@article {Miloudi2022191,
	title = {The Impact of Instance Selection Algorithms on Maintenance Effort Estimation for Open-Source Software},
	journal = {Lecture Notes in Networks and Systems},
	volume = {470 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {191-201},
	abstract = {Open-source software are very used nowadays in the industry, and the performance of the estimation of their maintenance effort becomes an interesting research topic. In this context, researchers have conducted many open-source software maintenance effort estimation (O-MEE) studies based on statistical and machine learning (ML) techniques for better estimation. This study focuses on the impact of instance selection on the performance of ML techniques in O-MEE, mainly for bug resolution. An empirical study was conducted using three techniques: K-nearest neighbor (kNN), support vector machine (SVM), and multinomial na{\"\i}ve Bayes (MNB) using all-kNN instance selection algorithms on three datasets: Eclipse JDT, Eclipse Platform, and Mozilla Thunderbird datasets. This study reports on a set of 18 experiments and a comparison of the results. The results of this study show that instance selection helped make ML techniques more performant. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-04829-6_17},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130234489\&doi=10.1007\%2f978-3-031-04829-6_17\&partnerID=40\&md5=7ac0a2d5915327c057f3ec68815ba211},
	author = {Miloudi, C. and Cheikhi, L. and Idri, A. and Abran, A.}
}
@conference {Miloudi2022,
	title = {Maintenance Effort Estimation for Open Source Software: Current trends},
	booktitle = {CEUR Workshop Proceedings},
	volume = {3272},
	year = {2022},
	note = {cited By 0},
	abstract = {Software maintenance of Open Source Software (OSS) has gained more attention in recent years and facilitated by the help of the Internet. Since volunteers in OSS do not record the effort of their contribution in maintenance tasks, researchers have to indirectly estimate the maintenance effort of such software. A review of the published OSS-MEE models has been performed using a set of 65 selected studies in a Systematic Mapping Study (SMS). This study analyses, discusses the state of the art about O-MEE and identifies trends through five additional Mapping Questions (MQs). In summary, various maintenance effort estimation (MEE) models were developed for OSS or industrial software. Researchers have mostly expressed the maintenance effort in terms of bug fixing, bug resolution time and severity in conjunction with bug report attributes. Regression Analysis and Bayesian Networks were most used estimation techniques, Recall, Precision, R2 and F-measure evaluation criteria in addition to k-fold cross validation method. Most of the models were implemented using WEKA, R software and MATLAB. More than half of the selected studies lacked of any validity analysis of their results. Trends are also discussed to identify a set of implications for researchers. {\textcopyright} 2020 Copyright for this paper by its authors.},
	keywords = {{\textquoteright}current, Bayesian networks, Bug-fixing, Computer software maintenance, Effort Estimation, Effort estimation model, Industrial software, Maintenance efforts, Maintenance tasks, Mapping, MATLAB, Open source software, Open systems, Open-source softwares, Regression analysis, State of the art, Systematic mapping studies},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142872497\&partnerID=40\&md5=d5e99ad4babcbb3a4fd5f50001fae51c},
	author = {Miloudi, C. and Cheikhi, L. and Abran, A. and Idri, A.}
}
@article {Elmidaoui2022202,
	title = {Towards a Taxonomy of Software Maintainability Predictors: A Detailed View},
	journal = {Lecture Notes in Networks and Systems},
	volume = {470 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {202-210},
	abstract = {To help practitioners and researchers choose the most suitable predictors when selecting from existing Software Product Maintainability Prediction (SPMP) models or designing new ones, a literature review of empirical studies on SPMP identified a large number of metrics or factors used as predictors of maintainability. However, there is a redundancy and ambiguity in both the naming and meaning of these predictors. To address this terminology issue, a one-level taxonomy of the SPMP predictors identified in the literature review have been proposed. This paper now proposes a more detailed two-level taxonomy where the first level refers to four categories, namely, software design, software size, quality attributes (or factors), and software process, the second to sub-categories, and predictors inventoried from empirical studies on SPMP. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-04829-6_18},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130308935\&doi=10.1007\%2f978-3-031-04829-6_18\&partnerID=40\&md5=e8e2d1411e399bbdb041c3d9d26ccad6},
	author = {Elmidaoui, S. and Cheikhi, L. and Idri, A. and Abran, A.}
}
@article {ElOuassif202150,
	title = {Classification techniques in breast cancer diagnosis: A systematic literature review},
	journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization},
