Every now and again, I stumble upon a really interesting dataset. Previously, when this happened I wrote an extensive blog post; but the SiP dataset was just too big and too detailed, it called out for a more expansive treatment.
How big is the SiP effort estimation dataset? It contains 10,100 unique task estimates, from ten years of commercial development using Agile. That’s around two orders of magnitude larger than other, current, public effort datasets.
How detailed is the SiP effort estimation dataset? It contains the (anonymized) identity of the 22 developers making the estimates, for one of 20 project codes, dates, plus various associated items. Other effort estimation datasets usually just contain values for estimated effort and actual effort.
Data analysis is a conversation between the person doing the analysis and the person(s) with knowledge of the application domain from which the data came. The aim is to discover information that is of practical use to the people working in the application domain.
I suggested to Stephen Cullum (the person I got talking to at a workshop, a director of Software in Partnership Ltd, and supplier of data) that we write a paper having the form of a conversation about the data; he bravely agreed.
The result is now available: A conversation around the analysis of the SiP effort estimation dataset.
I’m looking forward to seeing what other people do with the SiP dataset. There are surely other patterns waiting to be discovered, and what about building a simulation model based on the charcteristics of this data?
Turning software engineering into an evidence-based disciple requires a lot more data; I plan to go looking for more large datasets.
Software engineering researchers are a remarkable unambitious bunch of people. The SiP dataset should be viewed as the first of 100 such datasets. With 100 datasets we can start to draw general, believable conclusions about the processes involved in software effort estimation.
Readers, today is the day you start asking managers to make any software engineering data they have publicly available. Yes, it can be anonymized (I am willing to do that for people who are looking to release data). Yes, ‘old’ data is useful (data from the 1980s could have an interesting story to tell; SiP runs from 2004-2014). Yes, I will analyze any interesting data that is made public for free.
Ask, and you shall receive.