scholarly journals Optimizing Software Effort Estimation Models Using Back-Propagation Versus Radial Base Function Networks

2019 ◽  
Vol 15 (3) ◽  
pp. 321-331
Author(s):  
Abdel Karim Baareh
Keyword(s):  
Back Propagation ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Base Function ◽  
Radial Base Function ◽  
Estimation Models

SOFTWARE DEVELOPMENT EFFORT ESTIMATION USING CLASSICAL AND FUZZY ANALOGY: A CROSS-VALIDATION COMPARATIVE STUDY

2014 ◽  
Vol 13 (03) ◽  
pp. 1450013 ◽  
Author(s):  
FATIMA AZZAHRA AMAZAL ◽  
ALI IDRI ◽  
ALAIN ABRAN
Keyword(s):  
Software Development ◽  
Case Based Reasoning ◽  
Development Effort ◽  
Software Project ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Software Projects ◽  
Software Development Effort ◽  
Research Questions ◽  
Estimation Models

Software effort estimation is one of the most important tasks in software project management. Of several techniques suggested for estimating software development effort, the analogy-based reasoning, or Case-Based Reasoning (CBR), approaches stand out as promising techniques. In this paper, the benefits of using linguistic rather than numerical values in the analogy process for software effort estimation are investigated. The performance, in terms of accuracy and tolerance of imprecision, of two analogy-based software effort estimation models (Classical Analogy and Fuzzy Analogy, which use numerical and linguistic values respectively to describe software projects) is compared. Three research questions related to the performance of these two models are discussed and answered. This study uses the International Software Benchmarking Standards Group (ISBSG) dataset and confirms the usefulness of using linguistic instead of numerical values in analogy-based software effort estimation models.

scholarly journals Optimizing Software Effort Estimation Models Using Firefly Algorithm

2015 ◽  
Vol 08 (03) ◽  
pp. 133-142 ◽  
Author(s):  
Nazeeh Ghatasheh ◽  
Hossam Faris ◽  
Ibrahim Aljarah ◽  
Rizik M. H. Al-Sayyed
Keyword(s):  
Firefly Algorithm ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Estimation Models

scholarly journals Analyzing the Stationarity Process in Software Effort Estimation Datasets

2020 ◽  
Vol 30 (11n12) ◽  
pp. 1607-1640
Author(s):  
Michael Franklin Bosu ◽  
Stephen G. MacDonell ◽  
Peter A. Whigham
Keyword(s):  
Software Engineering ◽  
Model Development ◽  
Kernel Estimator ◽  
Prediction Errors ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Underlying Assumption ◽  
Software Engineering Process ◽  
Linear Regression Modeling ◽  
Estimation Models

Software effort estimation models are typically developed based on an underlying assumption that all data points are equally relevant to the prediction of effort for future projects. The dynamic nature of several aspects of the software engineering process could mean that this assumption does not hold in at least some cases. This study employs three kernel estimator functions to test the stationarity assumption in five software engineering datasets that have been used in the construction of software effort estimation models. The kernel estimators are used in the generation of nonuniform weights which are subsequently employed in weighted linear regression modeling. In each model, older projects are assigned smaller weights while the more recently completed projects are assigned larger weights, to reflect their potentially greater relevance to present or future projects that need to be estimated. Prediction errors are compared to those obtained from uniform models. Our results indicate that, for the datasets that exhibit underlying nonstationary processes, uniform models are more accurate than the nonuniform models; that is, models based on kernel estimator functions are worse than the models where no weighting was applied. In contrast, the accuracies of uniform and nonuniform models for datasets that exhibited stationary processes were essentially equivalent. Our analysis indicates that as the heterogeneity of a dataset increases, the effect of stationarity is overridden. The results of our study also confirm prior findings that the accuracy of effort estimation models is independent of the type of kernel estimator function used in model development.

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