A Systematic Literature Review on Software Development Estimation Techniques

Srivastava, Prateek; Srivastava, Nidhi; Agarwal, Rakesh; Singh, Pawan Kumar

doi:10.1007/978-981-16-4641-6_11

Cited by 1 publication

(5 citation statements)

References 15 publications

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“…The model is derived from Rayleigh/Norden's allocation of manpower and analysis of a number of completed projects [13].The software equation defines Putnam's model as follows:…”

Section: Putnam's Modelmentioning

confidence: 99%

“…An effective way of measuring evaluation criteria is through MAE. The equation below indicates the average of absolute errors between actual and projected efforts [13].…”

Section: Mean Absolute Error (Mae)mentioning

confidence: 99%

“…Software prediction models are mostly compared using MMRE. We select the most appropriate model based on MMRE [13]. [13].…”

Section: Mean Magnitude Relative Error (Mmre)mentioning

confidence: 99%

“…We select the most appropriate model based on MMRE [13]. [13]. An error variation will be smaller if the MSE value is smaller.MSE can be determined mathematically using the following equation.…”

Section: Mean Magnitude Relative Error (Mmre)mentioning

confidence: 99%

“…(2) Evaluate the results of the measures outlined above.Below is an equation that illustrates how to determine PRED(x) mathematically [13]. (6) There are N projects, where 'MRE' represents the percentage of projects with MREs less or equal to x.…”

Section: Prediction (Pred (X))mentioning

confidence: 99%

See 4 more Smart Citations

An Intelligent Framework for Estimating Software Development Projects using Machine Learning

Srivastava,

Agarwal

et al. 2023

IJRITCC

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The IT industry has faced many challenges related to software effort and cost estimation. A cost assessment is conducted after software effort estimation, which benefits customers as well as developers. The purpose of this paper is to discuss various methods for the estimation of software effort and cost in the context of software engineering, such as algorithmic methods, expert judgment methods, analogy-based estimation methods, and machine learning methods, as well as their different aspects. In spite of this, estimation of the effort involved in software development are subject to uncertainty. Several methods have been developed in the literature for improving estimation accuracy, many of which involve the use of machine learning techniques. A machine learning framework is proposed in this paper to address this challenging problem. In addition to being completely independent of algorithmic models and estimation problems, this framework also features a modular architecture. It has high interpretability, learning capability, and robustness to imprecise and uncertain inputs.

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“…The model is derived from Rayleigh/Norden's allocation of manpower and analysis of a number of completed projects [13].The software equation defines Putnam's model as follows:…”

Section: Putnam's Modelmentioning

confidence: 99%

“…An effective way of measuring evaluation criteria is through MAE. The equation below indicates the average of absolute errors between actual and projected efforts [13].…”

Section: Mean Absolute Error (Mae)mentioning

confidence: 99%

“…Software prediction models are mostly compared using MMRE. We select the most appropriate model based on MMRE [13]. [13].…”

Section: Mean Magnitude Relative Error (Mmre)mentioning

confidence: 99%

Section: Mean Magnitude Relative Error (Mmre)mentioning

confidence: 99%

Section: Prediction (Pred (X))mentioning

confidence: 99%

See 3 more Smart Citations

An Intelligent Framework for Estimating Software Development Projects using Machine Learning

Srivastava,

Agarwal

et al. 2023

IJRITCC

View full text Add to dashboard Cite

show abstract

A Systematic Literature Review on Software Development Estimation Techniques

Cited by 1 publication

References 15 publications

An Intelligent Framework for Estimating Software Development Projects using Machine Learning

An Intelligent Framework for Estimating Software Development Projects using Machine Learning

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