Two years ago, my book Evidence-based Software Engineering: based on the publicly available data was released. The first two weeks saw 0.25 million downloads, and 0.5 million after six months. The paperback version on Amazon has sold perhaps 20 copies.
How have the book contents fared, and how well has my claim to have discussed all the publicly available software engineering data stood up?
The contents have survived almost completely unscathed. This is primarily because reader feedback has been almost non-existent, and I have hardly spent any time rereading it.
In the last two years I have discovered maybe a dozen software engineering datasets that would have been included, had I known about them, and maybe another dozen non-software related datasets that could have been included in the Human behavior/Cognitive capitalism/Ecosystems/Reliability chapters. About half of these have been the subject of blog posts (links below), with the others waiting to be covered.
Each dataset provides a sliver of insight into the much larger picture that is software engineering; joining the appropriate dots, by analyzing multiple datasets, can provide a larger sliver of insight into the bigger picture. I have not spent much time attempting to join dots, but have joined a few tiny ones, and a few that are not so small, e.g., Estimating using a granular sequence of values and Task backlog waiting times are power laws.
I spent the first year, after the book came out, working through the backlog of tasks that had built up during the 10-years of writing. The second year was mostly dedicated to trying to find software project data (including joining Twitter), and reading papers at a much reduced rate.
The plot below shows the number of monthly downloads of the A4 and mobile friendly pdfs, along with the average kbytes per download (code+data):
The monthly averages for 2022 are around 6K A4 and 700 mobile friendly pdfs.
I have been averaging one in-person meetup per week in London. Nearly everybody I tell about the book has not previously heard of it.
The following is a list of blog posts either analyzing existing data or discussing/analyzing new data.
analysis: Software effort estimation is mostly fake research
analysis: Moore’s law was a socially constructed project
data (reasoning): The impact of believability on reasoning performance
data: The Approximate Number System and software estimating
data (social conformance): How large an impact does social conformity have on estimates?
data (anchoring): Estimating quantities from several hundred to several thousand
data: Cognitive effort, whatever it might be
analysis: Delphi and group estimation
analysis: The CESAW dataset: a brief introduction
analysis: Parkinson’s law, striving to meet a deadline, or happenstance?
analysis: Evaluating estimation performance
analysis: Complex software makes economic sense
analysis: Cost-effectiveness decision for fixing a known coding mistake
analysis: Optimal sizing of a product backlog
analysis: Evolution of the DORA metrics
analysis: Two failed software development projects in the High Court
data: Pomodoros worked during a day: an analysis of Alex’s data
data: Multi-state survival modeling of a Jira issues snapshot
data: Over/under estimation factor for ‘most estimates’
data: Estimation accuracy in the (building|road) construction industry
data: Rounding and heaping in non-software estimates
data: Patterns in the LSST:DM Sprint/Story-point/Story ‘done’ issues
data: Shopper estimates of the total value of items in their basket
analysis: Most percentages are more than half