Contrasting ideal and realistic conditions as a means to improve judgment-based software development effort estimation

ContextThe effort estimates of software development work are on average too low. A possible reason for this tendency is that software developers, perhaps unconsciously, assume ideal conditions when they estimate the most likely use of effort. In this article, we propose and evaluate a two-step estimation process that may induce more awareness of the difference between idealistic and realistic conditions and as a consequence more realistic effort estimates. The proposed process differs from traditional judgment-based estimation processes in that it starts with an effort estimation that assumes ideal conditions before the most likely use of effort is estimated.ObjectiveThe objective of the paper is to examine the potential of the proposed method to induce more realism in the judgment-based estimates of work effort.MethodThree experiments with software professionals as participants were completed. In all three experiments there was one group of participants which followed the proposed and another group which followed the traditional estimation process. In one of the experiments there was an additional group which started with a probabilistically defined estimate of minimum effort before estimating the most likely effort.ResultsWe found, in all three experiments, that estimation of most likely effort seems to assume rather idealistic assumptions and that the use of the proposed process seems to yield more realistic effort estimates. In contrast, starting with an estimate of the minimum effort, rather than an estimate based on ideal conditions, did not have the same positive effect on the subsequent estimate of the most likely effort.ConclusionThe empirical results from our studies together with similar results from other domains suggest that the proposed estimation process is promising for the improvement of the realism of software development effort estimates.

► Software developers assume ideal conditions when estimating effort.
► Estimating ideal effort, then most likely effort improves estimation accuracy.
► Estimating minimum effort first does not lead to the same improvement.
► The improvement is a result of better separation of ideal and realistic conditions.