@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.}
}
@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.}
}
@conference {Miloudi2020251,
	title = {A Review of Open Source Software Maintenance Effort Estimation},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {251-256},
	abstract = {Open Source Software (OSS) is gaining interests of software engineering community as well as practitioners from industry with the growth of the internet. Studies in estimating maintenance effort (MEE) of such software product have been published in the literature in order to provide better estimation. The aim of this study is to provide a review of studies related to maintenance effort estimation for open source software (OSSMEE). To this end, a set of 60 primary empirical studies are selected from six electronic databases and a discussion is provided according to eight research questions (RQs) related to: publication year, publication source, datasets (OSS projects), metrics (independent variables), techniques, maintenance effort (dependent variable), validation methods, and accuracy criteria used in the empirical validation. This study has found that popular OSS projects have been used, Linear Regression, Na{\"\i}ve Bayes and k Nearest Neighbors were frequently used, and bug resolution was the most used regarding the estimation of maintenance effort for the future releases. A set of gaps are identified and recommendations for researchers are also provided. {\textcopyright} 2020 ACM.},
	keywords = {Computer software maintenance, Dependent variables, Electronic database, Empirical validation, Engineering community, Independent variables, Intelligent systems, K-nearest neighbors, Maintenance efforts, Nearest neighbor search, Open source software, Open systems, Research questions, Sodium compounds},
	doi = {10.1145/3419604.3419809},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096413214\&doi=10.1145\%2f3419604.3419809\&partnerID=40\&md5=7ad5b9eacb605d7875e2123cfec6ddeb},
	author = {Miloudi, C. and Cheikhi, L. and Idri, A.}
}
@article {Elmidaoui2019823,
	title = {Towards a Taxonomy of Software Maintainability Predictors},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {930},
	year = {2019},
	note = {cited By 0},
	pages = {823-832},
	doi = {10.1007/978-3-030-16181-1_77},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064883521\&doi=10.1007\%2f978-3-030-16181-1_77\&partnerID=40\&md5=0a2c124cf59cc02604b4d34b5ed98a5a},
	author = {Elmidaoui, S. and Cheikhi, L. and Idri, A.}
}
@article {Elmidaoui201826,
	title = {Accuracy comparison of empirical studies on software product maintainability prediction},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {746},
	year = {2018},
	pages = {26-35},
	doi = {10.1007/978-3-319-77712-2_3},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045339755\&doi=10.1007\%2f978-3-319-77712-2_3\&partnerID=40\&md5=7400e19626fc0aa4658b0ac497b89bda},
	author = {Elmidaoui, S. and Cheikhi, L. and Idri, A.}
}
@article {Cheikhi201836,
	title = {Measurement based E-government portals{\textquoteright} benchmarking framework: Architectural and procedural views},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {746},
	year = {2018},
	pages = {36-45},
	doi = {10.1007/978-3-319-77712-2_4},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045330135\&doi=10.1007\%2f978-3-319-77712-2_4\&partnerID=40\&md5=941334f001bfb6ab10e687f93e3f62a7},
	author = {Cheikhi, L. and Fath-Allah, A. and Idri, A. and Al-Qutaish, R.E.}
}
@conference {Fath-ALLAH2017326,
	title = {A Best Practice Based E-Government Portals{\textquoteright} Maturity Model-A Case Study},
	booktitle = {IEEE CIT 2017 - 17th IEEE International Conference on Computer and Information Technology},
	year = {2017},
	note = {cited By 0},
	pages = {326-331},
	abstract = {An e-government portal{\textquoteright}s maturity model is a set of incremental stages that defines the maturity of e-government portals. In fact, these models can be used by e-government agencies to identify the maturity rank of their portal and at the same time provide recommendations and guidelines for agencies to improve their portals{\textquoteright} maturity. In previous research studies, we have built an e-government portals{\textquoteright} best practice model and a maturity model that is based on the former model. Moreover, we have evaluated the model by a set of experts in the e-government field. The model has proved its validity and reliability within the set of the participants. The aim of this paper is to perform a case study on this maturity model on a real portal. Our findings show that the portal being measured is in the presence stage of maturity. {\textcopyright} 2017 IEEE.},
	doi = {10.1109/CIT.2017.23},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032359695\&doi=10.1109\%2fCIT.2017.23\&partnerID=40\&md5=765abc46de7761960104adbb2b22b013},
	author = {Fath-Allah, A. and Cheikhi, L. and Idri, A. and Al-Qutaish, R.}
}
@article {Haoues2017886,
	title = {A guideline for software architecture selection based on ISO 25010 quality related characteristics},
	journal = {International Journal of Systems Assurance Engineering and Management},
	volume = {8},
	year = {2017},
	note = {cited By 0},
	pages = {886-909},
