Cuauhtemoc Lopez-Martin

Software development effort prediction of industrial projects applying a general regression neural network

An important factor for planning, budgeting and bidding a software project is prediction of the development effort required to complete it. This prediction can be obtained from models related to neural networks. The hypothesis of this research was the following: effort prediction accuracy of a general regression neural network (GRNN) model is statistically equal or better than that obtained by a statistical regression model, using data obtained from industrial environments. Each model was generated from a separate dataset obtained from the International Software Benchmarking Standards Group (ISBSG) software projects repository. Each of the two models was then validated using a new dataset from the same ISBSG repository. Results obtained from a variance analysis of accuracies of the models suggest that a GRNN could be an alternative for predicting development effort of software projects that have been developed in industrial environments.

Predictive accuracy comparison of fuzzy models for software development effort of small programs

Regression analysis to generate predictive equations for software development effort estimation has recently been complemented by analyses using less common methods such as fuzzy logic models. On the other hand, unless engineers have the capabilities provided by personal training, they cannot properly support their teams or consistently and reliably produce quality products. In this paper, an investigation aimed to compare personal Fuzzy Logic Models (FLM) with a Linear Regression Model (LRM) is presented. The evaluation criteria were based mainly upon the magnitude of error relative to the estimate (MER) as well as to the mean of MER (MMER). One hundred five small programs were developed by thirty programmers. From these programs, three FLM were generated to estimate the effort in the development of twenty programs by seven programmers. Both the verification and validation of the models were made. Results show a slightly better predictive accuracy amongst FLM and LRM for estimating the development effort at personal level when small programs are developed.

Applying a Feedforward Neural Network for Predicting Software Development Effort of Short-Scale Projects

The software project effort estimation is an important aspect of software engineering practices. The improvement in accuracy of estimations is a topic that still remains as one of the greatest challenges of software engineering and computer science in general. In this work, the effort estimation for shortscale software projects, developed in academic setting, is modeled by two techniques: statistical regression and neural network. Two groups of software projects were made. One group of projects was used to calculate linear regression parameters and to train a neural network. The two models were then compared on both groups, the one used for their calculation and the other that was not used before. The accuracy of estimates was measured by using the magnitude of error relative to the estimate (MER) for each project and its mean MMER over each group of projects. The hypothesis accepted in this paper suggested that a feed forward neural network could be used for predicting short-scale software projects.

Applying a general regression neural network for predicting development effort of short-scale programs

Software development effort prediction is considered in several international software processes as the Capability Maturity Model-Integrated (CMMi), by ISO-15504 as well as by ISO/IEC 12207. In this paper, data of two kinds of lines of code gathered from programs developed with practices based on the Personal Software Process (PSP) were used as independent variables in three models for estimating and predicting the development effort. Samples of 163 and 80 programs were used for verifying and validating, respectively, the models. The prediction accuracy comparison among a multiple linear regression, a general regression neural network, and a fuzzy logic model was made using as criteria the magnitude of error relative to the estimate (MER) and mean square error (MSE). Results accepted the following hypothesis: effort prediction accuracy of a general regression neural network is statistically equal than those obtained by a fuzzy logic model as well as by a multiple linear regression, when new and change code and reused code obtained from short-scale programs developed with personal practices are used as independent variables.

Neural networks for predicting the duration of new software projects

Two neural networks are applied for predicting the development duration of new software projects.The software projects are obtained from the ISBSG dataset release 11.Adjusted function points and team size are used as independent variables.Prediction accuracy is calculated from the absolute residuals.Prediction accuracy of the neural networks resulted statistically better than that of a statistical regression. The duration of software development projects has become a competitive issue: only 39% of them are finished on time relative to the duration planned originally. The techniques for predicting project duration are most often based on expert judgment and mathematical models, such as statistical regression or machine learning. The contribution of this study is to investigate whether or not the duration prediction accuracy obtained with a multilayer feedforward neural network model, also called a multilayer perceptron (MLP), and with a radial basis function neural network (RBFNN) model is statistically better than that obtained by a multiple linear regression (MLR) model when functional size and the maximum size of the team of developers are used as the independent variables. The three models mentioned above are trained and tested by predicting the duration of new software development projects with a set of projects from the International Software Benchmarking Standards Group (ISBSG) release 11. Results based on absolute residuals, Pred(l) and a Friedman statistical test show that prediction accuracy with the MLP and the RBFNN is statistically better than with the MLR model.

