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Expert judgment in software effort estimation requires someone with previous experience in effort estimation who knows and understands the task under consideration to provide an approximation of effort. Expert judgment utilizes the knowledge of an expert and is a widely used strategy for software estimating (Shekhar & Kumar, 2016). However, expert judgment can exhibit bias by the estimator and relies on the expert's previous experience on similar projects to generate a realistic estimate (Khuat & Le, 2016). Expert judgment comprises two approaches: effort-time and effort-size (Arifin et al., 2017). Effort-time is an absolute value method, such as person-days or person-hours; effort-size is a relative measure such as story points (Arifin et al., 2017) or t-shirt sizing. McConnell (2006) states that using Using a top-down approach that decomposes tasks into a granularity that is less than about two days enhances the expert judgment's accuracy and effectiveness of expert judgment. Large task estimation is prone to error and more challenging to estimate; thus, decomposition provides higher accuracy.
Expert judgment is ta common estimation technique used in effort estimation in software development. Although there is high availability of commercial estimation tools and approaches, expert estimation remains the most widely used estimation methodology (Ivan & Despa, 2016; Shekhar & Kumar, 2016; Usman, Britto, Damm, & Börstler, 2018). Expert-based effort estimates result from quantitative intuition as experts seldom base estimates on explicit analytical argumentation (Jørgensen & Boehm, 2009). Expert judgment is a non-algorithmic technique and may be prone to error as estimations can be inconsistent, lack repeatability, and be overly dependent on human memory (Sehra et al., 2017). Estimation inaccuracies can stem from over-optimism and over-reliance on accuracy due to over-confidence in the estimator’s ability to deliver accurate estimations.