Effort estimation in agile software development: a survey on the state of the practice

Context: There are numerous studies on effort estimation in Agile Software Development (ASD) and the state of the art in this area has been recently documented in a Systematic Literature Review (SLR). However, to date there are no studies on the state of the practice in this area, focusing on similar issues to those investigated in the above-mentioned SLR. Objectives: The aim of this paper is to report on the state of the practice on effort estimation in ASD, focusing on a wide range of aspects such as the estimation techniques and effort predictors used, to name a few. Method: A survey was carried out using as instrument an on-line questionnaire answered by agile practitioners who have experience in effort estimation. Results: Data was collected from 60 agile practitioners from 16 different countries, and the main findings are: 1) Planning poker (63%), analogy (47%) and expert judgment (38%) are frequently practiced estimation techniques in ASD; 2) Story points is the most frequently (62%) employed size metric, used solo or in combination with other metrics (e.g., function points); 3) Team's expertise level and prior experience are most commonly used cost drivers; 4) 52% of the respondents believe that their effort estimates on average are under/over estimated by an error of 25% or more; 5) Most agile teams take into account implementation and testing activities during effort estimation; and 6) Estimation is mostly performed at sprint and release planning levels in ASD. Conclusions: Estimation techniques that rely on experts' subjective assessment are the ones used the most in ASD, with effort underestimation being the dominant trend. Further, the use of multiple techniques in combination and story points seem to present a positive association with estimation accuracy, and team-related cost drivers are the ones used by most agile teams. Finally, requirements and management related issues are perceived as the main reasons for inaccurate estimates.