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.

背景：准确的工作量估计对于敏捷迭代开发的规划至关重要。敏捷估计通常依赖于基于共识的方法，例如规划扑克，与其他正式方法（例如 COSMIC）相比，该方法需要更少的时间和信息，但容易出现不准确的情况。了解估计不准确的常见原因以及所提出的方法如何帮助从业者至关重要。然而，先前的系统文献综述（SLR）仅关注估计实践（例如，[26, 127]）和工作量估计方法（例如，[6]）。目标：我们的目标是确定估计不准确的原因主题，并对改进工作量估计的方法进行分类。方法：我们进行了 SLR 并确定了关键主题和分类法。结果：估算不准确的原因与信息质量、团队、估算实践、项目管理和业务影响有关。文献中对工作量估计方法进行了最多的研究，而只有少数方法旨在支持工作量估计过程。然而，很少有自动化方法面临数据泄露和间接验证场景的风险。建议：从业者应该通过采用自动化方法来提高工作量估算的信息质量。未来的研究应该致力于提高信息质量，同时避免数据泄漏和间接验证场景。