Challenges of Conducting Software Engineering Experiments: How to Stop Things Going Wrong

URL to the chapter

the markdown file. e.g. https://github.com/ds4se/chapters/blob/master/siravegas/Challenges SE Experiments.md

Message?

What is the chapter's clear and approachable take away message?

”One size does not fit all” applies to SE experiments as well

Accessible?

Is the chapters written for a generalist audience (no excessive use of technical terminology) with a minimum of diagrams and references? How can it be made more accessible to generalist?

Yes, it is written in a non-technical language, easy to understand also for non-experts

Size?

Is the chapter the right length? Should anything missing be added? Can anything superfluous be removed (e.g. by deleting some section that does not work so well or by using less jargon, less formulae, lees diagrams, less references).? What are the aspects of the chapter that authors SHOULD change?

_Length is right, language easy to understand.

For the definition of experiments, would replace “observe” (software development by “analyze”.
Saying “measurement procedure in SE very often is non-standard” … does this mean the metrics are context specific, but the procedure IS (kind of) standard (GQM)?
Would be nice to outline potential implications of NOT following the stated guidelines. were used at AT&T._
Gotta Mantra?

We encouraged (but did not require) the chapter title to be a mantra or something cute/catchy, i.e., some slogan reflecting best practice for data science for SE? If you have suggestion for a better title, please put them here.

What can go wrong in SE experiments?

Best Points

What are the best points of the chapter that the authors should NOT change?

Making clear that not all experiments automatically are meaningful and pay off

ds4se / chapters