	volume = {9},
	number = {1},
	year = {2021},
	note = {cited By 12},
	pages = {50-77},
	abstract = {Data mining (DM) consists in analysing a~set of observations to find unsuspected relationships and then summarising the data in new ways that are both understandable and useful. It has become widely used in various medical fields including breast cancer (BC), which is the most common cancer and the leading cause of death among women~worldwide.~BC diagnosis is a~challenging medical task and many studies have attempted to apply classification techniques to it. The objective of the present study is to identify studies on classification techniques~in~BC diagnosis and to analyse them from~three perspectives: classification techniques used, accuracy of the classifiers, and comparison of performance. We performed a~systematic literature review (SLR) of 176 selected studies published between January~2000 and November~2018. The results show that, of the nine classification techniques investigated, artificial neural networks, support vector machines and decision trees were the most frequently used. Moreover, artificial neural networks, support vector machines and ensemble classifiers performed better than the other techniques, with median accuracy values of 95\%, 95\% and 96\% respectively. Most of the selected studies (57.4\%) used datasets containing different types of images such as mammographic, ultrasound, and microarray images. {\textcopyright} 2021 Informa UK Limited, trading as Taylor \& Francis Group.},
	keywords = {Article, Artificial intelligence, artificial neural network, Breast Cancer, Breast cancer diagnosis, cancer diagnosis, cause of death, Causes of death, Classification (of information), Classification technique, Comparison of performance, Computer aided diagnosis, data extraction, Data mining, data synthesis, decision tree, Decision trees, Diseases, human, k nearest neighbor, Machine learning, Medical fields, Medical informatics, Network support, Neural networks, publication, qualitative research, Quality control, support vector machine, Support vector machine classifiers, Support vector machines, Support vectors machine, Systematic literature review, Systematic Review, validity},
	doi = {10.1080/21681163.2020.1811159},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098842973\&doi=10.1080\%2f21681163.2020.1811159\&partnerID=40\&md5=9a48998b1c44d263863efcfb25f9920f},
	author = {ElOuassif, B. and Idri, A. and Hosni, M. and Abran, A.}
}
@conference {Abnane20211,
	title = {Heterogeneous ensemble imputation for software development effort estimation},
	booktitle = {PROMISE 2021 - Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering, co-located with ESEC/FSE 2021},
	year = {2021},
	note = {cited By 1},
	pages = {1-10},
	abstract = {Choosing the appropriate Missing Data (MD) imputation technique for a given Software development effort estimation (SDEE) technique is not a trivial task. In fact, the impact of the MD imputation on the estimation output depends on the dataset and the SDEE technique used and there is no best imputation technique in all contexts. Thus, an attractive solution is to use more than one single imputation technique and combine their results for a final imputation outcome. This concept is called ensemble imputation and can help to significantly improve the estimation accuracy. This paper develops and evaluates a heterogeneous ensemble imputation whose members were the four single imputation techniques: K-Nearest Neighbors (KNN), Expectation Maximization (EM), Support Vector Regression (SVR), and Decision Trees (DT). The impact of the ensemble imputation was evaluated and compared with those of the four single imputation techniques on the accuracy measured in terms of the standardized accuracy criterion of four SDEE techniques: Case Based Reasoning (CBR), Multi-Layers Perceptron (MLP), Support Vector Regression (SVR) and Reduced Error Pruning Tree (REPTree). The Wilcoxon statistical test was also performed in order to assess whether the results are significant. All the empirical evaluations were carried out over the six datasets, namely, ISBSG, China, COCOMO81, Desharnais, Kemerer, and Miyazaki. Results show that the use of heterogeneous ensemble-based imputation instead single imputation significantly improved the accuracy of the four SDEE techniques. Indeed, the ensemble imputation technique was ranked either first or second in all contexts. {\textcopyright} 2021 ACM.},
	keywords = {Case based reasoning, Casebased reasonings (CBR), Decision trees, Empirical evaluations, Expectation Maximization, Forestry, Heterogeneous ensembles, Imputation techniques, K nearest neighbor (KNN), Maximum principle, Multilayer neural networks, Nearest neighbor search, Predictive analytics, Software design, Software development effort, Support vector regression, Support vector regression (SVR)},
	doi = {10.1145/3475960.3475984},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113586361\&doi=10.1145\%2f3475960.3475984\&partnerID=40\&md5=7bd6bbfb801a84cd7694e3713d2d0081},
	author = {Abnane, I. and Idri, A. and Hosni, M. and Abran, A.}
}
@article {Hosni2021,
