A Systematic Literature Review on Reasons and Approaches for Accurate Effort Estimations in Agile

TL;DR

This systematic literature review identifies key themes and approaches related to reasons for inaccurate effort estimations in Agile, emphasizing the importance of information quality and proposing automated methods to enhance estimation accuracy.

Contribution

The paper provides a comprehensive taxonomy of reasons for estimation inaccuracies and classifies existing approaches, highlighting gaps and proposing directions for future research and practice.

Findings

Reasons for inaccuracies include information quality and team factors

Effort estimation approaches are extensively studied, but few support the estimation process directly

Automated approaches face challenges like data leakage and indirect validation

Abstract

Background: Accurate effort estimation is crucial for planning in Agile iterative development. Agile estimation generally relies on consensus-based methods like planning poker, which require less time and information than other formal methods (e.g., COSMIC) but are prone to inaccuracies. Understanding the common reasons for inaccurate estimations and how proposed approaches can assist practitioners is essential. However, prior systematic literature reviews (SLR) only focus on the estimation practices (e.g., [26, 127]) and the effort estimation approaches (e.g., [6]). Aim: We aim to identify themes of reasons for inaccurate estimations and classify approaches to improve effort estimation. Method: We conducted an SLR and identified the key themes and a taxonomy. Results: The reasons for inaccurate estimation are related to information quality, team, estimation practice, project management, and business influences. The effort estimation approaches were the most investigated in the literature, while only a few aim to support the effort estimation process. Yet, few automated approaches are at risk of data leakage and indirect validation scenarios. Recommendations: Practitioners should enhance the quality of information for effort estimation, potentially by adopting an automated approach. Future research should aim to improve the information quality, while avoiding data leakage and indirect validation scenarios.