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Perspectives on Data Science for Software Engineering
https://github.com/ds4se/chapters

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Perspectives on Data Science for Software Engineering

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# Perspectives on Data Science for Software Engineering		



![logo](https://avatars2.githubusercontent.com/u/11729498?v=3&s=200)



This repo holds chapters, under development for a forthcoming Morgan Kaufmann book.		

		

_Keywords:_ software engineering; data science; analytics; data mining; visualization; decision making		

_Editors:_



+ Tim Menzies , NCState, USA

+ Laurie Williams , NCState, USA

+ Thomas Zimmmermann , Microsoft, USA

		

## Origins		

		

At a  recent Dagstuhl seminar on Software Analytics (June’2014, attended by 40 of the top researchers in the field), A repeated question at that meeting was how to transfer best (or safe) practices from seasoned SE data scientist to newcomers. 		

		

To address this problem, this book was planned to present the hard won lessons learned of  seasoned data miners.   Dagstuhl meeting, participants conducted brainstorming sessions to collect _mantras for data mining_.   Subsequently, the convenors of that meeting (Menzies, Williams, Zimmermann) refined that list into the list of chapters explored in this book.		

		

## Why This Book?		

		

There are many texts that cover the basics of data mining or advanced data mining scripting. However, there are no practitioner level texts that reflect the insights of seasoned data scientists.		

		

This _book of wisdom_ aims to address that gap.  Lessons learned (about SE data science) will be presented in small  standalone chapters that are easy to read and understand.  Newcomers to SE data science can learn tips and tricks of the trade. More experienced SE data scientists will benefit from “war stories” showing what traps to avoid.		

		

## Target Audience		

		

This book is targeted at industrial data science workers. Each chapter will be a short   (2 to 4 pages) and focused discussion  on one _mantra_ of SE data science. Chapters will be written for a generalist audience (no excessive use of technical terminology) with a minimum of diagrams and references. 		

		

## Dates 		

		

When          | What		

------------- | -------------------------------		

 Apr 30, 2015 |         Invitations issued (and accepted) to write each chapter to 40+ authors		

June 30, 2015 |     Chapters submitted. Commence peer review of chapters (by other chapter authors)	

July 31, 2015 |     First round reviews completed and sent back to authors		

Sept 15, 2015 |        Revised chapters received, optional second peer review		

Sept 30, 2015 |   Second round reviews completed and sent back to author		

Oct 21, 2015  | Final chapters sent to Morgan Kaufman		



## Instructions to Authors



The chapter title should be a _mantra_; i.e. some slogan reflecting best practice for data science for SE.



Chapters should use  diagrams in gray scale (or no diagrams at all).



Chapters should minimize the use of references (less than half a dozen).



Chapters to be written in Markdown and committed to this repo:



+ To begin that task, send your Githib repo id to any of the above editors. They will add you to the "colloberators" of this repo.

+ If you do not want to hassle with Github, then write in Markdown using on the [free Markdown editors](http://mashable.com/2013/06/24/markdown-tools/) and send to the editors.

     

Chapters should be short. For example, our [sample chapter](sample.md) has:



+   250 lines

+   1500 words

+   9000 characters



Chapters should be _approachable_ and have a _take away message_. For example, our  [sample chapter](sample.md) has certain features which you can (optionally) copy:



+ Some light hearted story to start the chapter;

+ Some list of specific recommendations to end the chapter (see the heading _In Summary_ at the end of the file).