assia najm - Academia.edu (original) (raw)
Papers by assia najm
Indonesian Journal of Electrical Engineering and Computer Science
The current paper proposes a novel type of decision tree, which is never used for software develo... more The current paper 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 Sof...
Received Apr 18, 2021 Revised Dec 16, 2021 Accepted Dec 30, 2021 The appearance of agile software... more Received Apr 18, 2021 Revised Dec 16, 2021 Accepted Dec 30, 2021 The appearance of agile software development techniques (ASDT) since 2001 has encouraged many organizations to move to an agile approach. ASDT presents an opportunity for researchers and professionals, but it has many challenges as well. One of the most critical challenges is agile effort prediction. Hence, many studies have investigated agile software development cost estimation (ASDCE). The objective of this study is twofold: First, to propose an improved model based on support vector regression with radial bias function kernel (SVR-RBF) enhanced by the optimized artificial immune network (Optainet). Second, to perform a detailed comparative analysis of the proposed method compared to other existing optimization techniques in the literature and applied for ASDCE. The experimental evaluation was carried out by assessing the performance of the proposed method using some trusted measures like standardized accuracy (SA),...
2018 IEEE 5th International Congress on Information Science and Technology (CiSt), 2018
The existing literature on software development effort estimation is extensive and focuses partic... more The existing literature on software development effort estimation is extensive and focuses particularly on traditional software projects, while few studies have been devoted to agile projects, especially estimating the effort needed for completing the whole software projects. Story points and team velocity are among the commonly effort drivers used for planning and predicting when a software will be completed. In this paper, we propose an improved model for estimating the software effort based on support vector regression (SVR) optimized by grid search method (GS). The story point and velocity were used as inputs of the prediction model. The empirical evaluation is carried out using 21 historical agile software projects through leave-one-out cross validation method. The obtained results demonstrate that our approach was able to improve the performance of SVR technique. Moreover, it outperforms, in terms of Pred(0.25), MMRE and MdMRE, some recent methods reported in the recent litera...
IAES International Journal of Artificial Intelligence (IJ-AI)
The appearance of agile software development techniques (ASDT) since 2001 has encouraged many org... more The appearance of agile software development techniques (ASDT) since 2001 has encouraged many organizations to move to an agile approach. ASDT presents an opportunity for researchers and professionals, but it has many challenges as well. One of the most critical challenges is agile effort prediction. Hence, many studies have investigated agile software development cost estimation (ASDCE). The objective of this study is twofold: First, to propose an improved model based on support vector regression with radial bias function kernel (SVR-RBF) enhanced by the optimized artificial immune network (Optainet). Second, to perform a detailed comparative analysis of the proposed method compared to other existing optimization techniques in the literature and applied for ASDCE. The experimental evaluation was carried out by assessing the performance of the proposed method using some trusted measures like standardized accuracy (SA), mean absolute error (MAE), prediction at level p (Pred(p)), mean...
2019 International Conference of Computer Science and Renewable Energies (ICCSRE)
The decision tree (DT) represents a nonparametric estimation method that has been mostly used for... more The decision tree (DT) represents a nonparametric estimation method that has been mostly used for both classification and regression problems. DTs were adopted for software development effort estimation (SDEE) generally for their simplicity of use and interpretation contrary to other learning methods. Nevertheless, to our self-knowledge, no systematic mapping has been devoted especially to decision trees. The aim of this study is to elaborate a systematic mapping study that classifies DTs papers in conformity with the succeeding criteria: research approach, contribution type, techniques employed in combination with DT methods besides identifying publication channels and trends. An automated search of five digital libraries was made to carry out a systematic mapping of DT studies mainly devoted to SDEE that were published in the period 1985–2017. We identify 46 relevant studies. Basically, the results revealed that most researchers focus on technique contribution type. In addition, the majority of papers deal with improving the existing DT models while few studies have proposed novel models to improve the reliability of SDEE. Furthermore, solution proposal and case study are the most frequently used approaches.
International Journal of Advanced Computer Science and Applications
The role of decision trees in software development effort estimation (SDEE) has received increase... more The role of decision trees in software development effort estimation (SDEE) has received increased attention across several disciplines in recent years thanks to their power of predicting, their ease of use, and understanding. Furthermore, there are a large number of published studies that investigated the use of a decision tree (DT) techniques in SDEE. Nevertheless, in reviewing the literature, a systematic literature review (SLR) that assesses the evidence stated on DT techniques is still lacking. The main issues addressed in this paper have been divided into five parts: prediction accuracy, performance comparison, suitable conditions of prediction, the effect of the methods employed in association with DT techniques, and DT tools. To carry out this SLR, we performed an automatic search over five digital libraries for studies published between 1985 and 2019. In general, the results of this SLR revealed that most DT methods outperform many techniques and show an improvement in accuracy when combined with association rules (AR), fuzzy logic (FL), and bagging. Additionally, it has been observed a limited use of DT tools: it is therefore suggested for researchers to develop more DT tools to promote the industrial utilization of DT amongst professionals.
MATEC Web of Conferences
The software cost prediction is a crucial element for a project’s success because it helps the pr... more 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 th...
