@article {11128948620160201,
	title = {Accuracy Comparison of Analogy-Based Software Development Effort Estimation Techniques.},
	journal = {International Journal of Intelligent Systems},
	volume = {31},
	number = {2},
	year = {2016},
	pages = {128 - 152},
	abstract = {Estimation by analogy is a commonly used software effort estimation technique and a suitable alternative to other conventional estimation techniques: It predicts the effort of the target project using information from former similar projects. While it is relatively easy to handle numerical attributes, dealing with categorical attributes is one of the most difficult issues for analogy-based estimation techniques. Therefore, we propose, in this paper, a novel analogy-based approach, called 2FA-kprototypes, to predict effort when software projects are described by a mix of numerical and categorical attributes. To this aim, the well-known fuzzy k-prototypes algorithm is integrated into the process of estimation by analogy. The estimation accuracy of 2FA-kprototypes was evaluated and compared with that of two techniques: (1) classical analogy-based technique and (2) 2FA-kmodes, which is a technique that we have developed recently. The comparison was performed using four data sets that are},
	keywords = {Algorithms, Artificial intelligence, Computer software development, Fuzzy control systems, Numerical analysis, Prototypes},
	issn = {08848173},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=111289486\&site=ehost-live},
	author = {Idri, Ali and Amazal, Fatima Azzahra and Abran, Alain}
}
@article { ISI:000365798200003,
	title = {Accuracy Comparison of Analogy-Based Software Development Effort Estimation Techniques},
	journal = {INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS},
	volume = {31},
	number = {2, SI},
	year = {2016},
	month = {FEB},
	pages = {128-152},
	abstract = {Estimation by analogy is a commonly used software effort estimation technique and a suitable alternative to other conventional estimation techniques: It predicts the effort of the target project using information from former similar projects. While it is relatively easy to handle numerical attributes, dealing with categorical attributes is one of the most difficult issues for analogy-based estimation techniques. Therefore, we propose, in this paper, a novel analogy-based approach, called 2FA-kprototypes, to predict effort when software projects are described by a mix of numerical and categorical attributes. To this aim, the well-known fuzzy k-prototypes algorithm is integrated into the process of estimation by analogy. The estimation accuracy of 2FA-kprototypes was evaluated and compared with that of two techniques: (1) classical analogy-based technique and (2) 2FA-kmodes, which is a technique that we have developed recently. The comparison was performed using four data sets that are quite diverse and have different sizes: ISBSG, COCOMO, USP05-FT, and USP05-RQ. The results obtained showed that both 2FA-kprototypes and 2FA-kmodes perform better than classical analogy. (C) 2015 Wiley Periodicals, Inc.},
	issn = {0884-8173},
	doi = {10.1002/int.21748},
	author = {Idri, Ali and Amazal, Fatima Azzahra and Abran, Alain}
}
@conference { ISI:000389715100001,
	title = {Dealing with Missing Values in Software Project Datasets: A Systematic Mapping Study},
	booktitle = {SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING},
	series = {Studies in Computational Intelligence},
	volume = {653},
	year = {2016},
	note = {17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Shanghai, PEOPLES R CHINA, MAY 30-JUN 01, 2016},
	pages = {1-16},
	publisher = {IEEE; Int Assoc Comp \& Informat Sci; SSCTL; IEEE Comp Soc; Cent Michigan Univ, Software Engn \& Informat Technol Inst; Shanghai Univ; Shanghai Key Lab Comp Software Testing \& Evaluating},
	organization = {IEEE; Int Assoc Comp \& Informat Sci; SSCTL; IEEE Comp Soc; Cent Michigan Univ, Software Engn \& Informat Technol Inst; Shanghai Univ; Shanghai Key Lab Comp Software Testing \& Evaluating},
