Systematics, morphology and physiology: New species of tetramorium mayr (hymenoptera: Formicidae) from puebla state, Mexico

This paper addresses the problem of on-line identification of Discrete Event Systems (DES). A passive method for the progressive building of Petri net (PN) models from DES outputs evolution is presented. After introducing several concepts related with dynamical properties of DES, a learning algorithm that computes ordinary PN models according to the measurement of cyclic output streams is proposed. A procedure based on this algorithm can be on-line executed tracking the DES behavior from its output signals, whose durations are stored. The successive computed models tend progressively to represent the actual observed behavior. " 2011 IEEE.",,,,,,"10.1109/ICCA.2011.6137968",,,"http://hdl.handle.net/20.500.12104/44951","http://www.scopus.com/inward/record.url?eid=2-s2.0-84858974968&partnerID=40&md5=185dc1384e10c8721c3fc4aeafd76442",,,,,,,,"IEEE International Conference on Control and Automation, ICCA",,"120

Synthesis of timed Petri net models for on-line identification of Discrete Event Systems

This paper addresses the problem of on-line identification of Discrete Event Systems (DES). A passive method for the progressive building of Petri net (PN) models from DES outputs evolution is presented. After introducing several concepts related with dynamical properties of DES, a learning algorithm that computes ordinary PN models according to the measurement of cyclic output streams is proposed. A procedure based on this algorithm can be on-line executed tracking the DES behavior from its output signals, whose durations are stored. The successive computed models tend progressively to represent the actual observed behavior. © 2011 IEEE

Software development effort estimation in academic environments applying a general regression neural network involving size and people factors

In this research a general regression neural network (GRNN) was applied for estimating the development effort in software projects that have been developed in laboratory learning environments. The independent variables of the GRNN were two size measures as well as a developer measure. This GRNN was trained from a dataset of projects developed from the year 2005 to the year 2008 and then this GRNN was validated by estimating the effort of a new dataset integrated by projects developed from the year 2009 o the first months of the year 2010. Accuracy results from the GRNN model were compared with a statistical regression model. Results suggest that a GRNN could be used for estimating the development effort of software projects when two kinds of lines of code as well as the programming language experience of developers are used as independent variables. © 2011 Springer-Verlag Berlin Heidelberg

Software development productivity prediction of individual projects applying a neural network

Machine learning techniques have been applied in the software engineering field and their models could be applied for predicting the development productivity of software developers. In this paper, a neural network model was trained from a data set of 140 individual projects developed from between years 2005 and 2008 with practices based on a process specificaly designed to laboratory learning environments: Personal Software Process (PSP). Then, this model was applied for predicting the productivity of a new projects consisting of 156 projects developed from between years 2009 and 2010. The code in all projects was developed by 74 graduated students, using object oriented programming languages C++ and Java. Prediction accuracy obtained from neural network was compared to those obtained from a fuzzy logic model as well as from a statistical regression model. Results suggest that a neural network model could be used for predicting development productivity of individual projects, when they are developed in a disciplined way in a laboratory learning environment

A machine learning technique for predicting the productivity of practitioners from individually developed software projects

Context: Productivity management of software developers is a challenge in Information and Communication Technology. Predictions of productivity can be useful to determine corrective actions and to assist managers in evaluating improvement alternatives. Productivity prediction models have been based on statistical regressions, statistical time series, fuzzy logic, and machine learning. Goal: To propose a machine learning model termed general regression neural network (GRNN) for predicting the productivity of software practitioners. Hypothesis: Prediction accuracy of a GRNN is better than a statistical regression model when these two models are applied for predicting productivity of software practitioners who have individually developed their software projects. Method: A sample obtained from 396 software projects developed between the years 2005 and 2011 by 99 practitioners was used for training the models, whereas a sample of 60 projects developed by 15 practitioners in the first months of 2012 was used for testing the models. All projects were developed based upon a disciplined development process within a controlled environment. The accuracy of the GRNN was compared against that of a multiple regression model (MLR). The criteria for evaluating the accuracy of these two models were the Magnitude of Error Relative to the estimate and a t-paired statistical test. Results: Prediction accuracy of an GRNN was statistically better than that of an MLR model at the 99% confidence level. Conclusion: An GRNN could be applied for predicting the productivity of practitioners when New and Changed lines of code, reused code, and programming language experience of practitioners are used as independent variables. � 2014 IEEE

Software size estimation of individual projects

Background: Software project size is often used as independent variable for predicting dependent variables such as effort, schedule, costs or risks of software projects. The better the size estimation accuracy, the better the prediction accuracy of dependent variables. The size of each project is usually estimated in terms of the number of lines of code for the programming language in which the project will be coded. This estimation is typically done using expert judgment techniques or applying prediction models. Hypothesis: There is a statistically significant difference amongst size prediction accuracy of projects by the object-oriented programming language used, when they are estimated by expert judgment from specified requirements in natural language. Method: A population of 1,414 individual software projects was developed by 202 practitioners. Each project had its own specified requirements in natural language, and each one was developed within a controlled experiment and following a disciplined process. A sample of 676 projects developed in C++ or Java was selected for this study. Results: There was a statistically significant difference between size estimation accuracy for C++ and Java at a 95% level of confidence. Conclusions: The size estimation accuracy of software projects coded in Java was better than the estimation accuracy of projects coded in C++

Required conditions to identify petri net models based on an asymptotic identification approach

The aim of this paper is to present the conditions under which a Discrete Event System (DES) can be identified using an asymptotic identification approach. The asymptotic identification problem consists in compute an Interpreted Petri Net (IPN) model in proportion as new output sequences of the system are observed. Given this problem, the identification conditions are related with: 1) the possibility to detect a change of state from the output signal, 2) the structure of the system to be identified and 3) the input signal given to the system to generate the output sequences required

Software development productivity prediction of individual projects applying a neural network

Machine learning techniques have been applied in the software engineering field and their models could be applied for predicting the development productivity of software developers. In this paper, a neural network model was trained from a data set of 140 individual projects developed from between years 2005 and 2008 with practices based on a process specificaly designed to laboratory learning environments: Personal Software Process (PSP). Then, this model was applied for predicting the productivity of a new projects consisting of 156 projects developed from between years 2009 and 2010. The code in all projects was developed by 74 graduated students, using object oriented programming languages C++ and Java. Prediction accuracy obtained from neural network was compared to those obtained from a fuzzy logic model as well as from a statistical regression model. Results suggest that a neural network model could be used for predicting development productivity of individual projects, when they are developed in a disciplined way in a laboratory learning environment

Usefulness of serum lipid peroxide as a diagnostic test for hypoxic ischemic encephalopathy in the full-term neonate

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. " 2013 IEEE.",,,,,,"10.1109/ICMLA.2013.182",,,"http://hdl.handle.net/20.500.12104/45573","http://www.scopus.com/inward/record.url?eid=2-s2.0-84899457261&partnerID=40&md5=defda50b6e552d1056e24c81aa5df09e",,,,,,,,"Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013",,"15

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. © 2013 IEEE