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

TL;DR

This systematic literature review investigates two core questions in Agile effort estimation: (RQ1) why estimations are inaccurate, and (RQ2) how approaches can improve estimation accuracy. It synthesizes 82 primary studies across five digital libraries using SEGRESS-guided methodology and open card sorting to derive a taxonomy of causes and a taxonomy of improvement approaches. The key finding is that information quality of the input artifacts (stories, acceptance criteria, requirements) is the most common root cause of inaccuracy, with additional influences from team dynamics, estimation practices, project management, and business pressure. Regarding approaches, most work targets estimating effort (often with ML or parametric methods) rather than supporting the estimation process, and there are notable concerns about data leakage and validation realism. The results underscore a need for automated, industry-context validated methods to improve information quality and to support practitioners in Agile environments, bridging the gap between academic methods and practical adoption.

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.

Figures (3)

• Figure 1: The search strategy (left) and study selection (part) approaches we used in the systematic literature review.
• Figure 2: The number of selected studies published in each year.
• Figure 3: Categorization of the approaches to improve effort estimation based on the purposes of the approaches.