	abstract = {Missing Values (MV) present a serious problem facing research in software engineering (SE) which is mainly based on statistical and/or data mining analysis of SE data. Therefore, various techniques have been developed to deal adequately with MV. In this paper, a systematic mapping study was carried out to summarize the existing techniques dealing with MV in SE datasets and to classify the selected studies according to six classification criteria: research type, research approach, MV technique, MV type, data types and MV objective. Publication channels and trends were also identified. As results, 35 papers concerning MV treatments of SE data were selected. This study shows an increasing interest in machine learning (ML) techniques especially the K-nearest neighbor algorithm (KNN) to deal with MV in SE datasets and found that most of the MV techniques are used to serve software development effort estimation techniques.},
	isbn = {978-3-319-33810-1; 978-3-319-33809-5},
	issn = {1860-949X},
	doi = {10.1007/978-3-319-33810-1\_1},
	author = {Idri, Ali and Abnane, Ibtissam and Abran, Alain},
	editor = {Lee, R}
}
@article { ISI:000392285600072,
	title = {Improved estimation of software development effort using Classical and Fuzzy Analogy ensembles},
	journal = {APPLIED SOFT COMPUTING},
	volume = {49},
	year = {2016},
	month = {DEC},
	pages = {990-1019},
	abstract = {Delivering an accurate estimate of software development effort plays a decisive role in successful management of a software project. Therefore, several effort estimation techniques have been proposed including analogy based techniques. However, despite the large number of proposed techniques, none has outperformed the others in all circumstances and previous studies have recommended generating estimation from ensembles of various single techniques rather than using only one solo technique. Hence, this paper proposes two types of homogeneous ensembles based on single Classical Analogy or single Fuzzy Analogy for the first time. To evaluate this proposal, we conducted an empirical study with 100/60 variants of Classical/Fuzzy Analogy techniques respectively. These variants were assessed using standardized accuracy and effect size criteria over seven datasets. Thereafter, these variants were clustered using the Scott-Knott statistical test and ranked using four unbiased errors measures. Moreover, three linear combiners were used to combine the single estimates. The results show that there is no best single Classical/Fuzzy Analogy technique across all datasets, and the constructed ensembles (Classical/Fuzzy Analogy ensembles) are often ranked first and their performances are, in general, higher than the single techniques. Furthermore, Fuzzy Analogy ensembles achieve better performance than Classical Analogy ensembles and there is no best Classical/Fuzzy ensemble across all datasets and no evidence concerning the best combiner. (C) 2016 Elsevier B.V. All rights reserved.},
	issn = {1568-4946},
	doi = {10.1016/j.asoc.2016.08.012},
	author = {Idri, Ali and Hosni, Mohamed and Abran, Alain}
}
@article { ISI:000377231800036,
	title = {Missing data techniques in analogy-based software development effort estimation},
	journal = {JOURNAL OF SYSTEMS AND SOFTWARE},
	volume = {117},
	year = {2016},
	month = {JUL},
	pages = {595-611},
	abstract = {Missing Data (MD) is a widespread problem that can affect the ability to use data to construct effective software development effort prediction systems. This paper investigates the use of missing data (MD) techniques with two analogy-based software development effort estimation techniques: Classical Analogy and Fuzzy Analogy. More specifically, we analyze the predictive performance of these two analogy-based techniques when using toleration, deletion or k-nearest neighbors (KNN) imputation techniques. A total of 1512 experiments were conducted involving seven data sets, three MD techniques (toleration, deletion and KNN imputation), three missingness mechanisms (MCAR: missing completely at random, MAR: missing at random, NIM: non-ignorable missing), and MD percentages from 10 percent to 90 percent. The results suggest that Fuzzy Analogy generates more accurate estimates in terms of the Standardized Accuracy measure (SA) than Classical Analogy regardless of the MD technique, the data set used, the missingness mechanism or the MD percentage. Moreover, this study found that the use of KNN imputation, rather than toleration or deletion, may improve the prediction accuracy of both analogy-based techniques. However, toleration, deletion and KNN imputation are affected by the missingness mechanism and the MD percentage, both of which have a strong negative impact upon effort prediction accuracy. (C) 2016 Elsevier Inc. All rights reserved.},
	issn = {0164-1212},
	doi = {10.1016/j.jss.2016.04.058},
	author = {Idri, Ali and Abnane, Ibtissam and Abran, Alain}
}
@article { ISI:000378664500010,