APPLYING EXPERT JUDGMENT TO IMPROVE AN INDIVIDUAL'S ABILITY TO PREDICT SOFTWARE DEVELOPMENT EFFORT

Expert-based effort prediction in software projects can be taught, beginning with the practices learned in an academic environment in courses designed to encourage them. However, the length of such courses is a major concern for both industry and academia. Industry has to work without its employees while they are taking such a course, and academic institutions find it hard to fit the course into an already tight schedule. In this research, the set of Personal Software Process (PSP) practices is reordered and the practices are distributed among fewer assignments, in an attempt to address these concerns. This study involved 148 practitioners taking graduate courses who developed 1,036 software course assignments. The hypothesis on which it is based is the following: When the activities in the original PSP set are reordered into fewer assignments, the result is expert-based effort prediction that is statistically significantly better.

Applying Genetic Programming for Estimating Software Development Effort of Short-scale Projects

Statistical regression and neural networks have frequently been used to estimate the development effort of both short and large software projects. In this paper, a genetic programming technique is used with the goal of estimating the effort required in the development of short-scale projects. Results obtained are compared with those generated using the first two techniques. A sample of 132 short-scale projects developed by 40 programmers was used for generating the three models, whereas another sample of 77 projects developed by 24 programmers was used for validating those three models. Accuracy results from the model obtained with genetic programming suggest that it could be used to estimate software development effort of short-scale projects when these projects are developed in a disciplined manner within a development controlled environment.

Use of evolved artificial regulatory networks to simulate 3D cell differentiation

Cell differentiation has a crucial role in both artificial and natural developments. This paper presents results from simulations in which a genetic algorithm (GA) was used to evolve artificial regulatory networks (ARNs) to produce predefined 3D cellular structures through the selective activation and inhibition of genes. The ARNs used in this work are extensions of a model previously used to create 2D geometrical patterns. The GA worked by evolving the gene regulatory networks that were used to control cell reproduction, which took place in a testbed based on cellular automata (CA). After the final chromosomes were produced, a single cell in the middle of the CA lattice was allowed to replicate controlled by the ARN found by the GA, until the desired cellular structures were formed. Two simple cubic layered structures were first developed to test multiple gene synchronization. The model was then applied to the problem of generating a 3D French flag pattern using morphogenetic gradients to provide cells with positional information that constrained cellular replication.

Use of a Feedforward Neural Network for Predicting the Development Duration of Software Projects

Context: In the software engineering field, only 20 percent of software projects finish on time relative to their original plan. A software project can be classified as a new development, an enhanced development or a re-development. Goal: To propose a feed forward neural network (FFNN) for predicting the duration of new software development projects. Hypothesis: The accuracy of duration prediction for an FFNN is statistically better than the accuracy obtained from a statistical regression (SR) when an adjusted function points (AFPs) value, obtained from new software development projects, is used as the independent variable. Method: A sample obtained from the International Software Benchmarking Standards Group (ISBSG) Release 11 corresponding to new development projects was used. The accuracy of the FFNN was compared against that of an SR model. The criteria for evaluating the accuracy of these two models were the Mean Magnitude of Relative Error (MMRE) and an ANOVA statistical test. Results: Prediction accuracy of an FFNN was statistically better than that of an SR model at the 90% confidence level. Conclusion: An FFNN could be applied for predicting the duration of new software development projects when AFPs were used as independent variable.

Software Development Productivity Prediction of Small Programs Using Fuzzy Logic

In this paper, a fuzzy logic model was created from a data set of 140 small programs developed with practices based on Personal Software Process (PSP) and then this fuzzy model was applied for predicting the productivity of a new data set consisted of 60 small programs; all programs were developed with object oriented programming languages by 35 and 15 graduated programmers respectively. Accuracy result of this fuzzy logic model was compared with that of a statistical regression model. Results suggest that a fuzzy logic model could be used for estimating and predicting productivity of the software development.