Radek Silhavy

Algorithmic Optimisation Method for Improving Use Case Points Estimation

This paper presents a new size estimation method that can be used to estimate size level for software engineering projects. The Algorithmic Optimisation Method is based on Use Case Points and on Multiple Least Square Regression. The method is derived into three phases. The first phase deals with calculation Use Case Points and correction coefficients values. Correction coefficients are obtained by using Multiple Least Square Regression. New project is estimated in the second and third phase. In the second phase Use Case Points parameters for new estimation are set up and in the third phase project estimation is performed. Final estimation is obtained by using newly developed estimation equation, which used two correction coefficients. The Algorithmic Optimisation Method performs approximately 43% better than the Use Case Points method, based on their magnitude of relative error score. All results were evaluated by standard approach: visual inspection, goodness of fit measure and statistical significance.

Modern Trends and Techniques in Computer Science

This book is based on the research papers presented in the 3rd Computer Science On-line Conference 2014 (CSOC 2014).The conference is intended to provide an international forum for discussions on the latest high-quality research results in all areas related to Computer Science. The topics addressed are the theoretical aspects and applications of Artificial Intelligences, Computer Science, Informatics and Software Engineering. The authors provide new approaches and methods to real-world problems, and in particular, exploratory research that describes novel approaches in their field. Particular emphasis is laid on modern trends in selected fields of interest. New algorithms or methods in a variety of fields are also presented. This book is divided into three sections and covers topics including Artificial Intelligence, Computer Science and Software Engineering. Each section consists of new theoretical contributions and applications which can be used for the further development of knowledge of everybody who is looking for new knowledge or new inspiration for further research.

Evaluating subset selection methods for use case points estimation

Abstract When the Use Case Points method is used for software effort estimation, users are faced with low model accuracy which impacts on its practical application. This study investigates the significance of using subset selection methods for the prediction accuracy of Multiple Linear Regression models, obtained by the stepwise approach. K-means, Spectral Clustering, the Gaussian Mixture Model and Moving Window are evaluated as appropriate subset selection techniques. The methods were evaluated according to several evaluation criteria and then statistically tested. Evaluation was performing on two independent datasets - which differ in project types and size. Both were cut by the hold-out method. If clustering were used, the training sets were clustered into 3 classes; and, for each of class, an independent regression model was created. These were later used for the prediction of testing sets. If Moving Window was used, then window of sizes 5, 10 and 15 were tested. The results show that clustering techniques decrease prediction errors significantly when compared to Use Case Points or moving windows methods. Spectral Clustering was selected as the best-performing solution, because it achieves a Sum of Squared Errors reduction of 32% for the first dataset, and 98% for the second dataset. The Mean Absolute Percentage Error is less than 1% for the second dataset for Spectral Clustering; 9% for moving window; and 27% for Use Case Points. When the first dataset is used, then prediction errors are significantly higher – 53% for Spectral Clustering, but Use Case Points produces a 165% result. It can be concluded that this study proves subset selection techniques as a significant method for improving the prediction ability of linear regression models - which are used for software development effort prediction. It can also be concluded that the clustering method performs better than the moving window method.

Web-based service portal in healthcare

Information delivery is one the most important task in healthcare. The growing sector of electronic healthcare has an important impact on the information delivery. There are two basic approaches towards information delivering. The first is web portal and second is touch-screen terminal. The aim of this paper is to investigate the web-based service portal. The most important advantage of web-based portal in the field of healthcare is an independent access for patients. This paper deals with the conditions and frameworks for healthcare portals

Software Engineering in Intelligent Systems

This volume is based on the research papers presented in the 4th Computer Science On-line Conference. The volume Software Engineering in Intelligent Systems presents new approaches and methods to real-world problems, and in particular, exploratory research that describes novel approaches in the field of Software Engineering. Particular emphasis is laid on modern trends in selected fields of interest. New algorithms or methods in a variety of fields are also presented. The Computer Science On-line Conference (CSOC 2015) is intended to provide an international forum for discussions on the latest high-quality research results in all areas related to Computer Science. The addressed topics are the theoretical aspects and applications of Computer Science, Artificial Intelligences, Cybernetics, Automation Control Theory and Software Engineering.

Using Analytical Programming and UCP Method for Effort Estimation

This article is aimed to using the analytical programming and the Use Case Points method to estimate time effort in software engineering. The calculation of Use Case Points method is strictly algorithmically defined, and the calculation of this method is simple and fast. Despite a lot of research on this field, there are many attempts to calibrating the weights of Use Case Points method. In this paper is described idea that equation used in Use Case Points method could be less accurate in estimation than other equations. The aim of this research is to create new method, that will be able to create new equations for Use Case Points method. Analytical programming with self-organizing migration algorithm is used for this task. The experimental results shows that this method improving accuracy of effort estimation by 25–40 %.

Patients’ Perspective of the Design of Provider-Patients Electronic Communication Services

Information Delivery is one the most important tasks in healthcare practice. This article discusses patient’s tasks and perspectives, which are then used to design a new Effective Electronic Methodology. The system design methods applicable to electronic communication in the healthcare sector are also described. The architecture and the methodology for the healthcare service portal are set out in the proposed system design.