	title = {On the value of filter feature selection techniques in homogeneous ensembles effort estimation},
	journal = {Journal of Software: Evolution and Process},
	volume = {33},
	number = {6},
	year = {2021},
	note = {cited By 1},
	abstract = {Software development effort estimation (SDEE) remains as the principal activity in software project management planning. Over the past four decades, several methods have been proposed to estimate the effort required to develop a software system, including more recently machine learning (ML) techniques. Because ML performance accuracy depends on the features that feed the ML technique, selecting the appropriate features in the preprocessing data step is important. This paper investigates three filter feature selection techniques to check the predictive capability of four single ML techniques: K-nearest neighbor, support vector regression, multilayer perceptron, and decision trees and their homogeneous ensembles over six well-known datasets. Furthermore, the single and ensembles techniques were optimized using the grid search optimization method. The results suggest that the three filter feature selection techniques investigated improve the reasonability and the accuracy performance of the four single techniques. Moreover, the homogeneous ensembles are statistically more accurate than the single techniques. Finally, adopting a random process (i.e., random subspace method) to select the inputs feature for ML technique is not always effective to generate an accurate homogeneous ensemble. {\textcopyright} 2021 John Wiley \& Sons, Ltd.},
	keywords = {Decision trees, Effort Estimation, Feature extraction, K-nearest neighbors, Multilayer neural networks, Nearest neighbor search, Object oriented programming, Predictive capabilities, Project management, Random processes, Random subspace method, Selection techniques, Software design, Software development effort, Software project management, Software systems, Support vector regression},
	doi = {10.1002/smr.2343},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103402056\&doi=10.1002\%2fsmr.2343\&partnerID=40\&md5=0f4c04b547f3d628d6db8d65b74912e5},
	author = {Hosni, M. and Idri, A. and Abran, A.}
}
@article {Abnane2020,
	title = {Fuzzy case-based-reasoning-based imputation for incomplete data in software engineering repositories},
	journal = {Journal of Software: Evolution and Process},
	volume = {32},
	number = {9},
	year = {2020},
	note = {cited By 6},
	abstract = {Missing data is a serious issue in software engineering because it can lead to information loss and bias in data analysis. Several imputation techniques have been proposed to deal with both numerical and categorical missing data. However, most of those techniques used is simple reuse techniques originally designed for numerical data, which is a problem when the missing data are related to categorical attributes. This paper aims (a) to propose a new fuzzy case-based reasoning (CBR) imputation technique designed for both numerical and categorical data and (b) to evaluate and compare the performance of the proposed technique with the k-nearest neighbor (KNN) imputation technique in terms of error and accuracy under different missing data percentages and missingness mechanisms in four software engineering data sets. The results suggest that the proposed fuzzy CBR technique outperformed KNN in terms of imputation error and accuracy regardless of the missing data percentage, missingness mechanism, and data set used. Moreover, we found that the missingness mechanism has an important impact on the performance of both techniques. The results are encouraging in the sense that using an imputation technique designed for both categorical and numerical data is better than reusing methods originally designed for numerical data. {\textcopyright} 2020 John Wiley \& Sons, Ltd.},
	keywords = {accuracy, Case based reasoning, Categorical data, Clustering algorithms, Empirical Software Engineering, Fuzzy analogy, imputation, Missing data, Nearest neighbor search, Numerical methods, Software engineering},
	doi = {10.1002/smr.2260},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081904769\&doi=10.1002\%2fsmr.2260\&partnerID=40\&md5=762cb4270c6a55d209feaa8eb6df5c5f},
	author = {Abnane, I. and Idri, A. and Abran, A.}
}
@conference {Elmidaoui2020,
	title = {Predicting software maintainability using ensemble techniques and stacked generalization},
	booktitle = {CEUR Workshop Proceedings},
	volume = {2725},
	year = {2020},
	note = {cited By 1},
