A review article on software effort estimation in agile methodology

Sudarmaningtyas, Pantjawati and Rozlina, Mohamed (2021) A review article on software effort estimation in agile methodology. Pertanika Journal of Science & Technology (JST), 29 (2). 837 -861. ISSN 0128-7680. (Published)

A review article on software effort estimation in agile methodology.pdf
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (937kB) | Preview


Currently, Agile software development method has been commonly used in software development projects, and the success rate is higher than waterfall projects. The effort estimation in Agile is still a challenge because most existing means are developed based on the conventional method. Therefore, this study aimed to ascertain the software effort estimation method that is applied in Agile, the implementation approach, and the attributes that affect effort estimation. The results showed the top three estimation that is applied in Agile, are machine learning (37%), Expert Judgement (26%), and Algorithmic (21%). The implementation of all machine learning methods used a hybrid approach, which is a combination of machine learning and expert judgement, or a mix of two or more machine learning. Meanwhile, the implementation of effort estimation through a hybrid approach was only used in 47% of relevant articles. In addition, effort estimation in Agile involved twenty-four attributes, where Complexity, Experience, Size, and Time are the most commonly used and implemented.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Agile; Effort estimation attributes; Expert judgement; Hybrid approach; Software effort estimation
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Institute of Postgraduate Studies
Faculty of Computing
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 11 Feb 2022 07:36
Last Modified: 11 Feb 2022 07:36
URI: http://umpir.ump.edu.my/id/eprint/31907
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item