Dealing with Missing Values in Software Project Datasets: A Systematic Mapping Study

TitreDealing with Missing Values in Software Project Datasets: A Systematic Mapping Study
Publication TypeConference Paper
Year of Publication2016
AuthorsIdri, A, Abnane, I, Abran, A
EditorLee, R
Conference NameSOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING
PublisherIEEE; 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
ISBN Number978-3-319-33810-1; 978-3-319-33809-5
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.

DOI10.1007/978-3-319-33810-1\_1
Revues: 

Partenaires

Localisation


Location map

Suivez-nous sur

  

Contactez-nous

ENSIAS

Avenue Mohammed Ben Abdallah Regragui, Madinat Al Irfane, BP 713, Agdal Rabat, Maroc

Résultat de recherche d'images pour "icone fax" Télécopie : (+212) 5 37 77 72 30

    Compteur de visiteurs:280,166
    Education - This is a contributing Drupal Theme
    Design by WeebPal.