What is the chapter's clear and approachable take away message?
To summarize dynamic data, consider state transitions
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?
I am not sure whether the problem of bike shedding in empirical SE is sufficiently widespread to warrant this chapter; so more evidence and/or motivation would be welcome. Likewise, I am not sure whether the solution of summarizing data into state transitions is sufficiently general to be framed as the one solution. I would prefer having a concrete story (which is currently hidden in the chapter) on how state transitions saved a particular concrete project.
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?
See above. I can live with the chapter as is, but making it more of an anecdote would be both more compelling and justified.
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.
Look for State Transitions in Temporal Data
Best Points
What are the best points of the chapter that the authors should NOT change?
Introducing state transitions for temporal relationships. That was an interesting and potentially valuable insight.
tzimmermsr
commented
8 years ago
Title of chapter
Summarizing Dynamic Data
URL to the chapter
https://github.com/ds4se/chapters/blob/master/rtholmes/summarizing-dynamic-data.md
Message?
What is the chapter's clear and approachable take away message?
To summarize dynamic data, consider state transitions
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?
I am not sure whether the problem of bike shedding in empirical SE is sufficiently widespread to warrant this chapter; so more evidence and/or motivation would be welcome. Likewise, I am not sure whether the solution of summarizing data into state transitions is sufficiently general to be framed as the one solution. I would prefer having a concrete story (which is currently hidden in the chapter) on how state transitions saved a particular concrete project.
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?
See above. I can live with the chapter as is, but making it more of an anecdote would be both more compelling and justified.
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.
Look for State Transitions in Temporal Data
Best Points
What are the best points of the chapter that the authors should NOT change?
Introducing state transitions for temporal relationships. That was an interesting and potentially valuable insight.