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Estimation is an important challenge in software engineering, determining cost, effort, and resource planning. This paper presents an adapted Multivocal Literature Review (MLR) synthesising formal and grey literature on four main estimation approaches identified in literature: algorithmic, expert-based, machine learning, and ensemble-based hybrid approaches. This review discusses the strengths and limitations of each approach, identifying the emerging role of ML models, while also examining non-ML based agile approaches such as planning poker and story points. The findings indicate that estimation is both complex and non-deterministic, where no single approach universally applies across all contexts. Ensemble-based hybrid models which employ a variety of estimation techniques in parallel report promising results in terms of accuracy and adaptability.

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A Review of Estimation in Software Engineering

  • 会议论文

  • 发表日期：

    2026年

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  • Kevin James Tomescu,
  • Niamh Gowran,
  • Lorena Gomez,
  • Eoin Delahunty,
  • Andrew McCarren,
  • Gerard Marks,
  • Murat Yilmaz,
  • Richard Messnarz,
  • Paul M. Clarke

摘要

Estimation is an important challenge in software engineering, determining cost, effort, and resource planning. This paper presents an adapted Multivocal Literature Review (MLR) synthesising formal and grey literature on four main estimation approaches identified in literature: algorithmic, expert-based, machine learning, and ensemble-based hybrid approaches. This review discusses the strengths and limitations of each approach, identifying the emerging role of ML models, while also examining non-ML based agile approaches such as planning poker and story points. The findings indicate that estimation is both complex and non-deterministic, where no single approach universally applies across all contexts. Ensemble-based hybrid models which employ a variety of estimation techniques in parallel report promising results in terms of accuracy and adaptability.