Indonesian Journal of Electrical Engineering and Computer Science
The current paper proposes a novel type of decision tree, which is never used for software develo... more The current paper 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 Sof...
Received Apr 18, 2021 Revised Dec 16, 2021 Accepted Dec 30, 2021 The appearance of agile software... more Received Apr 18, 2021 Revised Dec 16, 2021 Accepted Dec 30, 2021 The appearance of agile software development techniques (ASDT) since 2001 has encouraged many organizations to move to an agile approach. ASDT presents an opportunity for researchers and professionals, but it has many challenges as well. One of the most critical challenges is agile effort prediction. Hence, many studies have investigated agile software development cost estimation (ASDCE). The objective of this study is twofold: First, to propose an improved model based on support vector regression with radial bias function kernel (SVR-RBF) enhanced by the optimized artificial immune network (Optainet). Second, to perform a detailed comparative analysis of the proposed method compared to other existing optimization techniques in the literature and applied for ASDCE. The experimental evaluation was carried out by assessing the performance of the proposed method using some trusted measures like standardized accuracy (SA),...
2018 IEEE 5th International Congress on Information Science and Technology (CiSt), 2018
The existing literature on software development effort estimation is extensive and focuses partic... more The existing literature on software development effort estimation is extensive and focuses particularly on traditional software projects, while few studies have been devoted to agile projects, especially estimating the effort needed for completing the whole software projects. Story points and team velocity are among the commonly effort drivers used for planning and predicting when a software will be completed. In this paper, we propose an improved model for estimating the software effort based on support vector regression (SVR) optimized by grid search method (GS). The story point and velocity were used as inputs of the prediction model. The empirical evaluation is carried out using 21 historical agile software projects through leave-one-out cross validation method. The obtained results demonstrate that our approach was able to improve the performance of SVR technique. Moreover, it outperforms, in terms of Pred(0.25), MMRE and MdMRE, some recent methods reported in the recent litera...
IAES International Journal of Artificial Intelligence (IJ-AI)
The appearance of agile software development techniques (ASDT) since 2001 has encouraged many org... more The appearance of agile software development techniques (ASDT) since 2001 has encouraged many organizations to move to an agile approach. ASDT presents an opportunity for researchers and professionals, but it has many challenges as well. One of the most critical challenges is agile effort prediction. Hence, many studies have investigated agile software development cost estimation (ASDCE). The objective of this study is twofold: First, to propose an improved model based on support vector regression with radial bias function kernel (SVR-RBF) enhanced by the optimized artificial immune network (Optainet). Second, to perform a detailed comparative analysis of the proposed method compared to other existing optimization techniques in the literature and applied for ASDCE. The experimental evaluation was carried out by assessing the performance of the proposed method using some trusted measures like standardized accuracy (SA), mean absolute error (MAE), prediction at level p (Pred(p)), mean...
2019 International Conference of Computer Science and Renewable Energies (ICCSRE)
The decision tree (DT) represents a nonparametric estimation method that has been mostly used for... more The decision tree (DT) represents a nonparametric estimation method that has been mostly used for both classification and regression problems. DTs were adopted for software development effort estimation (SDEE) generally for their simplicity of use and interpretation contrary to other learning methods. Nevertheless, to our self-knowledge, no systematic mapping has been devoted especially to decision trees. The aim of this study is to elaborate a systematic mapping study that classifies DTs papers in conformity with the succeeding criteria: research approach, contribution type, techniques employed in combination with DT methods besides identifying publication channels and trends. An automated search of five digital libraries was made to carry out a systematic mapping of DT studies mainly devoted to SDEE that were published in the period 1985–2017. We identify 46 relevant studies. Basically, the results revealed that most researchers focus on technique contribution type. In addition, the majority of papers deal with improving the existing DT models while few studies have proposed novel models to improve the reliability of SDEE. Furthermore, solution proposal and case study are the most frequently used approaches.
International Journal of Advanced Computer Science and Applications
The role of decision trees in software development effort estimation (SDEE) has received increase... more The role of decision trees in software development effort estimation (SDEE) has received increased attention across several disciplines in recent years thanks to their power of predicting, their ease of use, and understanding. Furthermore, there are a large number of published studies that investigated the use of a decision tree (DT) techniques in SDEE. Nevertheless, in reviewing the literature, a systematic literature review (SLR) that assesses the evidence stated on DT techniques is still lacking. The main issues addressed in this paper have been divided into five parts: prediction accuracy, performance comparison, suitable conditions of prediction, the effect of the methods employed in association with DT techniques, and DT tools. To carry out this SLR, we performed an automatic search over five digital libraries for studies published between 1985 and 2019. In general, the results of this SLR revealed that most DT methods outperform many techniques and show an improvement in accuracy when combined with association rules (AR), fuzzy logic (FL), and bagging. Additionally, it has been observed a limited use of DT tools: it is therefore suggested for researchers to develop more DT tools to promote the industrial utilization of DT amongst professionals.
MATEC Web of Conferences
The software cost prediction is a crucial element for a project’s success because it helps the pr... more 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 th...