𝐴 (𝑖 )) For each subsequent iteration, the interval [−1, 1] is divided depending on the cost effect function as follows [7,30] (28): W1L1new = W1L1old W1L2new = W1L2old + (W1L3old − W1L2old)/2 (28) W1L3new = W1L3old where W1L1old, W1L2old, and W1L3old are values form the previous iteration. The set of input values of each dataset converges depending on the value of the cost effect function. Step 4: The defuzzification method is used according to the following formula (29), (30) [35]: Y𝑖 = (𝑋 + (𝑋 )) ∙ (𝑋 − 𝑋 ) (29) 𝑚𝑖𝑛 𝑖 𝑚𝑎𝑥 𝑚𝑖𝑛 OA(ANNi) = Yi, where i = 16, i = 36. (30) where OA represents actual effort of the particular project, which is calculated based on ANN-L12 and ANN-L36prim. Step 5: For each iteration in our experiment, the output values are obtained according to the following formulas/measures [2,4,30] (31)–(35): (31) 𝑡𝑖𝑜𝑛𝐷𝑒𝑣𝑖𝑎 = | 𝐴 𝑜 − 𝐸| 𝑀𝐴𝐸 = | 𝐴 − 𝐸| (32) (33) = / 𝐴 𝑐𝑡𝐸𝑓𝑓𝑜𝑟𝑡 𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑀𝑅𝐸 𝑠𝑡𝐸𝑓𝑓𝑜𝑟𝑡 𝑐𝑡𝐸𝑓𝑓𝑜𝑟𝑡 𝑠𝑡𝐸𝑓𝑓𝑜𝑟𝑡 𝑐𝑡𝐸𝑓𝑓𝑟𝑡 𝜇𝐷 𝑁𝑁 𝐸𝑠𝑡𝐸𝑓𝑓𝑜𝑟𝑡𝐴𝑁𝑁 Appl. Sci. 2021, 11, 8799 10 of 19 = ∙ 𝑅𝑀𝐸 (34) MMRE = mean (MRE) (35) For each of the experimental parts in every iteration, the Gradient Descent is moni- tored with the condition GA < 0.01, calculated as [4,30,31] (36): (36) = 𝑀 −𝑀𝐸𝑅 < 0.01, 𝑤ℎ𝑒𝑟𝑒 𝑖 = 1, … , 𝑛 𝑛 𝑖𝑠 𝑎 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐴 . Step 6: This step concerns the influence of the dependent variables UUCP and AUCP on the change in MMRE value. 1. The influence of the input parameter UUCP and its value are calculated as (37): 𝛿 1 = mean(MMRE) − mean(MMRE1) (37) where MMRE1 is mean(MMRE) when UUCP = 0; 2. The influence of the input parameter AUCP and its value are calculated as (38): 𝛿 2 = mean(MMRE) − mean(MMRE2) when AUCP = 0; (38) Step 7: Correlation, Prediction The Pearson’s [36], Spearman’s [37] and R [38] coefficients are monitored (39). ∑ (𝑥 − 𝑥 ̅ )(𝑦 − 𝑦 ) ( ) 𝑋, 𝑌 = (39) ∑ ∑ (𝑥 − 𝑥 ̅ ) (𝑦 − 𝑦 ) The second and third parts are executed by the same algorithm as the first part, with different projects and datasets being used. The second part uses the ISBSG dataset, but with projects that were not used in the first part. In the third part, the Desharnais dataset and combined dataset are used. Additionally, prediction at 25%, 30%, and 50% is the percentage of the total number of ANNs that meet the GA criterion (40) [39], [40], [41]. 1, 𝑀𝑅𝐸 ≤ 𝑥 (𝑥) ∙ 0, 𝑜𝑡ℎ𝑒𝑖𝑠𝑒𝑤𝑟 (40) PRED(k) = count(MRE) < 25% PRED(k) = count(MRE) < 30% PRED(k) = count(MRE) < 50%, where k = 25, k = 30, and k = 50. The second and third parts are executed by the same algorithm as the first part, with different projects and datasets being used. The second part uses the also UCP Benchmark (Mendeley) dataset, but with projects that were not used in the first part. In the third part, the combined industrial datasets were used. 4. Discussion With the UCP model, it is possible to measure the size of the system as with the model of functional points. A model that uses system user characteristics and use cases is a newer method of software evaluation. It is one of the most commonly used models due to the exceptional evaluation results that its application can achieve. The disadvantage of this model is that it does not consider the data structure in the system because such data are not contained in the cases of use. Table 5 shows the results obtained by training the first proposed ANN-L16 architecture on the used dataset. The number of iterations concerning the set GA criterion was monitored. The GA criterion was met after four iterations. Based on all MRE values in each executed iteration, the “Winner” network, i.e., the ANN net- work with the lowest MRE value, was determined. Additionally, the MMRE value was 𝑃𝑅𝐸𝐷 𝑖𝑓 𝐶𝑜𝑟𝑟𝑒𝑙 𝑁𝑁 𝑅𝐸 𝐺𝐴 𝑀𝑅𝐸 Appl. Sci. 2021, 11, 8799 11 of 19 calculated for each iteration. The