Buch, Englisch, 408 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 910 g
Buch, Englisch, 408 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 910 g
ISBN: 978-0-12-804206-9
Verlag: William Andrew Publishing
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.
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Weitere Infos & Material
Introduction
Perspectives on data science for software engineering
Software analytics and its application in practice
Seven principles of inductive software engineering: What we do is different
The need for data analysis patterns (in software engineering)
From software data to software theory: The path less traveled
Why theory matters
Success Stories/Applications
Mining apps for anomalies
Embrace dynamic artifacts
Mobile app store analytics
The naturalness of software
Advances in release readiness
How to tame your online services
Measuring individual productivity
Stack traces reveal attack surfaces
Visual analytics for software engineering data
Gameplay data plays nicer when divided into cohorts
A success story in applying data science in practice
There's never enough time to do all the testing you want
The perils of energy mining: measure a bunch, compare just once
Identifying fault-prone files in large industrial software systems
A tailored suit: The big opportunity in personalizing issue tracking
What counts is decisions, not numbers-Toward an analytics design sheet
A large ecosystem study to understand the effect of programming languages on code quality
Code reviews are not for finding defects-Even established tools need occasional evaluation
Techniques
Interviews
Look for state transitions in temporal data
Card-sorting: From text to themes
Tools! Tools! We need tools!
Evidence-based software engineering
Which machine learning method do you need?
Structure your unstructured data first!: The case of summarizing unstructured data with tag clouds
Parse that data! Practical tips for preparing your raw data for analysis
Natural language processing is no free lunch
Aggregating empirical evidence for more trustworthy decisions
If it is software engineering, i