Almost since people started building software systems, effort estimation has been a hot topic for researchers.
Effort estimation models are necessarily driven by the available data (the Putnam model is one of few whose theory is based on more than arm waving). General information about source code can often be obtained (e.g., size in lines of code), and before package software and open source, software with roughly the same functionality was being implemented in lots of organizations.
Estimation models based on source code characteristics proliferated, e.g., COCOMO. What these models overlooked was human variability in implementing the same functionality (a standard deviation that is 25% of the actual size is going to introduce a lot of uncertainty into any effort estimate), along with the more obvious assumption that effort was closely tied to source code characteristics.
The advent of
high-tech clueless button pushing machine learning created a resurgence of new effort estimation models; actually they are estimation adjustment models, because they require an initial estimate as one of the input variables. Creating a machine learned model requires a list of estimated/actual values, along with any other available information, to build a mapping function.
The sparseness of the data to learn from (at most a few hundred observations of half-a-dozen measured variables, and usually less) has not prevented a stream of puffed-up publications making all kinds of unfounded claims.
Until a few years ago the available public estimation data did not include any information about who made the estimate. Once estimation data contained the information needed to distinguish the different people making estimates, the uncertainty introduced by human variability was revealed (some consistently underestimating, others consistently overestimating, with 25% difference between two estimators being common, and a factor of two difference between some pairs of estimators).
How much accuracy is it realistic to expect with effort estimates?
At the moment we don’t have enough information on the software development process to be able to create a realistic model; without a realistic model of the development process, it’s a waste of time complaining about the availability of information to feed into a model.
I think a project simulation model is the only technique capable of creating a good enough model for use in industry; something like Abdel-Hamid’s tour de force PhD thesis (he also ignores my emails).
We are still in the early stages of finding out the components that need to be fitted together to build a model of software development, e.g., round numbers.
Even if all attempts to build such a model fail, there will be payback from a better understanding of the development process.