Perspectives on Data Science for Software Engineering (Paperback)

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, it is (probably) a Bayesian factor

Becoming Goldilocks: Privacy and data sharing in “just right? conditions

The wisdom of the crowds in predictive modeling for software engineering

Combining quantitative and qualitative methods (when mining software data)

A process for surviving survey design and sailing through survey deployment

Wisdom

Log it all?

Why provenance matters

Open from the beginning

Reducing time to insight

Five steps for success: How to deploy data science in your organizations

How the release process impacts your software analytics

Security cannot be measured

Gotchas from mining bug reports

Make visualization part of your analysis process

Don't forget the developers! (and be careful with your assumptions)

Limitations and context of research

Actionable metrics are better metrics

Replicated results are more trustworthy

Diversity in software engineering research

Once is not enough: Why we need replication

Mere numbers aren't enough: A plea for visualization

Don’t embarrass yourself: Beware of bias in your data

Operational data are missing, incorrect, and decontextualized

Data science revolution in process improvement and assessment?

Correlation is not causation (or, when not to scream “Eureka!?)

Software analytics for small software companies: More questions than answers

Software analytics under the lamp post (or what star trek teaches us about the importance of asking the right questions)

What can go wrong in software engineering experiments?

One size does not fit all

While models are good, simple explanations are better

The white-shirt effect: Learning from failed expectations

Simpler questions can lead to better insights

Continuously experiment to assess values early on

Lies, damned lies, and analytics: Why big data needs thick data

The world is your test suite