Effort Estimation in Agile Software Development: An Updated Review

• Published in: International journal of software engineering and knowledge engineering Vol. 28; no. 11n12; pp. 1811 - 1831
• Main Authors: Dantas, Emanuel, Perkusich, Mirko, Dilorenzo, Ednaldo, Santos, Danilo F. S., Almeida, Hyggo, Perkusich, Angelo
• Format: Journal Article
• Language: English
• Published: Singapore World Scientific Publishing Co. Pte., Ltd 01.11.2018
• Subjects: Artificial intelligence; Cost analysis; Identification methods; Literature reviews; Machine learning; Software development; State-of-the-art reviews; Upgrading
• ISSN: 0218-1940; 1793-6403
• DOI: 10.1142/S0218194018400302

One of the main issues of an agile software project is how to accurately estimate development effort. In 2014, a Systematic Literature Review (SLR) regarding this subject was published. The authors concluded that there were several gaps in the literature, such as the low level of accuracy of the techniques and little consensus on appropriate cost drivers. The goal of our work is to provide an updated review of the state of the art based on this reference SLR work. We applied a Forward Snowballing approach, in which our seed set included the former SLR and its selected papers. We identified a strong indication of solutions based on Artificial Intelligence and Machine Learning methods for effort estimation in Agile Software Development (ASD). We also identified that there is a gap in terms of agreement on suitable cost drivers. Thus, we applied Thematic Analysis in the selected papers and identified a representative set of 10 cost drivers for effort estimation. This updated review of the state of the art resulted in 24 new relevant papers selected.