An industrial study of effort estimation in mobile app development using agile processes

Abstract

Estimates of software effort, size and cost are essential to a project manager, in order to plan and manage a project to prevent it from failing. The planning and development of mobile apps are unlike traditional software applications due to the characteristics of the mobile environment, including high autonomy requirements, market competition and many other constraints. The aim of this study is to investigate the current state of practice relating to the essential factors and optimal techniques for effort estimation that help and support an Agile team to give reliable estimates for developing a mobile app task. The results of a Systematic Literature Review (SLR) of effort estimation models in mobile app development are presented; this is followed by a summary of estimation techniques used across mobile apps. In particular, the focus in the SLR is on the software estimation models applicable to the Agile Software Development (ASD) process. From the SLR, some research gaps are suggested, along with possible future work on mobile effort estimation techniques in ASD. Following this, the research aim and objectives are provided for this study, as well as appropriate research methods, strategies, plans and designs. In this study 20 interviews were conducted in 18 different organisations, consisting of both structured and non-structured questions. The result revealed that Expert Judgment and Planning Poker were the most used estimation techniques in the organisations. Some challenges and issues were raised and discussed around these techniques. The results, in addition, revealed 68 comprehensive factors and appropriate techniques that could be used in effort estimation for development of a mobile app. A proposed effort estimation technique has been constructed from the interviews. Moreover, the study presents case studies in three companies that examined and evaluated their current estimation techniques. It proposes and has validated an estimation technique to enhance the accuracy of existing techniques. The study presents the effectiveness of the estimation factors/predictors in supporting a development team to manage, estimate and create subtasks for their user stories

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