obtained value of the “Winner” network (ANN6) is 6.7%, and the value for MMRE is 7.1%. In addition to examining the MMRE value, the conver- gence rate on all training data for the two ANN architectures was examined. It can be concluded that the ANN-L36prim architecture quickly converges to the minimum knowledge of MMRE compared to the ANN-L16 architecture (Figure 4). Convergence rate ANN-L16 and ANN- L36prim 0.06 0.04 0.02 0.00 12 345 Datasets ANN-L16 ANN-L36prim Figure 4. Convergence rate of ANN-L16 vs. ANN-L36prim (UCP). Table 5. ANN-L16 training results. No. of Iter. 1. 2. 3. 4. ANN-L16 MRE GA MRE GA MRE GA MRE GA ANN1 0.084 0.084 0.080 0.004 0.076 0.004 0.072 0.004 ANN2 0.196 0.196 0.112 0.084 0.082 0.030 0.073 0.009 ANN3 0.188 0.188 0.113 0.076 0.081 0.031 0.072 0.009 ANN4 0.085 0.085 0.077 0.008 0.074 0.003 0.072 0.003 ANN5 0.161 0.161 0.105 0.056 0.080 0.025 0.073 0.008 ANN6 0.069 0.069 0.068 0.002 0.067 0.000 0.067 0.000 ANN7 0.078 0.078 0.073 0.006 0.071 0.002 0.070 0.001 ANN8 0.151 0.151 0.105 0.046 0.081 0.024 0.073 0.008 ANN9 0.191 0.191 0.120 0.071 0.076 0.044 0.072 0.004 ANN10 0.073 0.073 0.080 0.007 0.074 0.006 0.073 0.001 ANN11 0.078 0.078 0.080 0.001 0.075 0.005 0.072 0.003 ANN12 0.130 0.130 0.084 0.047 0.074 0.010 0.070 0.003 ANN13 0.113 0.113 0.083 0.030 0.074 0.009 0.071 0.002 ANN14 0.094 0.094 0.080 0.014 0.072 0.008 0.071 0.002 ANN15 0.094 0.094 0.080 0.015 0.073 0.007 0.071 0.002 ANN16 0.102 0.102 0.082 0.021 0.074 0.008 0.071 0.002 GA 16 10 5 0 Winner 6.9% 6.8% 6.7% 6.7% MMRE 11.8% 8.9% 7.5% 7.1% A graphical representation of the GA values, during four iterations, is shown in Fig- ure 5. delta(i) Appl. Sci. 2021, 11, 8799 12 of 19 Gradient Descent (GA) ANN-L16 18 16 12 10 2 0 01 2345 No. of Iteration Figure 5. GA for ANN-L16-training part (UCP). A graphical representation of the MRE value for the Winner network, relative to the MMRE value on the training dataset, during the four iterations, is given in Figure 6. Figure 6. “Winner” MRE vs. MMRE on the training dataset (ANN-L16). Table 6 shows the results obtained by training the second proposed ANN-36prim architecture on the used dataset. The number of iterations concerning the set GA criterion was monitored. The GA criterion was met after six iterations. Based on all MRE values in each executed iteration, the “Winner” network was determined, i.e., the ANN network with the lowest MRE value. Additionally, the MMRE value was calculated for each itera- tion. The obtained value of the “Winner” network (ANN10) is 6.9%, and the value for MMRE is 7.0%. Table 6. ANN-L36prim training results. ANN-L36prim GA 36 35 23 14 3 0 Winner 7.3% 7.2% 7.1% 7.0% 7.0% 6.9% MMRE 12.1% 9.4% 8.4% 7.5% 7.2% 7.0% A graphical representation of GA values, during six iterations, is shown in Figure 7. Value GA Appl. Sci. 2021, 11, 8799 13 of 19 Gradient Descent (GA) 30 23 10 3 01 234567 No. of Iteration Figure 7. GA for ANN-L36prim-training part (UCP). A graphical representation of the MRE value for the Winner network, relative to the MMRE value on the training dataset, during six iterations, is given in Figure 8. Figure 8. “Winner” MRE vs. MMRE on the training dataset (ANN-L36prim). The obtained results for the two proposed architectures, ANN-L16 and ANN-L36, in all three parts of the experiment showed that the different nature of the data set does not affect the complexity of the architecture used. That is, it does not depend on the value of the input values. In the first proposed architecture, ANN-L16, all six input values were used (where four are linearly independent and two linearly dependent), and the MMRE value in all three parts of the experiment is 7.5% of Table 7. Using the second architecture, ANN-L36prim, with four independent input values, the same MMRE value, of 7.5%, was obtained in all