Using Genetic Programming to Improve Software Effort Estimation Based on General Data Sets

2003 ◽  
pp. 2477-2487 ◽  
Author(s):  
Martin Lefley ◽  
Martin J. Shepperd
Keyword(s):  
Genetic Programming ◽  
Data Sets ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
General Data

An Ensemble of Hybrid Search-Based Algorithms for Software Effort Prediction

2021 ◽  
Vol 13 (3) ◽  
pp. 28-37
Author(s):  
Wasiur Rhmann
Keyword(s):  
Machine Learning ◽  
Genetic Programming ◽  
Random Sets ◽  
Machine Learning Techniques ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Hybrid Search ◽  
Ensemble Technique ◽  
Project Failures ◽  
Fuzzy Learning

Software organizations rely on the estimation of efforts required for the development of software to negotiate customers and plan the schedule of the project. Proper estimation of efforts reduces the chances of project failures. Historical data of projects have been used to predict the effort required for software development. In recent years, various ensemble of machine learning techniques have been used to predict software effort. In the present work, a novel ensemble technique of hybrid search-based algorithms (EHSBA) is used for software effort estimation. Four HSBAs—fuzzy and random sets-based modeling (FRSBM-R), symbolic fuzzy learning based on genetic programming (GFS-GP-R), symbolic fuzzy learning based on genetic programming grammar operators and simulated annealing (GFS_GSP_R), and least mean squares linear regression (LinearLMS_R)—are used to create an ensemble (EHSBA). The EHSBA is compared with machine learning-based ensemble bagging, vote, and stacking on datasets obtained from PROMISE repository. Obtained results reported that EHSBA outperformed all other techniques.

scholarly journals Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1195
Author(s):  
Priya Varshini A G ◽  
Anitha Kumari K ◽  
Vijayakumar Varadarajan
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Software Development ◽  
Weighted Averaging ◽  
Software Project ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Single Model ◽  
Ensemble Techniques ◽  
Project Estimation

Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.

scholarly journals Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances

2016 ◽  
Vol 24 (1) ◽  
pp. 143-182 ◽  
Author(s):  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
Mark Johnston
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Genetic Programming ◽  
Visual System ◽  
Image Classification ◽  
Human Visual System ◽  
Binary Classification ◽  
Programming Approach ◽  
Data Sets ◽  
New Class

In the computer vision and pattern recognition fields, image classification represents an important yet difficult task. It is a challenge to build effective computer models to replicate the remarkable ability of the human visual system, which relies on only one or a few instances to learn a completely new class or an object of a class. Recently we proposed two genetic programming (GP) methods, one-shot GP and compound-GP, that aim to evolve a program for the task of binary classification in images. The two methods are designed to use only one or a few instances per class to evolve the model. In this study, we investigate these two methods in terms of performance, robustness, and complexity of the evolved programs. We use ten data sets that vary in difficulty to evaluate these two methods. We also compare them with two other GP and six non-GP methods. The results show that one-shot GP and compound-GP outperform or achieve results comparable to competitor methods. Moreover, the features extracted by these two methods improve the performance of other classifiers with handcrafted features and those extracted by a recently developed GP-based method in most cases.

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