There are large skill differences among software developers, and clients and managers will benefit from being able to identify those with better skill. This study examines the relations between low effort estimates, and other commonly used skill indicators, and measured programming skill. One hundred and four professional software developers were recruited. After skill-related information was collected, they were asked to estimate the effort for four larger and five smaller programming tasks. Finally, they completed a programming skill test. The lowest and most over-optimistic effort estimates for the larger tasks were given by those with the lowest programming skill, which is in accordance with the well-known Dunning-Kruger effect. For the smaller tasks, however, those with the lowest programming skill had the highest and most over-pessimistic estimates. The other programming skill indicators, such as length of experience, company assessed skill and self-assessed skill, were only moderately correlated with measured skill and not particularly useful in guiding developer skill identification. A practical implication is that for larger and more complex tasks, the use of low effort estimates and commonly used skill indicators as selection criteria leads to a substantial risk of selecting among the least skilled developers.

Purpose: This paper examines to what extent resource managers are likely to make normatively correct decisions in complex, but realistic, software development team resource management situations.

Research design: Three scenarios were designed, all of them with a software development project consisting of a higher and a lower productivity team. The resource managers were asked to decide which team to increase (or decrease) the productivity by 10% in order to minimize the total effort or the total duration of the software project. Ninety-nine software professionals, most of them with substantial resource management experience, were randomly allocated one of the scenarios.

Findings: Most of the resource managers perceived their scenario as realistic and occurring in practice. The decisions tended, however, to be different from the normatively correct ones. In particular, when the scenario structure resembled that used to document a time-saving bias, the great majority of the managers made non-normative decisions. The findings suggest that the resource managers made decisions based on simple heuristics, often leading to non-normative decisions. When asked about the decisions they would make in practice, more of the resource managers gave normatively correct responses. Our findings suggest a cost-saving potential from more awareness of how to make team resource management decisions.

Originality: The study may be the first to document non-normative team resource decisions, including those related to the time-saving bias, in the context of project management.

Context: Probabilistic effort estimates inform about the uncertainty and may give useful input to plans, budgets and investment analyses. Objective & Method: This paper introduces, motivates and illustrates two principles on how to evaluate the accuracy and other performance criteria of probabilistic effort estimates in software development contexts. Results: The first principle emphasizes a consistency between the estimation error measure and the loss function of the chosen type of probabilistic single point effort estimates. The second principle points at the importance of not just measuring calibration, but also informativeness of estimated prediction intervals and distributions. The relevance of the evaluation principles is illustrated by a performance evaluation of estimates from twenty-eight software professionals using two different uncertainty assessment methods to estimate the effort of the same thirty software maintenance tasks.