three parts of the experiment (Table 7). The error differences in individual parts of the experiment are not more than 0.5%, indicating the proposed model’s reliabil- ity. Table 7. MMRE value in all three parts of the experiment (UCP). ANN-L16 ANN-L36prim Part of experi- Datasets ment MMRE(%) MMRE(%) 6.7 7.0 Training Dataset_1 7.1 7.1 Testing Dataset_2 8.0 7.5 Validation1 Dataset_3 8.3 8.4 Validation2 AVERAGE(MMRE) 7.5 7.5 The huge values of the correlation coefficients (Pearson’s and Spearman’s rho) fur- ther show the consistency of the actual and estimated values obtained using the proposed Value GA Appl. Sci. 2021, 11, 8799 14 of 19 models. In the ANN-L36prim architecture, the Pearson value is 0.983, which indicates an exceptional interrelationship between the observed values (Table 8). Table 8. Correlation coefficients (UCP). Correlation ANN-L16 ANN-L36prim Pearson’s 0.875 0.983 Spearman’s rho 0.784 0.962 Prediction represents the number of projects that have an error less than the set cri- terion. Prediction can further confirm the validity and reliability of the models used. For all three proposed criteria (PRED (25), PRED (30), and PRED (50)) and in all three parts of the experiment, using both proposed architectures, the value is 100% (Table 9). Table 9. Prediction values (UCP). Training PRED(%) ANN-L16(%) ANN-L36prim(%) PRED(25) 100.0 100.0 PRED(30) 100.0 100.0 PRED(50) 100.0 100.0 Testing PRED(25) 100.0 100.0 PRED(30) 100.0 100.0 PRED(50) 100.0 100.0 Validation1 PRED(25) 100.0 100.0 PRED(30) 100.0 100.0 PRED(50) 100.0 100.0 Validation2 PRED(25) 100.0 100.0 PRED(30) 100.0 100.0 PRED(50) 100.0 100.0 By examining the influence of dependent and independent variables on the change in the MMRE value, it was shown that it is sufficient to use a four-dimensional vector instead of a six-dimensional vector. The error with dependent input values on the four datasets used is between −0.3 and 0.5, which is less than 1%. The most significant influence is the input value of AUCP (Dataset_3), and the change in the value of MMRE is, in this case, higher by 0.5%. The slightest influence has the input value of UUCW (Dataset_4), and the change in the value of MMRE is, in this case, lower by 0.5%, which would mean that the error can be reduced/increased if the observed values are further analyzed. It can be concluded that the architecture with six input sizes can be replaced with the architec- ture with four input sizes. That is, in the observed approach, the ANN-L16 architecture can be replaced with the ANN-L36prim architecture (Table 10). Table 10. Influence of the input values on the change in MMRE (UCP). Dataset MMRE UAW UUCW UUCP TCF ECF AUCP Dataset_1 6.7% 7.1% 6.7% 7.0% 6.7% 6.7% 7.1% Dataset_2 7.0% 7.1% 7.0% 7.2% 6.9% 7.1% 7.2% Dataset_3 8.0% 7.9% 8.1% 7.9% 8.1% 8.1% 7.5% Dataset_4 8.3% 7.9% 8.4% 7.9% 8.3% 8.2% 8.0% Appl. Sci. 2021, 11, 8799 15 of 19 From Table 11, it can be concluded that the dependent variable UUCP has less impact than the dependent variable AUCP. The most significant influence of AUCP (Dataset_3) on the change in the MMRE value is 0.5%. The slightest influence of AUCP (Dataset_1) on the change in the MMRE value is −0.3%. Table 11. Influence of dependent variables (UUCP and AUCP) on the change in the MMRE value. g − UUCP = MMRE − g – AUCP = MMRE − Dataset UUCP AUCP UUCP AUCP Dataset_1 6.9% −0.1% 6.8% −0.3% Dataset_2 7.1% −0.1% 7.1% −0.1% Dataset_3 8.0% 0.1% 8.0% 0.5% Dataset_4 8.2% 0.2% 8.2% 0.2% max 0.2% max 0.5% min −0.1% min −0.3% A graphical representation of the dependent input values of UUCP and AUCP with the values of their errors is given in Figure 9. UUCP and AUCP influence 0.60% 0.50% 0.50% 0.40% 0.30% 0.20% 0.20% 0.20% 0.10% 0.10% 0.00% -0.10% -0.10% -0.10% -0.10% -0.20% -0.30% -0.30% -0.40% g-UUCP g-AUCP Figure 9. Influence