Add to ORKGThe complexity of providing accurate software effort prediction models is well known in the software industry. Several prediction models have been proposed in the literature using different techniques, with different results, in different contexts. Objectives: This paper reports a benchmarking study using a genetic approach that automatically generates and compares different learning schemes (preprocessing+attribute selection+learning algorithms). The effectiveness of the software development effort prediction models (using function points) were validated using the ISBSG R12 dataset. Methods: Eight subsets of projects were analyzed running a M×N-fold cross-validation. We used a genetic approach to automatically select the componen...
Abstract—Context: The use of search-based methods has been recently proposed for software developmen...
Neural networks are often selected as tool for software effort prediction because of their capabilit...
Feature selection algorithms are used to extract the most relevant features from a dataset and filte...
Background: Several prediction models have been proposed in the literature using different technique...
Context: Bio-inspired feature selection algorithms got the attention of the researchers in the domai...
Feature selection algorithms select the best and relevant set of features of the datasets which lead...
Statistical and genetic programming techniques have been used to predict the software development ef...
Statistical and genetic programming techniques have been used to predict the software development ef...
Software development effort estimation is a critical activity of the project management process. In ...
A predictive model is required to be accurate and comprehensible in order to inspire confidence in a...
The idea of exploiting Genetic Programming (GP) to estimate software development effort is based on ...
Statistical regression and neural networks have frequently been used to estimate the development eff...
The idea of exploiting search-based methods to estimate development effort is based on the observati...
Constructing an accurate effort prediction model is a challenge in Software Engineering. This paper ...
Abstract—Context: The use of search-based methods has been recently proposed for software developmen...
Neural networks are often selected as tool for software effort prediction because of their capabilit...
Feature selection algorithms are used to extract the most relevant features from a dataset and filte...
Background: Several prediction models have been proposed in the literature using different technique...
Context: Bio-inspired feature selection algorithms got the attention of the researchers in the domai...
Feature selection algorithms select the best and relevant set of features of the datasets which lead...
Statistical and genetic programming techniques have been used to predict the software development ef...
Statistical and genetic programming techniques have been used to predict the software development ef...
Software development effort estimation is a critical activity of the project management process. In ...
A predictive model is required to be accurate and comprehensible in order to inspire confidence in a...
The idea of exploiting Genetic Programming (GP) to estimate software development effort is based on ...
Statistical regression and neural networks have frequently been used to estimate the development eff...
The idea of exploiting search-based methods to estimate development effort is based on the observati...
Constructing an accurate effort prediction model is a challenge in Software Engineering. This paper ...
Abstract—Context: The use of search-based methods has been recently proposed for software developmen...
Neural networks are often selected as tool for software effort prediction because of their capabilit...
Feature selection algorithms are used to extract the most relevant features from a dataset and filte...