research-article

Research patterns and trends in software effort estimation

Information and Software Technology, Volume 91, Issue C

Pages 1 - 21

https://doi.org/10.1016/j.infsof.2017.06.002

Published: 01 November 2017 Publication History

Abstract

This study identified research trends prevailing in software effort estimation literature.Latent Dirichlet Allocation (LDA) was applied to the corpus of 1178 articles.This study established the semantic mapping between research patterns and trends. ContextSoftware effort estimation(SEE) is most crucial activity in the field of software engineering. Vast research has been conducted in SEE resulting into a tremendous increase in literature. Thus it is of utmost importance to identify the core research areas and trends in SEE which may lead the researchers to understand and discern the research patterns in large literature dataset. ObjectiveTo identify unobserved research patterns through natural language processing from a large set of research articles on SEE published during the period 1996 to 2016. MethodA generative statistical method, called Latent Dirichlet Allocation(LDA), applied on a literature dataset of 1178 articles published on SEE. ResultsAs many as twelve core research areas and sixty research trends have been revealed; and the identified research trends have been semantically mapped to associate core research areas. ConclusionsThis study summarises the research trends in SEE based upon a corpus of 1178 articles. The patterns and trends identified through this research can help in finding the potential research areas.

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Cited By

Mustafa EOsman R(2024)A random forest model for early-stage software effort estimation for the SEERA datasetInformation and Software Technology10.1016/j.infsof.2024.107413169:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.infsof.2024.107413
Sakshi Kukreja V(2023)Recent trends in mathematical expressions recognitionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119028213:PBOnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119028
Abnane IIdri AAbran A(2023)Optimized fuzzy clustering‐based k‐nearest neighbors imputation for mixed missing data in software development effort estimationJournal of Software: Evolution and Process10.1002/smr.252936:4Online publication date: 4-Jan-2023
https://dl.acm.org/doi/10.1002/smr.2529
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Index Terms

Research patterns and trends in software effort estimation
1. Computing methodologies
  1. Machine learning

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cover image Information and Software Technology

Information and Software Technology Volume 91, Issue C

November 2017

198 pages

ISSN:0950-5849

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Published: 01 November 2017

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Mustafa EOsman R(2024)A random forest model for early-stage software effort estimation for the SEERA datasetInformation and Software Technology10.1016/j.infsof.2024.107413169:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.infsof.2024.107413
Sakshi Kukreja V(2023)Recent trends in mathematical expressions recognitionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119028213:PBOnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119028
Abnane IIdri AAbran A(2023)Optimized fuzzy clustering‐based k‐nearest neighbors imputation for mixed missing data in software development effort estimationJournal of Software: Evolution and Process10.1002/smr.252936:4Online publication date: 4-Jan-2023
https://dl.acm.org/doi/10.1002/smr.2529
Matsubara PGadelha BSteinmacher IConte T(2022)SEXTAMTJournal of Systems and Software10.1016/j.jss.2021.111148185:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.jss.2021.111148
Sharma CSharma SSakshi (2022)Latent DIRICHLET allocation (LDA) based information modelling on BLOCKCHAIN technology: a review of trends and research patterns used in integrationMultimedia Tools and Applications10.1007/s11042-022-13500-z81:25(36805-36831)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s11042-022-13500-z
Zhang XYu YWang TRastogi AWang H(2022)Pull request latency explained: an empirical overviewEmpirical Software Engineering10.1007/s10664-022-10143-427:6Online publication date: 4-Jul-2022
https://dl.acm.org/doi/10.1007/s10664-022-10143-4
Amasaki SAman HYokogawa T(2022)An extended study on applicability and performance of homogeneous cross-project defect prediction approaches under homogeneous cross-company effort estimation situationEmpirical Software Engineering10.1007/s10664-021-10103-427:2Online publication date: 27-Jan-2022
https://dl.acm.org/doi/10.1007/s10664-021-10103-4
Thakur DSingh JDhiman GShabaz MGera T(2021)Identifying Major Research Areas and Minor Research Themes of Android Malware Analysis and Detection Field Using LSAComplexity10.1155/2021/45510672021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/4551067
Chiyangwa Tvan Biljon JRenaud KDavidrajuh ROwolawi P(2021)Natural Language Processing Techniques to Reveal Human-Computer Interaction for Development Research TopicsProceedings of the International Conference on Artificial Intelligence and its Applications10.1145/3487923.3487932(1-7)Online publication date: 9-Dec-2021
https://dl.acm.org/doi/10.1145/3487923.3487932
Abnane IIdri AHosni MAbran AMcIntosh SXia XAmasaki S(2021)Heterogeneous ensemble imputation for software development effort estimationProceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering10.1145/3475960.3475984(1-10)Online publication date: 19-Aug-2021
https://dl.acm.org/doi/10.1145/3475960.3475984
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