of dependent variables (UUCP and AUCP) on the change in the MMRE value. By comparing the results of the parametric method COCOMO2000 with improved COCOMO2000 and ANN, it can be concluded that the model error is reduced by 193.1/43.3 = 4.5 times. In the second proposed approach, comparing the parametric method COCOMO2000 and the improved COSMIC FP and ANN, the model error reduc- tion is 193.1/28.8 = 6.7 times. Compared with the COCOMO2000 parametric method with UCP and ANN, the model error reduction is 193.1/7.5 = 25.7 times (Table 12; Figure 10). In the first proposed approach, the lowest model error value is 43.3% for the ANN-L36 architecture. In the second proposed approach, the lowest error value is achieved with ANN-L36prim, with a value of 28.8%. In the third proposed approach, both proposed architectures, ANN-L16 and ANN-L36prim, give the lowest model error value of 7.5% (Table 12; Figure 10). It can be concluded that the third proposed UCP approach achieves the lowest MMRE value. In addition, the ANN-L16 architecture in this approach con- verges rapidly and reaches the “stop criterion” after the fourth iteration, which is also the lowest number of repeated iterations that apply to all architectures used in all three pro- posed approaches. The influence of dependent variables on the change of MMRE values in the ANN-L16 architecture is less than 0.5%. It can be concluded that the improved UCP model using the ANN-L16 architecture is the best-proposed estimate of effort and cost for software project development. Appl. Sci. 2021, 11, 8799 16 of 19 Table 12. MMRE values for the approaches used. MMRE(%) COCOMO2000 and ANN COSMIC FP and ANN UCP and ANN ANN- COCOMO2000 ANN-L9 ANN-L18 ANN-L27 ANN-L36 ANN-L12 ANN-L36prim ANN-L16 L36prim 193.1% 72.0% 59.7% 45.3% 43.3% 29.7% 28.8% 7.5% 7.5% Figure 10. MMRE value for the approaches used. By selecting the best ANN architectures, which achieved the lowest MMRE value for each of the three proposed improved models, it can be concluded that: COSMIC FP and ANN are 43.3/28.8 = 1.5 times better than COCOMO2000 and ANN; UCP and ANN are 48.8/7.5 = 5.8 times better than COCOMO2000 and ANN; and UCP and ANN are 28.8/7.5 = 3.8 times better than COSMIC FP and ANN (Table 13). Table 13. COCOMO2000 and ANN vs. COSMIC FP and ANN vs. UCP and ANN. COCOMO2000 and COSMIC FP and ANN UCP and ANN ANN MMRE(%) ANN- ANN-L36 ANN-L36prim ANN-L16 L36prim 43.3% 28.8% 7.5% 7.5% The results shown for this approach (UCP) in the previous tables and figures were processed in the R programming language and checked in the Python programming lan- guage within the RStudio environment. The data required for statistical analysis were processed using the IBM SPSS Statistical 25 software tool. 5. Conclusions The proposed UCP model uses two different ANN architectures and four different datasets, a sigmoid activation function, a fuzzification method, and a Taguchi method to estimate the effort and cost of software development. By monitoring the MMRE value and the convergence rate of each of these architectures, this model gives much better results compared to the previous two models. Based on the three performed parts of the experi- ment, it was concluded that the ANN-L16 architecture converges after the fourth iteration and gives an MMRE value of only 7.5%, which is 35.8% better than the first COCOMO2000 model. The error value of the UCP model is 21.3% lower than the other proposed COSMIC FP model. Following the prediction through all parts of the experiment, both ANN archi- tectures of this model have a value of 100%, which means that the model is exact, accurate, Appl. Sci. 2021, 11, 8799 17 of 19 and reliable. In addition to the MMRE value, the influence of the dependent variables UUCP and AUCP was monitored to check the influence on the change