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A systematic mapping study on software effort estimation
Author(s):
1. AHLAM ALHADDAD: Department of Computer Science, Faculty of Computer Science and Information Technology, Sana'a University,Sana'a,Yemen
2. IBRAHIm Al-BALTAH: Department of Information Technology, Faculty of Computer Science and Information Technology, Sana'a University,Sana'a,Yemen
3. ABEDALLAH ABUALKISHIK: College of Computer and Information Technology, American University in the Emirates,Dubai, UAE,
4. MAJDI ABDELLATIEF: Department of Health Informatics, Inaya Medical Colleges,KSA,
5. ABDULMONEM ALI AL KHARUSI: Oman Research and Education Network,Muscat,Oman
Abstract:
Context: software effort estimation has been considered as one of the key drivers in software development success. A comprehensive understanding of state-of-the-art of software effort estimation techniques is very important. Objective: the aim of this study is to identify and characterize the existing software effort estimation techniques and to points insights of this research field. Method: a systematic mapping study on 136 primary studies was conducted to answer six research questions. Results: the study revealed that most of the existing work have used MMRE, MRE, and PRED for measuring the accuracy of effort estimation, where NASA93 and COCOM81 were the most used dataset. Furthermore, most of the reviewed studies attempted to use machine learning methods, whereas other studies proposed hybrid models. With respect to size metrics, most of the reviewed studies used line of code ( KLOC/ LOC/SLOC). Conclusion: new research should be carried out and oriented towards studying the relationship between the various factors that increase or decrease software effort such as, project type, team member's expertise, required software reliability, and software complexity, which can be very useful to enhance effort estimation techniques.
Page(s): 3619-3643
DOI: DOI not available
Published: Journal: Journal of Theoretical and Applied Information Technology, Volume: 98, Issue: 17, Year: 2020
Keywords:
Systematic mapping study , Software Effort Estimation
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