Systematic Review Study of Decision Trees based Software Development Effort Estimation

Najm, Assia; Zakrani, Abdelali; Marzak, Abdelaziz

doi:10.14569/ijacsa.2020.0110767

Cited by 8 publications

(3 citation statements)

References 54 publications

(79 reference statements)

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“…Consequently, several techniques were employed with DT methods to surmount the traditional DTs challenges. Mainly, according to Najm et al [8], the fuzzy-logic (FL) is the most employed technique with DT, and the resulting trees are then called fuzzy decision trees (FDTs).…”

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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Section: Introductionmentioning

confidence: 99%

“…Regarding related works, Najm et al [8] have performed a systematic literature review on decision trees-based SDCP. They outlined 50 studies published from 1985 to 2019.…”

mentioning

confidence: 99%

Cluster-based fuzzy regression trees for software cost prediction

Najm¹,

Zakrani²,

Marzak³

2022

IJEECS

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“…To that end, the prediction of software development cost remains crucial. Several methods have been proposed in the literature such as support vector regression (SVR) [1], artificial neural networks [2], and decision trees (DT) [3], [4], etc. However, it is difficult or even impossible to find a technique that performs well in all situations.…”

Section: Introductionmentioning

confidence: 99%

Optainet-based technique for SVR feature selection and parameters optimization for software cost prediction

Najm

Zakrani²,

Marzak

2021

MATEC Web Conf.

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The software cost prediction is a crucial element for a project’s success because it helps the project managers to efficiently estimate the needed effort for any project. There exist in literature many machine learning methods like decision trees, artificial neural networks (ANN), and support vector regressors (SVR), etc. However, many studies confirm that accurate estimations greatly depend on hyperparameters optimization, and on the proper input feature selection that impacts highly the accuracy of software cost prediction models (SCPM). In this paper, we propose an enhanced model using SVR and the Optainet algorithm. The Optainet is used at the same time for 1-selecting the best set of features and 2-for tuning the parameters of the SVR model. The experimental evaluation was conducted using a 30% holdout over seven datasets. The performance of the suggested model is then compared to the tuned SVR model using Optainet without feature selection. The results were also compared to the Boruta and random forest features selection methods. The experiments show that for overall datasets, the Optainet-based method improves significantly the accuracy of the SVR model and it outperforms the random forest and Boruta feature selection methods.

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