in the MMRE value. The resulting error is less than 0.5%, so it can be concluded that the ANN-L36prim archi- tecture and vice versa can replace the ANN-L16 architecture. Compared to the results ob- tained in previous studies, the best so far being 10% [22], our proposed approach gave a better result. The main advantages of this model are as follows: the number of iterations being in the interval from 4 to 6, which means reduced effort estimation time thanks to the exceptional convergence rate of both architectures; the simplicity of the two proposed ANN architectures; the high coverage of different real effort values; and the lowest MMRE value being 7.5%. A possible drawback is the finding of new methods that could further reduce the MMRE value. There are no specific limitations in applying this approach. This model can be used alone or in combination with the previous two for assessment depend- ing on the company’s historical data for which the software is implemented. Although not as standardized as the previous two, it is increasingly used by software companies, soft- ware engineers, and teams to assess the effort required to implement software projects effectively. Possible applications of the proposed model are as follows: signal processing; image and speech recognition; the recognition and processing of natural languages and different types of knowledge; the recognition of printed texts; and others. This model can also be successfully used in the medical sciences to construct various software solutions to diag- nose many diseases. In addition, it is widely used in meteorology to forecast weather con- ditions. It can be relevant in nuclear science, robotics, automatic control, telecommunica- tions, finance, and banking services. Numerous new applications of the proposed model of artificial intelligence are ex- pected in the future. Future research will focus on constructing models to solve problems related to cybercrime. Author Contributions: Conceptualization, N.R., D.R., M.I. and L.L.; Data curation, N.R., D.R., M.I. and L.L.; Formal analysis, N.R., D.R., M.I. and L.L.; Funding acquisition, N.R., D.R., M.I. and L.L.; Investigation, N.R., D.R., M.I. and L.L.; Methodology, N.R., D.R., M.I. and L.L.; Project administra- tion, N.R., D.R., M.I. and L.L.; Resources, N.R., D.R., M.I. and L.L.; Software, N.R., D.R., M.I. and L.L.; Supervision, N.R., D.R., M.I. and L.L.; Validation, N.R., D.R., M.I. and L.L.; Visualization, N.R., D.R., M.I. and L.L.; Writing—original draft, N.R., D.R., M.I. and L.L.; Writing—review & editing, N.R., D.R., M.I. and L.L. All authors have read and agreed to the published version of the manu- script. Funding: This research received no external funding Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: This study is based on the research and data that will be presented in detail in the doctoral dissertation of Nevena Rankovic Acknowledgments: Mirjana Ivanovic acknowledge financial support of the Ministry of Education, Science and Technological Development of the Republic of Serbia (Grant No. 451-03-9/2021-14/ 200125). Conflicts of Interest: The authors declare no conflict of interest. References 1. Fadhil, A.A.; Alsarraj, R.G.H.; Altaie, A.M. Software Cost Estimation Based on Dolphin Algorithm. IEEE Access 2020, 8, 75279– 75287, doi:10.1109/access.2020.2988867. 2. Stoica, A.; Blosiu, J. Neural Learning using orthogonal arrays. Adv. Intell. Syst. 1997, 41, 418; ISBN 978-90-5199-355-4. 3. Khaw, J.F.; Lim, B.; Lim, L.E. Optimal design of neural networks using the Taguchi method. Neurocomputing 1995, 7, 225–245, doi:10.1016/0925-2312(94)00013-i. Appl. Sci. 2021, 11, 8799 18 of 19 4. Rankovic, N.; Rankovic, D.; Ivanovic, M.; Lazic, L. A New Approach to Software Effort Estimation Using Different Artificial Neural Network Architectures and Taguchi Orthogonal Arrays. IEEE Access 2021, 9, 26926–26936, doi:10.1109/ac- cess.2021.3057807. 