@article {Chadli2016,
	title = {Software project management tools in global software development: a systematic mapping study},
	journal = {SpringerPlus},
	volume = {5},
	number = {1},
	year = {2016},
	note = {cited By 0},
	abstract = {Global software development (GSD) which is a growing trend in the software industry is characterized by a highly distributed environment. Performing software project management (SPM) in such conditions implies the need to overcome new limitations resulting from cultural, temporal and geographic separation. The aim of this research is to discover and classify the various tools mentioned in literature that provide GSD project managers with support and to identify in what way they support group interaction. A systematic mapping study has been performed by means of automatic searches in five sources. We have then synthesized the data extracted and presented the results of this study. A total of 102 tools were identified as being used in SPM activities in GSD. We have classified these tools, according to the software life cycle process on which they focus and how they support the 3C collaboration model (communication, coordination and cooperation). The majority of the tools found are standalone tools (77\%). A small number of platforms (8\%) also offer a set of interacting tools that cover the software development lifecycle. Results also indicate that SPM areas in GSD are not adequately supported by corresponding tools and deserve more attention from tool builders. {\textcopyright} 2016, The Author(s).},
	doi = {10.1186/s40064-016-3670-7},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84996484137\&doi=10.1186\%2fs40064-016-3670-7\&partnerID=40\&md5=303aaf83e70281377e2a1497382f7f9f},
	author = {Chadli, S.Y.a and Idri, A.a and Ros, J.N.b and Fern{\'a}ndez-Alem{\'a}n, J.L.b and de Gea, J.M.C.b and Toval, A.b}
}
@article {Idri2016151,
	title = {Systematic literature review of ensemble effort estimation},
	journal = {Journal of Systems and Software},
	volume = {118},
	year = {2016},
	note = {cited By 1},
	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. {\textcopyright} 2016 Elsevier Inc. All rights reserved.},
	doi = {10.1016/j.jss.2016.05.016},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84969531836\&doi=10.1016\%2fj.jss.2016.05.016\&partnerID=40\&md5=4217c2107911348615a692540959501e},
	author = {Idri, A.a and Hosni, M.a and Abran, A.b}
}
@conference {Idri2016132,
	title = {Systematic mapping study of ensemble effort estimation},
	booktitle = {ENASE 2016 - Proceedings of the 11th International Conference on Evaluation of Novel Software Approaches to Software Engineering},
	year = {2016},
	note = {cited By 0},
	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. Copyright {\textcopyright} 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979496940\&partnerID=40\&md5=8108d3f05a4d4c689c6237a0019141b7},
	author = {Idri, A.a and Hosni, M.a and Abran, A.b}
}
@conference {ElBajta2015197,
	title = {Software cost estimation for global software development: A systematic map and review study},
	booktitle = {ENASE 2015 - Proceedings of the 10th International Conference on Evaluation of Novel Approaches to Software Engineering},
	year = {2015},
	note = {cited By 1},
	pages = {197-206},
	abstract = {Software cost estimation plays a central role in the success of software project management in the context of global software development (GSD). The importance of mastering software cost estimation may appear to be obvious. However, as regards the issue of customer satisfaction, end-users are often unsatisfied with software project management results. In this paper, a systematic mapping study (SMS) is carried out with the aim of summarising software cost estimation in the context of GSD research by answering nine mapping questions. A total, of 16 articles were selected and classified according to nine criteria: publication source, publication year, research type, research approach, contribution type, software cost estimation techniques, software cost estimation activity, cost drivers and cost estimation performances for GSD projects. The results show that the interest in estimating software cost for GSD projects has increased in recent years and reveal that conferences are the most frequently targeted publications. Most software cost estimation for GSD research has focused on theory. The dominant contribution type of software cost estimation for GSD research is that of models, while the predominant activity was identified as being software development cost. Identifying empirical solutions to address software cost estimation for GSD is a promising direction for researchers.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84933555636\&partnerID=40\&md5=3e6ff23ed380cdb81d078b9b47e09964},
	author = {El Bajta, M.a and Idri, A.a and Fern{\'a}ndez-Alem{\'a}n, J.L.b and Ros, J.N.b and Toval, A.b}
}
@conference {Idri2015,
	title = {Systematic mapping study of missing values techniques in software engineering data},
	booktitle = {2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2015 - Proceedings},
	year = {2015},
	note = {cited By 0},
	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. {\textcopyright} 2015 IEEE.},
	doi = {10.1109/SNPD.2015.7176280},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84947080832\&doi=10.1109\%2fSNPD.2015.7176280\&partnerID=40\&md5=d82d376ab83550a21470787ee8a42f45},
	author = {Idri, A.a and Abnane, I.a and Abran, A.b}
}
@article {Amazal2014,
	title = {Software development effort estimation using classical and fuzzy analogy: A cross-validation comparative study},
	journal = {International Journal of Computational Intelligence and Applications},
	volume = {13},
	number = {3},
	year = {2014},
	note = {cited By 4},
	abstract = {Software effort estimation is one of the most important tasks in software project management. Of several techniques suggested for estimating software development effort, the analogy-based reasoning, or Case-Based Reasoning (CBR), approaches stand out as promising techniques. In this paper, the benefits of using linguistic rather than numerical values in the analogy process for software effort estimation are investigated. The performance, in terms of accuracy and tolerance of imprecision, of two analogy-based software effort estimation models (Classical Analogy and Fuzzy Analogy, which use numerical and linguistic values respectively to describe software projects) is compared. Three research questions related to the performance of these two models are discussed and answered. This study uses the International Software Benchmarking Standards Group (ISBSG) dataset and confirms the usefulness of using linguistic instead of numerical values in analogy-based software effort estimation models. {\textcopyright} Imperial College Press.},
	doi = {10.1142/S1469026814500138},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908567208\&doi=10.1142\%2fS1469026814500138\&partnerID=40\&md5=7cc9c1aeaf80c1739df9a20546ac1885},
	author = {Amazal, F.A.a and Idri, A.a and Abran, A.b}
}
@conference {Ouhbi2013231,
	title = {Software quality requirements: A systematic mapping study},
	booktitle = {Proceedings - Asia-Pacific Software Engineering Conference, APSEC},
	volume = {1},
	year = {2013},
	note = {cited By 3},
	pages = {231-238},
	abstract = {Software quality requirements (SQR) play a central role in software quality (SQ) success. The importance of mastering SQR can be seen as obvious; however, when it comes to customer satisfaction, end-users are often dissatisfied with SQ. In this paper, a systematic mapping study aims to summarize SQR research by answering nine mapping questions. In total, 51 articles were selected and classified according to multiple criteria: publication source, publication year, research type, research approach, contribution type of SQR literature, requirements engineering activity, well-known SQ model, software artifact and SQR type. The results show an increased interest in SQR research in recent years and reveal that conferences are the main SQR publication target. Most SQR research has used case studies. The dominant contribution type of SQR research is method while specification is the main requirements engineering activity identified. SQ models need to be more used for SQR identification. Design module and requirements documentation are the principal artifacts reported in SQR literature. External and internal SQR were the main SQR types addressed in literature. Identifying empirical solutions to address SQR is a promising research direction for researchers. {\textcopyright} 2013 IEEE.},
	doi = {10.1109/APSEC.2013.40},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84933501221\&doi=10.1109\%2fAPSEC.2013.40\&partnerID=40\&md5=f81ca9b922dd0cab1200996357d49459},
	author = {Ouhbi, S.a and Idri, A.a and Fern{\'a}ndez-Alem{\'a}n, J.L.b and Toval, A.b}
}
