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Unrealistic project plans and cost overruns create problems for software professionals and their clients. In spite of much research and numerous attempts to learn from experience, the problem of inaccurate, frequently overoptimistic cost estimates hasn't been solved. This article summarizes some knowledge that could improve the estimation accuracy, but we still lack some knowledge that hinders us from being able to produce accurate estimates. [ABSTRACT FROM PUBLISHER]

The article presents a discussion of methods by which software clients can avoid the selection of providers with cost estimates that are overoptimistic. The guidelines that can be followed in order to avoid the situations of delivery problems and cost overruns that can result from the selection of overoptimistic bids are described. The need for companies to analyze the possibility that the bidding process could be associated with cost overruns is mentioned, noting that surveys cite several reasons for cost overruns but typically ignore the bidding process as a contributing factor.

The article presents a discussion on the use of formal models vis-a-vis expert judgment in software development. It discusses the differences between the two types of estimation where it is pointed out that the essential difference lies in the quantification step or the final step where the input is transformed into the effort estimate. Magne Jorgensen and Barry Boehm discuss each model and come up with a conclusion that the two estimation methods are complementary but Jorgensen presents a stronger point for judgment-based methods.

Software development effort estimates are frequently too low, which may lead to poor project plans and project failures. One reason for this bias seems to be that the effort estimates produced by software developers are affected by information that has no relevance for the actual use of effort. We attempted to acquire a better understanding of the underlying mechanisms and the robustness of this type of estimation bias. For this purpose, we hired 374 software developers working in outsourcing companies to participate in a set of three experiments. The experiments examined the connection between estimation bias and developer dimensions: self-construal (how one sees oneself), thinking style, nationality, experience, skill, education, sex, and organizational role. We found that estimation bias was present along most of the studied dimensions. The most interesting finding may be that the estimation bias increased significantly with higher levels of interdependence, i.e., with stronger emphasis on connectedness, social context, and relationships. We propose that this connection may be enabled by an activation of one's self-construal when engaging in effort estimation, and a connection between a more interdependent self-construal and increased search for indirect messages, lower ability to ignore irrelevant context, and a stronger emphasis on socially desirable responses. [ABSTRACT FROM PUBLISHER]

Studies in laboratory settings report that software development effort estimates can be strongly affected by effort-irrelevant and misleading information. To increase our knowledge about the importance of these effects in field settings, we paid 46 outsourcing companies from various countries to estimate the required effort of the same five software development projects. The companies were allocated randomly to either the original requirement specification or a manipulated version of the original requirement specification. The manipulations were as follows: 1) reduced length of requirement specification with no change of content, 2) information about the low effort spent on the development of the old system to be replaced, 3) information about the client's unrealistic expectations about low cost, and 4) a restriction of a short development period with start up a few months ahead. We found that the effect sizes in the field settings were much smaller than those found for similar manipulations in laboratory settings. Our findings suggest that we should be careful about generalizing to field settings the effect sizes found in laboratory settings. While laboratory settings can be useful to demonstrate the existence of an effect and better understand it, field studies may be needed to study the size and importance of these effects. [ABSTRACT FROM AUTHOR]