Cost-Effective Supervised Learning Models for Software Effort Estimation in Agile Environments

2016 ◽  
Author(s):  
Kayhan Moharreri ◽  
Alhad Vinayak Sapre ◽  
Jayashree Ramanathan ◽  
Rajiv Ramnath
Keyword(s):  
Supervised Learning ◽  
Cost Effective ◽  
Effort Estimation ◽  
Learning Models ◽  
Software Effort Estimation

scholarly journals Feature Engineering for Enhanced Model Performance in Software Effort Estimation

2019 ◽  
Vol 8 (3) ◽  
pp. 6053-6063
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Parametric Models ◽  
Quality Data ◽  
Feature Engineering ◽  
Effort Estimation ◽  
Learning Models ◽  
Software Effort Estimation ◽  
Original Dataset ◽  
Manual Methods

Many new methodologies have been defined in the last two decades in the domain of Software Effort Estimation. They include manual methods based on expert judgment, analogy-based models, parametric models, regression models, machine learning models, and more recently, deep learning models. Except for manual methods, all other models depend heavily on data. Lack of quality data in this domain is a motivation to explore means to optimize the sparse data available. Machine learning algorithms depend on domain features, and their ability to represent and model the domain, to solve the problems irrespective of whether it is classification or regression, image, or voice synthesis. There is continued research for the best representation of the issue through the right feature space. While most of the traditional research rely on the original dataset and concentrate more on feature selection, modern-day approaches explore creating additional features that have the potential to extend the models representational space.This research builds on our last research exploring the potential to improve Software Effort Estimation accuracy by employing engineered features in addition to the original ones. The features are created manually based on the literature. Through the engineered features, we captured additional representational features such as missingness and proportion of categorical data available in the dataset. We present the rationale for the features generated and compare the prediction accuracy between a model using the original dataset and the engineered data set.Our experiments in Feature Engineering is innovative in the Software Estimation domain and the results conclusive establishing its use in predicting Software Effort. We report an improved accuracy of 38% with engineered features at PRED(15), and 11% improvement at PRED(20). The quantitative growth that we have been able to achieve in terms of accuracy is promising enough for this to be adopted as a standard in future research on the subject and practical applications.

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.

Examining the Feasibility of a Case-Based Reasoning Model for Software Effort Estimation

MIS Quarterly ◽  
1992 ◽  
Vol 16 (2) ◽  
pp. 155 ◽  
Author(s):  
Tridas Mukhopadhyay ◽  
Steven S. Vicinanza ◽  
Michael J. Prietula
Keyword(s):  
Case Based Reasoning ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Case Based

Machine Learning Models for Finger Bend Evaluation using Implemented Low cost Flex Sensor

2021 ◽  
Vol 9 (VI) ◽  
pp. 3605-3611
Author(s):  
Pratyush Kaware
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Low Cost ◽  
Learning Algorithms ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

In this paper a cost-effective sensor has been implemented to read finger bend signals, by attaching the sensor to a finger, so as to classify them based on the degree of bent as well as the joint about which the finger was being bent. This was done by testing with various machine learning algorithms to get the most accurate and consistent classifier. Finally, we found that Support Vector Machine was the best algorithm suited to classify our data, using we were able predict live state of a finger, i.e., the degree of bent and the joints involved. The live voltage values from the sensor were transmitted using a NodeMCU micro-controller which were converted to digital and uploaded on a database for analysis.

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