	title = {Systematic literature review of ensemble effort estimation},
	journal = {JOURNAL OF SYSTEMS AND SOFTWARE},
	volume = {118},
	year = {2016},
	month = {AUG},
	pages = {151-175},
	abstract = {The need to overcome the weaknesses of single estimation techniques for prediction tasks has given rise to ensemble methods in software development effort estimation (SDEE). An ensemble effort estimation (EEE) technique combines several of the single/classical models found in the SDEE literature. However, to the best of our knowledge, no systematic review has yet been performed with a focus on the use of EEE techniques in SDEE. The purpose of this review is to analyze EEE techniques from six viewpoints: single models used to construct ensembles, ensemble estimation accuracy, rules used to combine single estimates, accuracy comparison of EEE techniques with single models, accuracy comparison between EEE techniques and methodologies used to construct ensemble methods. We performed a systematic review of EEE studies published between 2000 and 2016, and we selected 24 of them to address the questions raised in this review. We found that EEE techniques may be separated into two types: homogeneous and heterogeneous, and that the machine learning single models are the most frequently employed in constructing EEE techniques. We also found that EEE techniques usually yield acceptable estimation accuracy, and in fact are more accurate than single models. (C) 2016 Elsevier Inc. All rights reserved.},
	issn = {0164-1212},
	doi = {10.1016/j.jss.2016.05.016},
	author = {Idri, Ali and Hosni, Mohamed and Abran, Alain}
}
@conference { ISI:000386649000023,
	title = {Systematic Mapping Study of Dealing with Error in Software Development Effort Estimation},
	booktitle = {2016 42ND EUROMICRO CONFERENCE ON SOFTWARE ENGINEERING AND ADVANCED APPLICATIONS (SEAA)},
	year = {2016},
	note = {42nd Euromicro Conference Series on Software Engineering and Advanced Applications (SEAA), Limassol, CYPRUS, AUG 31-SEP 02, 2016},
	pages = {140-147},
	publisher = {Univ Cyprus; Technolog Educ Inst Western Greece},
	organization = {Univ Cyprus; Technolog Educ Inst Western Greece},
	abstract = {Over the last decades, the software engineering community has investigated new techniques for software development effort estimation. Unfortunately, the estimates were not always accurate. Error approaches are then, an interesting track for improving the projects running performances and their financial profitability. The aim of this systematic mapping study is to summarize and synthesize the existing studies dealing with effort estimation error and uncertainty and to classify them based on research approaches, contribution types, accuracy criteria, datasets, error approaches and effort estimation techniques used. In total 19 papers published between 1990 and 2015 were selected. We observed a balance between the managerial approaches and the technical ones. Furthermore, the proposed error techniques and frameworks improve in general the accuracy of effort estimation techniques. Fuzzy logic, bootstrapping and risk analysis are promising avenues that could be combined with various estimation techniques.},
	isbn = {978-1-5090-2819-1},
	doi = {10.1109/SEAA.2016.39},
	author = {El Koutbi, Salma and Idri, Ali and Abran, Alain}
}
@conference { ISI:000391107900013,
	title = {Systematic Mapping Study of Ensemble Effort Estimation},
	booktitle = {ENASE: PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON EVALUATION OF NOVEL SOFTWARE APPROACHES TO SOFTWARE ENGINEERING},
	year = {2016},
	note = {11th International Conference on Evaluation of Novel Software Approaches to Software Engineering, Rome, ITALY, APR 27-28, 2016},
	pages = {132-139},
	abstract = {Ensemble methods have been used recently for prediction in data mining area in order to overcome the weaknesses of single estimation techniques. This approach consists on combining more than one single technique to predict a dependent variable and has attracted the attention of the software development effort estimation (SDEE) community. An ensemble effort estimation (EEE) technique combines several existing single/classical models. In this study, a systematic mapping study was carried out to identify the papers based on EEE techniques published in the period 2000-2015 and classified them according to five classification criteria: research type, research approach, EEE type, single models used to construct EEE techniques, and rule used the combine single estimates into an EEE technique. Publication channels and trends were also identified. Within the 16 studies selected, homogeneous EEE techniques were the most investigated. Furthermore, the machine learning single models were the most frequently employed to construct EEE techniques and two types of combiner (linear and non-linear) have been used to get the prediction value of an ensemble.},
