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Perspectives on Data Science for Software Engineering
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Identifying Fault-prone Files in Large Industrial Software Systems #110

Closed GRuhe closed 8 years ago

GRuhe commented 8 years ago

URL to the chapter

the markdown file. e.g. https://github.com/ds4se/chapters/blob/master/weyuker-ostrand/prediction.md

Message?

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

Accurate defect prediction IS possible

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, no suggestion for changes. Would be nice to have one or two sentences if and how successful the results 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.

Fault distribution among files follows the Pareto rule

Best Points

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

Starting with a (nice and to-the point) joke

tzimmermsr commented 8 years ago

Moved the review to #88.