A Comparative Analysis on Effort Estimation for Agile and Non-agile Software Projects Using DBN-ALO

Kaushik, Anupama; Tayal, Devendra K.; Yadav, Kalpana

doi:10.1007/s13369-019-04250-6

Cited by 14 publications

(11 citation statements)

References 37 publications

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“…Because the dataset in software effort estimating studies does not fit into any particular distribution, nonparametric tests are advised 44 . As per the nature of our data, non-parametric tests such as Friedman 45 have been applied to the ZKmS dataset using SPSS.…”

Section: Statistical Validationsmentioning

confidence: 99%

An efficient ANFIS-EEBAT approach to estimate effort of Scrum projects

Arora

Verma

Kavita

et al. 2022

Sci Rep

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Software effort estimation is a significant part of software development and project management. The accuracy of effort estimation and scheduling results determines whether a project succeeds or fails. Many studies have focused on improving the accuracy of predicted results, yet accurate estimation of effort has proven to be a challenging task for researchers and practitioners, particularly when it comes to projects that use agile approaches. This work investigates the application of the adaptive neuro-fuzzy inference system (ANFIS) along with the novel Energy-Efficient BAT (EEBAT) technique for effort prediction in the Scrum environment. The proposed ANFIS-EEBAT approach is evaluated using real agile datasets. It provides the best results in all the evaluation criteria used. The proposed approach is also statistically validated using nonparametric tests, and it is found that ANFIS-EEBAT worked best as compared to various state-of-the-art meta-heuristic and machine learning (ML) algorithms such as fireworks, ant lion optimizer (ALO), bat, particle swarm optimization (PSO), and genetic algorithm (GA).

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Section: Statistical Validationsmentioning

confidence: 99%

An efficient ANFIS-EEBAT approach to estimate effort of Scrum projects

Arora

Verma

Kavita

et al. 2022

Sci Rep

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“…Let H ={H 1 ,....H k }be a set of heterogeneity criteria of the corresponding k clusters. The node N j with the highest H k is chosen to be more refined (we refer the reader to [1] for more details about the calculation of H i ).…”

Section: C-fuzzy Regression Trees: the Tree Growth And Expansionmentioning

confidence: 99%

“…We repeat this procedure at each tree level. Finally, the estimated effort value (y ̂) calculation at the terminal node is equivalent to the ϕ i value of the (1).…”

Section: Software Cost Prediction Using the Treementioning

confidence: 99%

“…However, many limitations or conditions could negatively affect the performance of SDCP models and therefore give inaccurate estimations. There are many agile and non-agile effort prediction techniques [1], where researchers propose new methods or optimization for improving existing ones. Generally, decision trees (DTs) are one of the computational intelligence techniques which are largely employed for cost prediction [2], [3].…”

Section: Introductionmentioning

confidence: 99%

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Cluster-based fuzzy regression trees for software cost prediction

Najm¹,

Zakrani²,

Marzak³

2022

IJEECS

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The current paper <span lang="EN-US">proposes a novel type of decision tree, which is never used for software development cost prediction (SDCP) purposes, the cluster-based fuzzy regression tree (CFRT). This model uses the fuzzy k-means (FKM), which deals with data uncertainty and imprecision. The tree expansion is based on the variability measure by choosing the node with the highest value of granulation diversity. This paper outlined an experimental study comparing CFRT with four SDCP methods, notably linear regression, multi-layer perceptron, K-nearest-neighbors, and classification and regression trees (CART), employing eight datasets and the leave-one-out cross-validation (LOOCV). The results show that CFRT is among the best, ranked first in 3 datasets according to four accuracy measures. Also, according to the Pred(25%) values, the proposed CFRT model outperformed all the twelve compared techniques in four datasets: Albrecht, constructive cost model (COCOMO), Desharnais, and The International Software Benchmarking Standards Group (ISBSG) using LOOCV and 30-fold cross-validation technique.</span>

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“…"Estas metodologías ágiles tienen ventajas como la flexibilidad, responden a los cambios incluso en etapas posteriores de desarrollo, son amigables para el cliente y entregan el software en funcionamiento rápidamente." (Kaushik et al, 2020) 2020…”

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Estado del arte: metodologías de desarrollo de aplicaciones móviles

Ríos¹,

Tapia²,

Souto³

et al. 2021

3C Tecnología

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Since the origin of cell phones, the development of mobile applications has grown exponentially, opening a new field of research in the field of software engineering. The need to produce an application with high quality indices has led to the study and formulation of new methodologies that cover all the corresponding aspects related to quality and production time.The objective of this research is to carry out an exhaustive study to extract relevant data from each framework through bibliographic and field research for the construction of a state of the art that verifies through an analysis the methodology indicated for the development of mobile applications. The result obtained from the analysis shows that, despite finding a certain similarity between the methodologies with agile principles, Scrum is characterized as the indicated methodology for the development of mobile applications. This fact gave rise to the emergence of new software development methodologies with approaches to the so-called agile practices whose objective is the production of quality software.

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A Comparative Analysis on Effort Estimation for Agile and Non-agile Software Projects Using DBN-ALO

Cited by 14 publications

References 37 publications

An efficient ANFIS-EEBAT approach to estimate effort of Scrum projects

An efficient ANFIS-EEBAT approach to estimate effort of Scrum projects

Cluster-based fuzzy regression trees for software cost prediction

Estado del arte: metodologías de desarrollo de aplicaciones móviles

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