	abstract = {The prediction of software maintainability has emerged as an important research topic to address industry expectations for reducing costs, in particular maintenance costs. In the last decades, many studies have used single techniques to predict software maintainability but there is no agreement as to which technique can achieve the best prediction. Ensemble techniques, which combine two or more techniques, have been investigated in recent years. This study investigates ensemble techniques (homogeneous as well as heterogeneous) for predicting maintainability in terms of line code changes. To this end, well-known homogeneous ensembles such as Bagging, Boosting, Extra Trees, Gradient Boosting, and Random Forest are investigated first. Then the stacked generalization method is used to construct heterogeneous ensembles by combining the most accurate ones per dataset. The empirical results suggest that Gradient Boosting and Extra Trees are the best ensembles for all datasets, since they ranked first and second, respectively. Moreover, the findings of the evaluation of heterogeneous ensembles constructed using stacked generalization showed that they gave better prediction accuracy compared to all homogeneous ensembles. Copyright {\textcopyright} 2020 for this paper by its authors.},
	keywords = {Decision trees, Ensemble techniques, Forecasting, Forestry, Gradient boosting, Heterogeneous ensembles, Maintainability, Maintenance cost, Prediction accuracy, Research topics, Software engineering, Software maintainability, Stacked generalization},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098182236\&partnerID=40\&md5=32bab56e3a64ff6efa7e8717d9ee67c4},
	author = {Elmidaoui, S. and Cheikhi, L. and Idri, A. and Abran, A.}
}
@article {ElKoutbi2019,
	title = {Empirical evaluation of an entropy-based approach to estimation variation of software development effort},
	journal = {Journal of Software: Evolution and Process},
	volume = {31},
	number = {3},
	year = {2019},
	note = {cited By 0},
	doi = {10.1002/smr.2149},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063377815\&doi=10.1002\%2fsmr.2149\&partnerID=40\&md5=f8f11d4ac305f27600a3f8dec5af170c},
	author = {El Koutbi, S. and Idri, A. and Abran, A.}
}
@article {Hosni2019,
	title = {Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimation},
	journal = {Journal of Software: Evolution and Process},
	volume = {31},
	number = {2},
	year = {2019},
	note = {cited By 0},
	doi = {10.1002/smr.2117},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061585023\&doi=10.1002\%2fsmr.2117\&partnerID=40\&md5=72983b70d540ccb14da59885e96cc4ab},
	author = {Hosni, M. and Idri, A. and Abran, A.}
}
@article {Nassif2019,
	title = {Software development effort estimation using regression fuzzy models},
	journal = {Computational Intelligence and Neuroscience},
	volume = {2019},
	year = {2019},
	note = {cited By 0},
	doi = {10.1155/2019/8367214},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062599935\&doi=10.1155\%2f2019\%2f8367214\&partnerID=40\&md5=efd8130615f3d9857ff368174c7a2db1},
	author = {Nassif, A.B. and Azzeh, M. and Idri, A. and Abran, A.}
}
@article {Idri2018,
	title = {Evaluating Pred(p) and standardized accuracy criteria in software development effort estimation},
	journal = {Journal of Software: Evolution and Process},
	volume = {30},
	number = {4},
	year = {2018},
	doi = {10.1002/smr.1925},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045686530\&doi=10.1002\%2fsmr.1925\&partnerID=40\&md5=3a3d72a1a9b32d7a365db2168b5e3a45},
	author = {Idri, A. and Abnane, I. and Abran, A.}
}
@article {Idri2018,
	title = {Support vector regression-based imputation in analogy-based software development effort estimation},
	journal = {Journal of Software: Evolution and Process},
	volume = {30},
	number = {12},
	year = {2018},
	doi = {10.1002/smr.2114},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058493764\&doi=10.1002\%2fsmr.2114\&partnerID=40\&md5=09059bd4e9087fda5b7225f1c8aeea9d},
	author = {Idri, A. and Abnane, I. and Abran, A.}
}
@article {Hosni20185977,
	title = {On the value of parameter tuning in heterogeneous ensembles effort estimation},
	journal = {Soft Computing},
	volume = {22},
	number = {18},
	year = {2018},
	pages = {5977-6010},
	doi = {10.1007/s00500-017-2945-4},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035807309\&doi=10.1007\%2fs00500-017-2945-4\&partnerID=40\&md5=83d094f56b80a21295cf4230621f445d},
	author = {Hosni, M. and Idri, A. and Abran, A. and Nassif, A.B.}
}
@conference {Abnane20171302,
	title = {Empirical evaluation of fuzzy analogy for Software Development Effort Estimation},
	booktitle = {Proceedings of the ACM Symposium on Applied Computing},
	volume = {Part F128005},
	year = {2017},
	note = {cited By 0},
	pages = {1302-1304},
	abstract = {Software Development Effort Estimation (SDEE) plays a primary role in software project management. Among several techniques suggested for estimating software development effort, analogybased software effort estimation approaches stand out as promising techniques. In this paper, the performance of Fuzzy Analogy is compared with that of six other SDEE techniques (Linear Regression, Support Vector Regression, Multi-Layer Perceptron, M5P and Classical Analogy). The first step of the evaluation aimed to ensure that the SDEE techniques outperformed random guessing by using the Standardized Accuracy (SA). Then, we used a set of reliable performance measures and Borda count to rank them and identify which techniques are the most accurate. The results suggest that Fuzzy Analogy statistically outperformed the other SDEE techniques regardless of the dataset used. {\textcopyright} 2017 ACM.},
	doi = {10.1145/3019612.3019905},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020898807\&doi=10.1145\%2f3019612.3019905\&partnerID=40\&md5=df809b8bee59594505af4961af714d5e},
