A Review of the Regression Models Applicable to Software Project Effort Estimation

Advances in Intelligent Systems and Computing

2020

Self Cite

Use Case Points is considered to be one of the most popular methods to estimate the size of a developed software project. Many approaches have been proposed to optimise Use Case Points. The Algorithmic Optimisation Method uses the Multiple Least Squares method to improve the accuracy of Use Case Points by finding optimal coefficient regressions, based on the historical data. This paper aims to propose a new approach to optimise the Use Case Points method based on Gradient Descent with the support of the TensorFlow package. The significance of its purpose is to conduct a new approach that might lead to more accurate prediction than that of the Use Case Points and the Algorithmic Optimisation Method. As a result, this new approach outweighs both the Use Case Points and the Algorithmic Optimisation Methods.

Section: Our Proposed Methods -Adamucpmentioning

confidence: 99%

Section: Experiments Designmentioning

confidence: 99%

See 1 more Smart Citation

AdamOptimizer for the Optimisation of Use Case Points Estimation

Advances in Intelligent Systems and Computing

2020

Self Cite

“…Effort estimation of software development is a significant challenge in the process of software project building [1][2][3][4]. This process is likely the first phase to estimate the development resources (i.e., cost, time).…”

Section: Introductionmentioning

confidence: 99%

An Approach to Adjust Effort Estimation of Function Point Analysis

Software Engineering and Algorithms

2021

Self Cite

This study presents a modified approach to adjust a software development effort estimation. The AdamOptimizer-based regression model is adopted to adjust and enhance the accuracy of effort estimation. This approach is derived into three phases. The first step deals with the logarithmized formula of effort estimation computed by Function Point Analysis and Productivity Delivery Rate. The Adam-Optimizer-based regression model is examined in the second phase, and the ISBSG repository 2020 release R1 is considered as a historical dataset in this paper. Moreover, the K-Fold cross-validation technique is adopted to tunning the training model. In the following phase, all results are evaluated by statistical significance and the goodness of fit measure. Finally, a proposed approach is compared with others: Capers Jones, and the Mean Effort.

“…Then, starting from those factors, one can find out the corresponding effort by software size and fixed productivity factors -(20 person-hours). However, this method has been analysed in literature and it was shown that it has low accuracy in effort estimation [7,15,21].…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Technical and Environmental Complexity Factors for Improving Use Case Points Estimation

Advances in Intelligent Systems and Computing

2020

Self Cite

This paper presents a proposed method for improving the prediction ability of the Use Case Points method. Our main goal is to use the Least Absolute Shrinkage and Selection Operator Regression methods to find out which of the technical and environmental complexity factors significantly affect the accuracy of the Use Case Points method. Two regression models were used to calculate the selected significant variables. The results of several evaluation measures show that the proposed estimation method ability is better than the original Use Case Points method. The Sum of Squared Error of the proposed method is better than the results obtained by the original one. The study also enables project managers to understand how to assess the technical and environmental complexity factors better -since they do have an important impact on effort estimation.