	isbn = {978-989-758-189-2},
	doi = {10.5220/0005822701320139},
	author = {Idri, Ali and Hosni, Mohamed and Abran, Alain},
	editor = {Maciaszek, L and Filipe, J}
}
@article { ISI:000375706200001,
	title = {Usability evaluation of mobile applications using ISO 9241 and ISO 25062 standards},
	journal = {SPRINGERPLUS},
	volume = {5},
	year = {2016},
	month = {APR 29},
	abstract = {This paper presents an empirical study based on a set of measures to evaluate the usability of mobile applications running on different mobile operating systems, including Android, iOS and Symbian. The aim is to evaluate empirically a framework that we have developed on the use of the Software Quality Standard ISO 9126 in mobile environments, especially the usability characteristic. To do that, 32 users had participated in the experiment and we have used ISO 25062 and ISO 9241 standards for objective measures by working with two widely used mobile applications: Google Apps and Google Maps. The QUIS 7.0 questionnaire have been used to collect measures assessing the users{\textquoteright} level of satisfaction when using these two mobile applications. By analyzing the results we highlighted a set of mobile usability issues that are related to the hardware as well as to the software and that need to be taken into account by designers and developers in order to improve the usability of mobile applications.},
	issn = {2193-1801},
	doi = {10.1186/s40064-016-2171-z},
	author = {Moumane, Karima and Idri, Ali and Abran, Alain}
}
@article { ISI:000347022800012,
	title = {Analogy-based software development effort estimation: A systematic mapping and review},
	journal = {INFORMATION AND SOFTWARE TECHNOLOGY},
	volume = {58},
	year = {2015},
	month = {FEB},
	pages = {206-230},
	abstract = {Context: Analogy-based Software development Effort Estimation (ASEE) techniques have gained considerable attention from the software engineering community. However, existing systematic map and review studies on software development effort prediction have not investigated in depth several issues of ASEE techniques, to the exception of comparisons with other types of estimation techniques. Objective: The objective of this research is twofold: (1) to classify ASEE studies which primary goal is to propose new or modified ASEE techniques according to five criteria: research approach, contribution type, techniques used in combination with ASEE methods, and ASEE steps, as well as identifying publication channels and trends and (2) to analyze these studies from five perspectives: estimation accuracy, accuracy comparison, estimation context, impact of the techniques used in combination with ASEE methods, and ASEE tools. Method: We performed a systematic mapping of studies for which the primary goal is to develop or to improve ASEE techniques published in the period 1990-2012, and reviewed them based on an automated search of four electronic databases. Results: In total, we identified 65 studies published between 1990 and 2012, and classified them based on our predefined classification criteria. The mapping study revealed that most researchers focus on addressing problems related to the first step of an ASEE process, that is, feature and case subset selection. The results of our detailed analysis show that ASEE methods outperform the eight techniques with which they were compared, and tend to yield acceptable results especially when combining ASEE techniques with Fuzzy Logic (FL) or Genetic Algorithms (GA). Conclusion: Based on the findings of this study, the use of other techniques such FL and GA in combination with an ASEE method is promising to generate more accurate estimates. However, the use of ASEE techniques by practitioners is still limited: developing more ASEE tools may facilitate the application of these techniques and then lead to increasing the use of ASEE techniques in industry. (C) 2014 Elsevier B.V. All rights reserved.},
	issn = {0950-5849},
	doi = {10.1016/j.infsof.2014.07.013},
	author = {Idri, Ali and Amazal, Fatima Azzahra and Abran, Alain}
}
