@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: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}
}
@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}
}