	author = {Abnane, I. and Idri, A. and Abran, A.}
}
@conference {Hosni2017174,
	title = {Heterogeneous Ensembles for Software Development Effort Estimation},
	booktitle = {Proceedings - 2016 3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016},
	year = {2017},
	note = {cited By 0},
	pages = {174-178},
	abstract = {Software effort estimation influences almost all the process of software development such as: bidding, planning, and budgeting. Hence, delivering an accurate estimation in early stages of the software life cycle may be the key of success of any project. To this aim, many solo techniques have been proposed to predict the effort required to develop a software system. Nevertheless, none of them proved to be suitable in all circumstances. Recently, Ensemble Effort Estimation has been investigated to estimate software effort and consists on generating the software effort by combining more than one solo estimation technique by means of a combination rule. In this study, a heterogeneous EEE based on four machine learning techniques was investigated using three linear rules and two well-known datasets. The results of this study suggest that the proposed heterogeneous EEE yields a very promising performance and there is no best combiner rule that can be recommended. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/ISCMI.2016.15},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034656098\&doi=10.1109\%2fISCMI.2016.15\&partnerID=40\&md5=86bd61c4459af4a3491046df1925ab2c},
	author = {Hosni, M. and Idri, A. and Nassif, A.B. and Abran, A.}
}
@article {Hosni20171,
	title = {On the value of parameter tuning in heterogeneous ensembles effort estimation},
	journal = {Soft Computing},
	year = {2017},
	note = {cited By 0; Article in Press},
	pages = {1-34},
	abstract = {Accurate estimation of software development effort estimation (SDEE) is fundamental for efficient management of software development projects as it assists software managers to efficiently manage their human resources. Over the last four decades, while software engineering researchers have used several effort estimation techniques, including those based on statistical and machine learning methods, no consensus has been reached on the technique that can perform best in all circumstances. To tackle this challenge, Ensemble Effort Estimation, which predicts software development effort by combining more than one solo estimation technique, has recently been investigated. In this paper, heterogeneous ensembles based on four well-known machine learning techniques (K-nearest neighbor, support vector regression, multilayer perceptron and decision trees) were developed and evaluated by investigating the impact of parameter values of the ensemble members on estimation accuracy. In particular, this paper evaluates whether setting ensemble parameters using two optimization techniques (e.g., grid search optimization and particle swarm) permits more accurate estimates of SDEE. The heterogeneous ensembles of this study were built using three combination rules (mean, median and inverse ranked weighted mean) over seven datasets. The results obtained suggest that: (1) Optimized single techniques using grid search or particle swarm optimization provide more accurate estimation; (2) in general ensembles achieve higher accuracy than their single techniques whatever the optimization technique used, even though ensembles do not dominate over all single techniques; (3) heterogeneous ensembles based on optimized single techniques provide more accurate estimation; and (4) generally, particle swarm optimization and grid search techniques generate ensembles with the same predictive capability. {\textcopyright} 2017 Springer-Verlag GmbH Germany, part of Springer Nature},
	doi = {10.1007/s00500-017-2945-4},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035807309\&doi=10.1007\%2fs00500-017-2945-4\&partnerID=40\&md5=83d094f56b80a21295cf4230621f445d},
	author = {Hosni, M. and Idri, A. and Abran, A. and Nassif, A.B.}
}
@conference {Idri2008,
	title = {Functional equivalence between radial basis function neural networks and Fuzzy analogy in software cost estimation},
	booktitle = {2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications, ICTTA},
	year = {2008},
	note = {cited By 0},
	doi = {10.1109/ICTTA.2008.4530015},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-49149083210\&doi=10.1109\%2fICTTA.2008.4530015\&partnerID=40\&md5=662843c9cdc63dbc94ac6b4d87307bb2},
	author = {Idri, A. and Zakrani, A. and Abran, A.}
}
@conference {Idri2008576,
	title = {Fuzzy radial basis function neural networks for web applications cost estimation},
	booktitle = {Innovations{\textquoteright}07: 4th International Conference on Innovations in Information Technology, IIT},
	year = {2008},
	note = {cited By 0},
	pages = {576-580},
	doi = {10.1109/IIT.2007.4430367},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-50249168929\&doi=10.1109\%2fIIT.2007.4430367\&partnerID=40\&md5=ee74c556489187d35257cf7c8b48c2c0},
	author = {Idri, A. and Zakrani, A. and Elkoutbi, M. and Abran, A.}
}
