@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.}
}
@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.}
}
@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.}
}