The Effects of Clustering to Software Size Estimation for the Use Case Points Methods

The main objective of the paper is to present the suitability and effects of several different clustering methods for improving accuracy of software size estimation. For software size estimation was used the Algorithmic Optimisation Method (AOM), which is based Use Case Points (UCP) method. The comparison of K-means, Hierarchical and Density-based clustering is provided. Gap, Silhouette and Calinski-Harabasz criterion were selected as an evaluation criterion for clustering quality. Estimation ability of clustered model is compared on Sum of squared error (SSE). Results shows that clustering improves an estimation ability.

Evaluation of Data Clustering for Stepwise Linear Regression on Use Case Points Estimation

In this paper, stepwise linear regression model in conjunction with clustering for effort estimation is investigated. Effect of clustering is compared to Use Case Points model. The 2 to 20 clusters were tested. As shown increasing a number of clusters brings lower prediction errors. More clusters lower a distance between clusters members, which allows to construct more capable stepwise linear regression model.

New Approach Of Constant Resolving Of Analytical Programming.

This papers’ aim is to provide the Artificial Intelligence community with a better tool for symbolic regression. In this paper, the method of analytical programming and constant resolving is revisited and extended. Nowadays, analytical programming mainly uses two methods for constant resolving. The first method is meta-evolution, in which the second evolutionary algorithm is used for constant resolving. The second method uses non-linear fitting algorithm. This paper reveals the third method, which use the basic mathematics to generate constants. The findings of this study have a number of important implications for future practice. INTRODUCTION A lot of application in symbolic regression field requires some kind of algorithm for constant generation (Koza 1992), (O’Neill, Brabazon & Ryan 2002). Therefore the effective numeric constant resolving algorithm is very important. In this paper, we use analytical programming as a method for symbolic regression (Zelinka, Davendra, Senkerik, Jasek & Oplatkova 2011). This method based on existence of any kind of evolutionary algorithm which generate a pointers to function tables. Analytical programming (AP) from these pointers maps and assemble a final regression function. In the analytical programming algorithm, two main approaches can be selected for constant resolving. The first approach is meta-evolution, in which the second or slave evolutionary algorithm is used for constant resolving. The second approach is to use of nonlinear fitting algorithm. Both approaches added to analytical programming a lot of complexity. This study introduces a new approach for constant resolving in analytical programming algorithm. This approach is founded on basic mathematical calculation. Section II is devoted to the original algorithm of analytical programming. Section III presents the new approach for constant resolving. Section IV presents the methods used for this study. Section V summaries the results of this research. Finally, Section VI presents the conclusions of this study. Differential Evolution Differential Evolution is an optimization algorithm introduced by Storn and Price in 1995, (Storn & Price 1995). This optimization method is an evolutionary algorithm based on population, mutation and recombination. Differential Evolution is easy to implement and has only four parameters which need to be set. The parameters are: Generations, NP, F and Cr. The Generations Parameter determines the number of generations; the NP Parameter is the population size; the F Parameter is the Weighting Factor; and the Cr Parameter is the Crossover Probability, (Storn 1996). In this research, the differential evolution is used as an analytical programming engine. Analytical Programming Analytical Programming, is a symbolic regression method. The core of analytical programming is a set of functions and operands. These mathematical objects are used for the synthesis of a new function. Every function in the analytical programming set core has its own varying number of parameters. The functions are sorted according to these parameters into General Function Sets (GFS). For example, GFS1par contains functions that have only one parameter e.g. sin(), cos(), or other functions. AP must be used with any evolutionary algorithm that consists of a population of individuals for its run (Oplatkova, Senkerik, Zelinka & Pluhacek 2013). The function of analytical programming can be seen in Figure 1. In this case, Evolutionary Algorithm is Differential Evolution. The initial population is generated using Differential Evolution. This population, which must consist of natural numbers, is used for analytical programming purposes. The analytical programming then constructs the function on the basis of this population. This function is evaluated by its Cost Function. If the termination condition is met, then the algorithm ends. If the condition is not met, then Differential Evolution creates a new population through the Mutation and Recombination processes. The whole process continues with the new population. At the end of the analytical programming process, it is assumed that one has a function that is the optimal solution for the given task. Proceedings 30th European Conference on Modelling and Simulation ©ECMS Thorsten Claus, Frank Herrmann, Michael Manitz, Oliver Rose (Editors) ISBN: 978-0-9932440-2-5 / ISBN: 978-0-9932440-3-2 (CD) Fig. 1. Scheme of Analytical Programming with Differential Evolution algorithm ORIGINAL ALGORITHM Let’s have the individual of the n length ind = (x1, x2, . . . , xn) where xi ∈ R . This individual is then rounded indr = (‖x1‖, ‖x2‖, . . . , ‖xn‖) where ‖.