Evolving Software Effort Estimation Models Using Multigene Symbolic Regression Genetic Programming

Aljahdali, Sultan; Sheta, Alaa

doi:10.14569/ijarai.2013.021207

Cited by 7 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although some studies have used log-transformed variables (e.g., , Jeffery et al [2000], Chen et al [2005], and Dejaeger et al [2012]), Jorgensen and Shepperd [2007] found that few studies look at the "properties of the data." Although the warnings from Kitchenham and Mendes [2009], were presented some years ago, it appears that the trend in applying linear models without a consideration of the data and model assumptions has continued [Bardsiri et al 2014], or that no comparison against other models or the use of a training/test comparison is even considered [Aljahdali and Sheta 2013]. In response, in recent years increasing attention has been directed to the need for baseline models in SEE, against which any newly proposed model should be positively compared before adoption and use, that is, the new model should be shown superior to the baseline model.…”

Section: Introductionmentioning

confidence: 99%

A Baseline Model for Software Effort Estimation

Whigham

Owen

MacDonell

2015

ACM Trans. Softw. Eng. Methodol.

112

View full text Add to dashboard Cite

Software effort estimation (SEE) is a core activity in all software processes and development lifecycles. A range of increasingly complex methods has been considered in the past 30 years for the prediction of effort, often with mixed and contradictory results. The comparative assessment of effort prediction methods has therefore become a common approach when considering how best to predict effort over a range of project types. Unfortunately, these assessments use a variety of sampling methods and error measurements, making comparison with other work difficult. This article proposes an automatically transformed linear model (ATLM) as a suitable baseline model for comparison against SEE methods. ATLM is simple yet performs well over a range of different project types. In addition, ATLM may be used with mixed numeric and categorical data and requires no parameter tuning. It is also deterministic, meaning that results obtained are amenable to replication. These and other arguments for using ATLM as a baseline model are presented, and a reference implementation described and made available. We suggest that ATLM should be used as a baseline of effort prediction quality for all future model comparisons in SEE. . 2015. A baseline model for software effort estimation.

show abstract

Section: Introductionmentioning

confidence: 99%

A Baseline Model for Software Effort Estimation

Whigham

Owen

MacDonell

2015

ACM Trans. Softw. Eng. Methodol.

112

View full text Add to dashboard Cite

show abstract

“…The algorithm evolves these genes through genetic operations such as mutation, crossover, and selection, seeking to optimize the fitness function, which measures how well the set of genes fits a given dataset. MG-GP has been known to be a powerful tool for solving complex regression problems, such as those found in modeling and optimization of manufacturing processes [36], [37], software effort estimation [38], image reconstruction [39], and many others [40], [41]. It can address problems with datasets that have a complex and noisy relationship.…”

Section: What Is Multigene Symbolic Regression Gp?mentioning

confidence: 99%

Evolutionary Design of a PSO-Tuned Multigene Symbolic Regression Genetic Programming Model for River Flow Forecasting

Sheta¹,

Abdel-Raouf²,

Fraihat³

et al. 2023

IJACSA

View full text Add to dashboard Cite

The earth's population is growing at a rapid rate, while the availability of water resources remains limited. Water is required for various purposes, including drinking, agriculture, industry, recreation, and development. Accurate forecasting of river flows can have a significant economic impact, particularly in agricultural water management and planning during water resource scarcity. Developing precise river flow forecasting models can greatly improve the management of water resources in many countries. In this study, we propose a two-phase model for predicting the flow of the Blackwater river located in the South Central United States. In the first phase, we use Multigene Symbolic Regression Genetic Programming (MG-GP) to develop a mathematical model. In the second phase, Particle Swarm Optimization (PSO) is employed to fine-tune the model parameters. Fine-tuning the MG-GP parameters improves the prediction accuracy of the model. The newly fine-tuned model exhibits 96% and 94% accuracy in training and testing cases, respectively.

show abstract

“…Their results showed that parameter tuning can have critical impact on algorithmic performance, and that overfitting of parameter tuning is a serious limitation of empirical studies in search-based software engineering. In (Aljahdali and Sheta 2013), the authors argued that recently, computational intelligence paradigms were explored to handle the software effort estimation problem with promising results. In this paper they evolve two new models for software effort estimation using Multigene Symbolic Regression Genetic Programming.…”

Section: Effort Prediction Approaches Using Genetic Algorithmsmentioning

confidence: 99%

A genetic algorithm based framework for software effort prediction

Murillo-Morera

Quesada-López

Castro-Herrera

et al. 2017

J Softw Eng Res Dev

View full text Add to dashboard Cite

Background: Several prediction models have been proposed in the literature using different techniques obtaining different results in different contexts. The need for accurate effort predictions for projects is one of the most critical and complex issues in the software industry. The automated selection and the combination of techniques in alternative ways could improve the overall accuracy of the prediction models. Objectives: In this study, we validate an automated genetic framework, and then conduct a sensitivity analysis across different genetic configurations. Following is the comparison of the framework with a baseline random guessing and an exhaustive framework. Lastly, we investigate the performance results of the best learning schemes. Methods: In total, six hundred learning schemes that include the combination of eight data preprocessors, five attribute selectors and fifteen modeling techniques represent our search space. The genetic framework, through the elitism technique, selects the best learning schemes automatically. The best learning scheme in this context means the combination of data preprocessing + attribute selection + learning algorithm with the highest coefficient correlation possible. The selected learning schemes are applied to eight datasets extracted from the ISBSG R12 Dataset. Results: The genetic framework performs as good as an exhaustive framework. The analysis of the standardized accuracy (SA) measure revealed that all best learning schemes selected by the genetic framework outperforms the baseline random guessing by 45-80%. The sensitivity analysis confirms the stability between different genetic configurations. Conclusions: The genetic framework is stable, performs better than a random guessing approach, and is as good as an exhaustive framework. Our results confirm previous ones in the field, simple regression techniques with transformations could perform as well as nonlinear techniques, and ensembles of learning machines techniques such as SMO, M5P or M5R could optimize effort predictions.

show abstract

Evolving Software Effort Estimation Models Using Multigene Symbolic Regression Genetic Programming

Cited by 7 publications

References 29 publications

A Baseline Model for Software Effort Estimation

A Baseline Model for Software Effort Estimation

Evolutionary Design of a PSO-Tuned Multigene Symbolic Regression Genetic Programming Model for River Flow Forecasting

A genetic algorithm based framework for software effort prediction

Contact Info

Product

Resources

About