Is it Possible to Disregard Obsolete Requirements? A Family of Experiments in Software Effort Estimation

TL;DR

This study investigates whether obsolete requirements bias software effort estimations, finding that their presence causes systematic overestimation, with implications for improving estimation accuracy in both research and practice.

Contribution

The paper presents six experiments using Bayesian analysis to quantify the effect of obsolete requirements on effort estimation, highlighting the bias and its potential cognitive origins.

Findings

Obsolete requirements cause overestimation of effort.

The overestimation effect is smaller in field settings.

Bayesian analysis provides confidence in results despite small samples.

Abstract

Context: Expert judgement is a common method for software effort estimations in practice today. Estimators are often shown extra obsolete requirements together with the real ones to be implemented. Only one previous study has been conducted on if such practices bias the estimations. Objective: We conducted six experiments with both students and practitioners to study, and quantify, the effects of obsolete requirements on software estimation. Method By conducting a family of six experiments using both students and practitioners as research subjects (N = 461), and by using a Bayesian Data Analysis approach, we investigated different aspects of this effect. We also argue for, and show an example of, how we by using a Bayesian approach can be more confident in our results and enable further studies with small sample sizes. Results: We found that the presence of obsolete requirements triggered an overestimation in effort across all experiments. The effect, however, was smaller in a field setting compared to using students as subjects. Still, the over-estimations triggered by the obsolete requirements were systematically around twice the percentage of the included obsolete ones, but with a large 95% credible interval. Conclusions: The results have implications for both research and practice in that the found systematic error should be accounted for in both studies on software estimation and, maybe more importantly, in estimation practices to avoid over-estimation due to this systematic error. We partly explain this error to be stemming from the cognitive bias of anchoring-and-adjustment, i.e. the obsolete requirements anchored a much larger software. However, further studies are needed in order to accurately predict this effect.