Perspectives on Data Science for Software Engineering

✍ Scribed by Tim Menzies, Laurie Williams, Thomas Zimmermann

• Publisher: Morgan Kaufmann
• Year: 2016
• Tongue: English
• Leaves: 340
• Edition: 1
• Category: Library

✦ Synopsis

Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics.

At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community’s leaders gathered to share hard-won lessons from the trenches.

Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid.

• Presents the wisdom of community experts, derived from a summit on software analytics
• Provides contributed chapters that share discrete ideas and technique from the trenches
• Covers top areas of concern, including mining security and social data, data visualization, and cloud-based data
• Presented in clear chapters designed to be applicable across many domains

✦ Table of Contents

Content:
Front Matter,Copyright,Contributors,AcknowledgmentsEntitled to full textIntroductionPerspectives on data science for software engineering, Pages 3-6
Software analytics and its application in practice, Pages 7-11
Seven principles of inductive software engineering: What we do is different, Pages 13-17
The need for data analysis patterns (in software engineering), Pages 19-23
From software data to software theory: The path less traveled, Pages 25-28
Why theory matters, Pages 29-33
Mining apps for anomalies, Pages 37-42
Embrace dynamic artifacts, Pages 43-46
Mobile app store analytics, Pages 47-49
The naturalness of software ☆, Pages 51-55
Advances in release readiness, Pages 57-62
How to tame your online services, Pages 63-65
Measuring individual productivity, Pages 67-71
Stack traces reveal attack surfaces, Pages 73-76
Visual analytics for software engineering data, Pages 77-80
Gameplay data plays nicer when divided into cohorts, Pages 81-84
A success story in applying data science in practice, Pages 85-90
There's never enough time to do all the testing you want, Pages 91-95
The perils of energy mining: measure a bunch, compare just once, Pages 97-102
Identifying fault-prone files in large industrial software systems, Pages 103-106
A tailored suit: The big opportunity in personalizing issue tracking, Pages 107-110
What counts is decisions, not numbers—Toward an analytics design sheet, Pages 111-114
A large ecosystem study to understand the effect of programming languages on code quality, Pages 115-118
Code reviews are not for finding defects—Even established tools need occasional evaluation, Pages 119-122
Interviews, Pages 125-131
Look for state transitions in temporal data, Pages 133-135
Card-sorting: From text to themes, Pages 137-141
Tools! Tools! We need tools!, Pages 143-148
Evidence-based software engineering, Pages 149-153
Which machine learning method do you need?, Pages 155-159
Structure your unstructured data first!: The case of summarizing unstructured data with tag clouds, Pages 161-168
Parse that data! Practical tips for preparing your raw data for analysis, Pages 169-173
Natural language processing is no free lunch, Pages 175-179
Aggregating empirical evidence for more trustworthy decisions, Pages 181-186
If it is software engineering, it is (probably) a Bayesian factor, Pages 187-191
Becoming Goldilocks: Privacy and data sharing in “just right” conditions, Pages 193-197
The wisdom of the crowds in predictive modeling for software engineering, Pages 199-204
Combining quantitative and qualitative methods (when mining software data), Pages 205-211
A process for surviving survey design and sailing through survey deployment, Pages 213-219
Log it all?, Pages 223-225
Why provenance matters, Pages 227-231
Open from the beginning, Pages 233-237
Reducing time to insight, Pages 239-243
Five steps for success: How to deploy data science in your organizations, Pages 245-248
How the release process impacts your software analytics, Pages 249-253
Security cannot be measured, Pages 255-259
Gotchas from mining bug reports, Pages 261-265
Make visualization part of your analysis process, Pages 267-269
Don't forget the developers! (and be careful with your assumptions), Pages 271-275
Limitations and context of research, Pages 277-281
Actionable metrics are better metrics, Pages 283-287
Replicated results are more trustworthy, Pages 289-293
Diversity in software engineering research, Pages 295-298
Once is not enough: Why we need replication, Pages 299-302
Mere numbers aren't enough: A plea for visualization, Pages 303-307
Don’t embarrass yourself: Beware of bias in your data, Pages 309-315
Operational data are missing, incorrect, and decontextualized, Pages 317-322
Data science revolution in process improvement and assessment?, Pages 323-325
Correlation is not causation (or, when not to scream “Eureka!”), Pages 327-330
Software analytics for small software companies: More questions than answers, Pages 331-335
Software analytics under the lamp post (or what star trek teaches us about the importance of asking the right questions), Pages 337-340
What can go wrong in software engineering experiments?, Pages 341-345
One size does not fit all, Pages 347-348
While models are good, simple explanations are better, Pages 349-352
The white-shirt effect: Learning from failed expectations, Pages 353-357
Simpler questions can lead to better insights, Pages 359-363
Continuously experiment to assess values early on, Pages 365-368
Lies, damned lies, and analytics: Why big data needs thick data, Pages 369-374
The world is your test suite, Pages 375-378

✦ Subjects

COMPUTERS;General;Software engineering