@conference { ISI:000380431500128,
	title = {RBFN Networks-based Models for Estimating Software Development Effort: A Cross-validation Study},
	booktitle = {2015 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI)},
	year = {2015},
	note = {IEEE Symposium Series Computational Intelligence, Cape Town, SOUTH AFRICA, DEC 07-10, 2015},
	pages = {925-932},
	publisher = {IEEE; IEEE Computational Intelligence Soc; IEEE BigData},
	organization = {IEEE; IEEE Computational Intelligence Soc; IEEE BigData},
	abstract = {Software effort estimation is very crucial and there is always a need to improve its accuracy as much as possible. Several estimation techniques have been developed in this regard and it is difficult to determine which model gives more accurate estimation on which dataset. Among all proposed methods, the Radial Basis Function Neural (RBFN) networks models have presented promising results in software effort estimation. The main objective of this research is to evaluate the RBFN networks construction based on both hard and fuzzy C-means clustering algorithms using cross-validation approach. The objective of this replication study is to investigate if the RBFN-based models learned from the training data are able to estimate accurately the efforts of yet unseen data. This evaluation uses two historical datasets, namely COCOMO81 and ISBSG R8.},
	isbn = {978-1-4799-7560-0},
	doi = {10.1109/SSCI.2015.136},
	author = {Idri, Ali and Hassani, Aya and Abran, Alain}
}
@conference { ISI:000380431500135,
	title = {RBFN Networks-based Models for Estimating Software Development Effort: A Cross-validation Study},
	booktitle = {2015 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI)},
	year = {2015},
	note = {IEEE Symposium Series Computational Intelligence, Cape Town, SOUTH AFRICA, DEC 07-10, 2015},
	pages = {976-983},
	publisher = {IEEE; IEEE Computational Intelligence Soc; IEEE BigData},
	organization = {IEEE; IEEE Computational Intelligence Soc; IEEE BigData},
	abstract = {Software effort estimation is very crucial and there is always a need to improve its accuracy as much as possible. Several estimation techniques have been developed in this regard and it is difficult to determine which model gives more accurate estimation on which dataset. Among all proposed methods, the Radial Basis Function Neural (RBFN) networks models have presented promising results in software effort estimation. The main objective of this research is to evaluate the RBFN networks construction based on both hard and fuzzy C-means clustering algorithms using cross-validation approach. The objective of this replication study is to investigate if the RBFN-based models learned from the training data are able to estimate accurately the efforts of yet unseen data. This evaluation uses two historical datasets, namely COCOMO81 and ISBSG R8.},
	isbn = {978-1-4799-7560-0},
	doi = {10.1109/SSCI.2015.142},
	author = {Idri, Ali and Hassani, Aya and Abran, Alain}
}
@conference { ISI:000380439700114,
	title = {Systematic Mapping Study of Missing Values Techniques in Software Engineering Data},
	booktitle = {2015 16TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD)},
	year = {2015},
	note = {16th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Takamatsu, JAPAN, JUN 01-03, 2015},
	pages = {671-678},
	publisher = {IEEE; ACIS},
	organization = {IEEE; ACIS},
	abstract = {Missing Values (MV) present a serious problem facing research in software engineering (SE) which is mainly based on statistical and/or data mining analysis of SE data. The simple method of dealing with MV is to ignore data with missing observations. This leads to losing valuable information and then obtaining biased results. Therefore, various techniques have been developed to deal adequately with MV, especially those based on imputation methods. In this paper, a systematic mapping study was carried out to summarize the existing techniques dealing with MV in SE datasets and to classify the selected studies according to six classification criteria: research type, research approach, MV technique, MV type, data types and MV objective. Publication channels and trends were also identified. As results, 35 papers concerning MV treatments of SE data were selected. This study shows an increasing interest in machine learning (ML) techniques especially the K-nearest neighbor algorithm (KNN) to deal with MV in SE datasets and found that most of the MV techniques are used to serve software development effort estimation techniques.},
