Title page
by Thomas Zimmermann, Laurie Williams, Tim Menzies
Perspectives on Data Science for Software Engineering
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Acknowledgments
- 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
- Abstract
- Different and Important
- Principle #1: Humans Before Algorithms
- Principle #2: Plan for Scale
- Principle #3: Get Early Feedback
- Principle #4: Be Open Minded
- Principle #5: Be smart with your learning
- Principle #6: Live With the Data You Have
- Principle #7: Develop a Broad Skill Set That Uses a Big Toolkit
- 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?
- Abstract
- Learning Styles
- Do additional Data Arrive Over Time?
- Are Changes Likely to Happen Over Time?
- If You Have a Prediction Problem, What Do You Really Need to Predict?
- Do You Have a Prediction Problem Where Unlabeled Data are Abundant and Labeled Data are Expensive?
- Are Your Data Imbalanced?
- Do You Need to Use Data From Different Sources?
- Do You Have Big Data?
- Do You Have Little Data?
- In Summary…
- 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
- Abstract
- Use Assertions Everywhere
- Print Information About Broken Records
- Use Sets or Counters to Store Occurrences of Categorical Variables
- Restart Parsing in the Middle of the Data Set
- Test on a Small Subset of Your Data
- Redirect Stdout and Stderr to Log Files
- Store Raw Data Alongside Cleaned Data
- Finally, Write a Verifier Program to Check the Integrity of Your Cleaned Data
- 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?
- Abstract
- Operationalize Constructs
- Evaluate Different Design Alternatives
- Match Data Analysis and Experimental Design
- Do Not Rely on Statistical Significance Alone
- Do a Power Analysis
- Find Explanations for Results
- Follow Guidelines for Reporting Experiments
- Improving the reliability of experimental results
- 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
- Abstract
- How Great It Is, to Have Data Like You
- Looking for Answers in All the Wrong Places
- Beware the Reality Distortion Field
- Build It and They Will Come, but Should We?
- To Classify Is Human, but Analytics Relies on Algorithms
- Lean in: How Ethnography Can Improve Software Analytics and Vice Versa
- Finding the Ethnographer Within
- The world is your test suite
Perspectives on Data Science for Software Engineering
First Edition
Tim Menzies
Laurie Williams
Thomas Zimmermann
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