	abstract = {As the complexity of software increases, the choice of the appropriate software architecture becomes a critical task. This paper provides a guideline for selecting the appropriate software architecture based on pertinent ISO 25010 quality characteristics. The guideline was established through an analytical survey of 113 papers published from 2010 to 2014. Through this survey, we first identified a set of commonly used software architectures in the software engineering literature. Secondly, we applied the Formal Concept Analysis technique to classify each one of these architectures according to ISO 25010 quality characteristics. Finally, we identified the relationships among ISO 25010 quality characteristics, which in turn helped us to develop a guideline on how to select the appropriate software architecture with respect to ISO 25010 quality characteristics. In order to make sure about the validation of the proposed guideline, a survey with industrial experts is in progress. Data were collected from two companies working in the software development field (ST2i and Telnet). {\textcopyright} 2016, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.},
	doi = {10.1007/s13198-016-0546-8},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034990679\&doi=10.1007\%2fs13198-016-0546-8\&partnerID=40\&md5=180857479792ea94cf87783efc8285da},
	author = {Haoues, M. and Sellami, A. and Ben-Abdallah, H. and Cheikhi, L.}
}
@conference {Idri2017,
	title = {A survey of secondary studies in software process improvement},
	booktitle = {Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA},
	year = {2017},
	note = {cited By 0},
	abstract = {Software Process Improvement (SPI) has become one of the main strategic objectives in software industry. Companies make more investments in implementing software quality standards and models that focus on process assessment to improve their performance and productivity. To achieve these goals, companies focus on improving their process by means of improvement initiatives which may be implemented. To help practitioners find more innovative ways to manage and implement software process improvement initiatives efficiently, an important number of studies related to this topic have been emerged in recent years. Some of them, referred to as secondary studies, focused on the interpretation and synthesis of available published research works by giving an up to date state of art about SPI. This state of the art is provided in a form of literature surveys or in a methodological form using well established approaches such as systematic reviews or systematic mappings or tertiary studies. The objective of this paper is to identify and present the current secondary studies on SPI. The purpose is to discuss methods that these literature reviews of SPI use, their quality, and specific subjects that they cover. A set of survey research questions have been proposed and discussed through the investigation of 70 selected secondary studies collected from different digital libraries. The results show that success factors and issues related to implementation of SPI initiatives are the most studied, and there is a need to address in depth the measurement aspects in SPI. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/AICCSA.2016.7945655},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021871351\&doi=10.1109\%2fAICCSA.2016.7945655\&partnerID=40\&md5=e6b8548942846520db1fe2f2d41c4ecc},
	author = {Idri, A. and Cheikhi, L.}
}
@conference {Elmidaoui2016,
	title = {A survey of empirical studies in software product maintainability prediction models},
	booktitle = {SITA 2016 - 11th International Conference on Intelligent Systems: Theories and Applications},
	year = {2016},
	note = {cited By 0},
	abstract = {Software product maintainability is critical to the achievement of the software product quality. In order to keep the software useful as long as possible, software product maintainability prediction (SPMP) has become an important endeavor. The objective of this paper is to identify and present the current research on SPMP. The search was conducted using digital libraries to And as much research papers as possible. Selected papers are classified according to the following survey classification criteria (SCs): research type, empirical type, publication year and channel. Based on the results of the survey, we provide a discussion of the current state of the art in software maintainability prediction models or techniques. We believe that this study will be a reliable basis for further research in software maintainability studies. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/SITA.2016.7772267},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010468522\&doi=10.1109\%2fSITA.2016.7772267\&partnerID=40\&md5=8184d51d9fbbba990544f8dc158d2375},
	author = {Elmidaoui, S. and Cheikhi, L. and Idri, A.}
}
@article {Cheikhi2012462,
	title = {Software productivity: Harmonization in ISO/IEEE software engineering standards},
	journal = {Journal of Software},
	volume = {7},
	number = {2},
	year = {2012},
	note = {cited By 2},
	pages = {462-470},
	doi = {10.4304/jsw.7.2.462-470},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857929017\&doi=10.4304\%2fjsw.7.2.462-470\&partnerID=40\&md5=55088a24a8af596776fe0e2ddf74892c},
	author = {Cheikhi, L. and Al-Qutaish, R.E. and Idri, A.}
}