5. Langsari, K.; Sarno, R. Optimizing effort and time parameters of COCOMO II estimation using fuzzy multi-objective PSO. In Proceedings of the 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Yogyakarta, Indonesia, 19-21 September 2017; pp. 1–6, doi:10.1109/EECSI.2017.8239157. 6. Quesada-López, C.; Madrigal-Sánchez, D.; Jenkins, M. An Empirical Analysis of IFPUG FPA and COSMIC FP Measurement Methods. In International Conference on Information Technology & Systems, Bogota, Colombia, 5-7 February 2020; Springer: Cham, Switzerland, 2020; pp. 265–274. 7. Symons, C. Function point analysis: difficulties and improvements. IEEE Trans. Softw. Eng. 1988, 14, 2–11, doi:10.1109/32.4618. 8. Lavazza, L.; Liu, G. An Empirical Evaluation of the Accuracy of NESMA Function Points Estimates. ICSEA 2019, 2019, 36. 9. Ochodek, M.; Kopczyńska, S.; Staron, M. Deep learning model for end-to-end approximation of COSMIC functional size based on use-case names. Inf. Softw. Technol. 2020, 123, 106310, doi:10.1016/j.infsof.2020.106310. 10. Kläs, M.; Trendowicz, A.; Wickenkamp, A.; Münch, J.; Kikuchi, N.; Ishigai, Y. Chapter 4 The Use of Simulation Techniques for Hybrid Software Cost Estimation and Risk Analysis. Adv. Organomet. Chem. 2008, 74, 115–174, doi:10.1016/s0065-2458(08)00604- 11. Kirmani, M.M.; Wahid, A. Revised use case point (re-ucp) model for software effort estimation. Int. J. Adv. Comput. Sci. Appl. 2015, 6, 65–71. 12. 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, doi:10.1049/iet-sen.2016.0322. 13. Grover, M.; Bhatia, P.K.; Mittal, H. Estimating Software Test Effort Based on Revised UCP Model Using Fuzzy Technique. In Proceedings of the International Conference on Information and Communication Technology for Intelligent Systems, March 2017; Springer: Cham, Switzerland, 2017; pp. 490–498. 14. Ani, Z.C.; Basri, S.; Sarlan, A. A reusability assessment of UCP-based effort estimation framework using object-oriented ap- proach. J. Telecommun. Electron. Comput. Eng. JTEC 2017, 9, 111–114. 15. Sharma, P.; Singh, J. Systematic Literature Review on Software Effort Estimation Using Machine Learning Approaches. In Pro- ceedings of the 2017 International Conference on Next Generation Computing and Information Systems (ICNGCIS); December 2017; pp. 43–47. 16. Gustav, K. Resource Estimation for Objectory Projects; Torshamnsgatan 39, Box 1128, 164 22 Kista, Objective Systems SF AB: 1993; pp. 1–9. 17. Nassif, A.B.; Capretz, L.F.; Ho, D. Enhancing Use Case Points Estimation Method using Soft Computing Techniques. J. Glob. Res. Comput. Sci. 2010, 1, 12–21. 18. Couellan, N. Probabilistic robustness estimates for feed-forward neural networks. Neural Netw. 2021, 142, 138–147, doi:10.1016/j.neunet.2021.04.037. 19. Mukherjee, S.; and Malu, R.K. Optimization of project effort estimate using neural network. In Proceedings of the 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, 2014; pp. 406–410. 20. Dar, A.A.; Anuradha, N. Use of orthogonal arrays and design of experiment via Taguchi L9 method in probability of default. Accounting 2018, 4, 113–122, doi:10.5267/j.ac.2017.11.001. 21. Alshibli, M.; El Sayed, A.; Kongar, E.; Sobh, T.; Gupta, S.M. A Robust Robotic Disassembly Sequence Design Using Orthogonal Arrays and Task Allocation. Robotics 2019, 8, 20, doi:10.3390/robotics8010020. 22. Carroll, E.R. Estimating software based on use case points. In Proceedings of the 20th Annual ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications—OOPSLA ’05, San Diego, CA, USA, 2005; pp. 257–265. 23. Nassif, A.B. Software Size and Effort Estimation from Use Case Diagrams Using Regression and Soft Computing Models. Ph.D. Thesis, Western University, London, ON, Canada, 2012. 