‖ is nearest integer function. Let’s have a set called GFSall which consists of m functions GFSall = {{f1, fp1}, {f2, fp2}, . . . , {fm, fpm}} where fm is function and fpm is number of parameters of function fm. Then these functions are sorted to 4 sets : GFS0, GFS1,0, GFS2,1,0 and GFSall. TABLE I. GFSS BY PARAMETERS GFS0 = {{f1, 0}, {f2, 0}, . . . , {fn, 0}} ⊂ GFSall GFS1 = {{f1, 1}, {f2, 1}, . . . , {fn, 1}} ⊂ GFSall GFS2 = {{f1, 2}, {f2, 2}, . . . , {fn, 2}} ⊂ GFSall GFS1,0 = GFS1 ∪ GFS0 GFS2,1,0 = GFS2 ∪ GFS1 ∪ GFS0 The sets from table I are expanded to the maximum value from the individual because we expected that the value of each number in individual could be higher then the length of GFS. TABLE II. EXAMPLE OF GFSS, WHEN MAXIMUM VALUE OF INDIVIDUAL IS 6 GFS0 = {{K, 0}, {x, 0}, {K, 0}, {x, 0}, {K, 0}, {x, 0}} GFS1,0 = {{Sin, 1}, {Cos, 1}{K, 0}, {x, 0} {Sin, 1}, {Cos, 1}} GFS2,1,0 = {{Plus, 2}, {Minus, 2}, {Sin, 1}, {Cos, 1}, {K, 0}, {x, 0}} GFSall = {{Plus, 2}, {Minus, 2}, {Sin, 1}, {Cos, 1}, {K, 0}, {x, 0}} Then we need to construct a matrix with functions mapped by individual. After that we have function = ((f1, fp1), (f2, fp2), . . . , (fn, fpn)) where fn is function and fpn is number of parameters of function fn. Then we need to choose which function is applied to which function. The values are pointers to the functions in GFSs. After that, we have constructed function; however, there is a constant K, which have to be resolved. Now we have two possibilities • Meta-evolution e.g. Differential Evolution • Non-linear least square fitting for example Levenberg-Marquardt NEW APPROACH Let’s have the individual of the n length ind = (x1, x2, . . . , xn) where xi ∈ R . This individual is then rounded indf = (⌊x1⌋, ⌊x2⌋, . . . , ⌊xn⌋) where ⌊.⌋ is round to floor. Now we can construct a difference between ind and indf . indc = ind− indf In vector indf are pointers to GFSs and indc are corresponding constants. The decimal numbers in indc are in range < 0, 1 >. These numbers could be easily converted to constants into the chosen range. Let’s have a set called GFSall which consists of m functions GFSall = {{f1, fp1}, {f2, fp2}, . . . , {fm, fpm}} where fi is function and fpi is number of parameters of function fi. Then this functions are sorted to 4 sets : GFS0, GFS1,0, GFS2,1,0 and GFSall. The next key change in analytical programming algorithm is function selection. In this new approach, the GFSs are not expanded to the maximum value of the individual. The selection of function is controlled by modulo function. On the position where the constant K will be mapped; we can read a constant number from the indc vector. The original mapping algorithm is the same. Now we have constructed function with resolved constants. PROBLEM STATEMENT The overall research question to be answered within the study is whether there is a possibility to outperformed the original analytical programming method. This section presents the design of the research question. We performed experiments to get an insight in the constant resolving of analytical programming. The research question of our study can be outlined as follows: RQ: Analysing the impact of new approach on the calculation duration and minimization performance of analytical programming. The research question (RQ) aims to get an insight on the new approach of constant resolving of analytical programming and understand the actual effectiveness of this technique. For this reason, we use 3 different methods for constant resolving. Analytical programming with differential evolution and two new versions of proposed algorithm. Then, we try to outperformed the original constant resolving algorithm of analytical programming. To asses the performance of fitness function, we used descriptive statistics. METHOD New constant resolving algorithm for analytical programming was tested for searching regression functions. Results were compared by descriptive statistics. Following functions have been tested • f(x) = 45.5 • f(x) = 3x+ 0.65 • f(x) = 2.3x − 20x− 5.6 • f(x) = 3.65 ∗ sin(2x) These functions were selected with emphasis on constant resolving. Functions such as constant, linear, quadratic and harmonic were tested. There was generated 20 points for each function. And the task for analytical programming was to fit these points. Three methods of constant resolving were tested. • Analytical programming with differential evolution further referred to as AP+DE • New analytical programming version with constant range < −1000, 1000 > further referred to as AP2(-1000,1000) • New analytical programming version with constant range < 0, 10 > further referred to as AP2(0,10) TABLE III. SYSTEM CONFIGURATION Parameter Value CPU AMD Phenom II X2 3GHz RAM 8 GB Operation system Windows 7 Professional 64 bit Programming language LUA 5.2 Table III shows the system configuration for performing tests. Table IV shows the analytical programming set-up. The number of leafs (functions built by analytical programming TABLE IV. SET-UP OF ANALYTICAL PROGRAMMING Parameter Value Number of leafs 16 GFS functions plus, minus, multiply, divide, power, log, log10, exp, sqrt, floor, ceil, abs, sin, cos GFS constants x, K can be seen as trees) was set to 16. This value was sufficient for the purpose of this paper. TABLE V. SET-UP OF DIFFERENTIAL EVOL