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An Empirical Study of Effort-Size and Effort-Time in Expert-Based Estimations

The misguided debates over the software effort estimation have so far resulted in no clear understanding within software engineering field. The great one remains lie between two prominent expert-based methods, Effort-Size (i.e., story points) and Effort-Time (i.e., ideal time). In other fields, A Theory of Magnitude shows the common processing mechanisms of human estimations in different physical magnitudes, i.e., time, space, and quantity. Moreover, it also appears in logical things, e.g., linguistic. Our methodologies are proposed to evaluate the similar linear regression and scalar variability effect through size and time estimations of software effort. As the results, we found that both methods have positive correlations to completion of the Actual Effort in hours. However, in our samples, Effort-Time seems relatively more fitted to the linear regression model than Effort-Size. The other finding is human tend to have larger standard deviation to the mean of size and time estimations (scalar variability effect) in wider ranges of issue. Estimations become noisier when ranges of the Actual Effort become bigger. Therefore, either Effort-Time or Effort-Size should not be taken generally as "silver bullet". We believe that a better understanding of these similar effects can improve the accuracy of software effort estimations, e.g., avoiding or improving the use of expert-based methods alone in wider ranges of software tasks.