24. Azzeh, M. Fuzzy Model Tree for Early Effort Estimation Machine Learning and Applications. In Proceedings of the 12th Inter- national Conference on Machine Learning and Applications, Miami, FL, USA, 2013; pp. 117–121. 25. Urbanek, T.; Prokopova, Z.; Silhavy, R.; Sehnalek, S. Using Analytical Programming and UCP Method for Effort Estimation. In Modern Trends and Techniques in Computer Science; Advances in Intelligent Systems and Computing; Springer: Berlin/Heidelberg, Germany, 2014; Volume 285, pp. 571–581. 26. Kaur, A.; Kaur, K. Effort Estimation for Mobile Applications Using Use Case Point (UCP). In Smart Innovations in Communication and Computational Sciences; Springer: Singapore, 2019; pp. 163–172. 27. Mahmood, Y.; Kama, N.; Azmi, A. A systematic review of studies on use case points and expert-based estimation of software development effort. J. Softw. Evol. Process. 2020, 32, e2245, doi:10.1002/smr.2245. 28. Gebretsadik, K.K.; Sewunetie, W.T. Designing Machine Learning Method for Software Project Effort Prediction. Comput. Sci. Eng. 2019, 9, 6–11. 29. Alves, R.; Valente, P.; Nunes, N.J. Improving software effort estimation with human-centric models: A comparison of UCP and iUCP accuracy. In Proceedings of the 5th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, 2013; pp. 287–296. 30. Rankovic, D.; Rankovic, N.; Ivanovic, M.; Lazic, L. Convergence rate of Artificial Neural Networks for estimation in software development projects. Inf. Softw. Technol. 2021, 138, 106627, doi:10.1016/j.infsof.2021.106627. Appl. Sci. 2021, 11, 8799 19 of 19 31. Rankovic, N.; Rankovic, D.; Ivanovic, M.; Lazic, L. Improved Effort and Cost Estimation Model Using Artificial Neural Net- works and Taguchi Method with Different Activation Functions. Entropy 2021, 23, 854, doi:10.3390/e23070854. 32. Available online: https://data.mendeley.com/datasets/2rfkjhx3cn/1 (accessed on 4 February 2020). 33. Lam, H. A review on stability analysis of continuous-time fuzzy-model-based control systems: From membership-function- independent to membership-function-dependent analysis. Eng. Appl. Artif. Intell. 2018, 67, 390–408, doi:10.1016/j.en- gappai.2017.09.007. 34. Ghosh, L.; Saha, S.; Konar, A. Decoding emotional changes of android-gamers using a fused Type-2 fuzzy deep neural network. Comput. Hum. Behav. 2021, 116, 106640, doi:10.1016/j.chb.2020.106640. 35. Ritu, O.P. Software Quality Prediction Method Using Fuzzy Logic. Turk. J. Comput. Math. Educ. 2021, 12, 807–817. 36. Boldin, M.V. On the Power of Pearson’s Test under Local Alternatives in Autoregression with Outliers. Math. Methods Stat. 2019, 28, 57–65, doi:10.3103/s1066530719010046. 37. Blum, D.; Holling, H. Spearman’s law of diminishing returns. A meta-analysis. Intelligence 2017, 65, 60–66, doi:10.1016/j.in- tell.2017.07.004. 38. Wang, H.; Wang, Z. Deterministic and probabilistic life-cycle cost analysis of pavement overlays with different pre-overlay conditions. Road Mater. Pavement Des. 2019, 20, 58–73, doi:10.1080/14680629.2017.1374996. 39. Qiao, L. Deep learning based software defect prediction. Neurocomputing 2020, 385, 100–110, doi:10.1016/j.neucom.2019.11.067. 40. Shah, M.A. Ensembling Artificial Bee Colony with Analogy-Based Estimation to Improve Software Development Effort Predic- tion. IEEE Access 2020, 8, 58402–58415, doi:10.1109/access.2020.2980236. 41. Manali, P.; Maity, R.; Ratnam, J.V.; Nonaka, M.; Behera, S.K. Long-lead prediction of ENSO modoki index using machine learn- ing algorithms. Sci. Rep. 2020, 10, 365.

Applied Sciences – Multidisciplinary Digital Publishing Institute

**Published: ** Sep 22, 2021

**Keywords: **software development estimation; Use Case Point Analysis; orthogonal array-based experiment; artificial neural networks design

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