	isbn = {978-1-4799-8676-7},
	author = {Idri, Ali and Abnane, Ibtissam and Abran, Alain},
	editor = {Saisho, K}
}
@conference { ISI:000358736600001,
	title = {On the Use of Software Quality Standard ISO/IEC9126 in Mobile Environments},
	booktitle = {2013 20TH ASIA-PACIFIC SOFTWARE ENGINEERING CONFERENCE (APSEC 2013), VOL 1},
	series = {Asia-Pacific Software Engineering Conference},
	year = {2013},
	note = {20th Asia-Pacific Software Engineering Conference (APSEC), Chulalongkorn Univ, Fac Engn, Dept Comp Engn, Bangkok, THAILAND, DEC 02-05, 2013},
	pages = {1-8},
	publisher = {Prov Elect Author; Metropolitan Elect Author; Thailand Convent \& Exhibit Bur; IEEE Comp Soc},
	organization = {Prov Elect Author; Metropolitan Elect Author; Thailand Convent \& Exhibit Bur; IEEE Comp Soc},
	abstract = {The capabilities and resources offered by mobile technologies are still far from those provided by fixed environments, and this poses serious challenges, in terms of evaluating the quality of applications operating in mobile environments. This article presents a study to help quality managers apply the ISO 9126 standard on software quality, particularly the External Quality model, to mobile environments. The influence of the limitations of mobile technologies are evaluated for each software quality characteristic, based on the coverage rates of its external metrics, which are themselves influenced by these limitations. The degrees of this influence are discussed and aggregated to provide useful recommendations to quality managers for their evaluation of quality characteristics in mobile environments. These recommendations are intended for mobile software in general and aren{\textquoteright}t targeted a specific ones. The External Quality model is especially valuable for assessing the Reliability, Usability, and Efficiency characteristics, and illustrates very well the conclusive nature of the recommendations of this study. However, more study is needed on the other quality characteristics, in order to determine the relevance of evaluating them in mobile environments.},
	isbn = {978-1-4799-2143-0},
	issn = {1530-1362},
	doi = {10.1109/APSEC.2013.12},
	author = {Idri, Ali and Moumane, Karima and Abran, Alain},
	editor = {Muenchaisri, P and Rothermel, G}
}
@conference { ISI:000261578000113,
	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, VOLS 1-5},
	year = {2008},
	note = {3rd International Conference on Information and Communication Technologies, Damascus, SYRIA, APR 07-11, 2008},
	pages = {615+},
	abstract = {We show in this paper the equivalence between the radial basis function networks and Fuzzy analogy in the field of software cost estimation. We prove that under weak conditions, the three layers of RBFN are functionally equivalent to the three steps of fuzzy analogy. This functional equivalence implies that advances in each literature, such new learning rules or new similarity measures, can be applied to both models directly. Furthermore, this equivalence can help us to provide a natural interpretation of cost estimation models based on RBFN.},
	isbn = {978-1-4244-1751-3},
	author = {Idri, Ali and Zakrani, Abdelali and Abran, Alain}
}
@conference { ISI:000255983000005,
	title = {Fuzzy radial basis function neural networks for web applications cost estimation},
	booktitle = {2007 INNOVATIONS IN INFORMATION TECHNOLOGIES, VOLS 1 AND 2},
	year = {2007},
	note = {4th International Conference on Innovations in Information Technology, Dubai, U ARAB EMIRATES, NOV 18-20, 2007},
	pages = {21+},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The Fuzzy Radial basis function Neural Networks (FRBFN) for software cost estimation is designed by integrating the principles of RBFN and the fuzzy C-means clustering algorithm. The architecture of the network is suitably modified at the hidden layer to realise a novel neural implementation of the fuzzy clustering algorithm. Fuzzy set-theoretic concepts are incorporated at the hidden layer, enabling the model to handle uncertain and imprecise data, which can improve greatly the accuracy of obtained estimates. MMRE and Pred are used as measures of prediction accuracy for this comparative study. The results show that an RBFN using fuzzy C-means performs better than an RBFN using hard C-means. This study uses data on web applications from the Tukutuku database.},
	isbn = {978-1-4244-1840-4},
	author = {Idri, Ali and Zakrani, Abdelali and Elkoutbi, Mohamed and Abran, Alain}
}
