UID:
edoccha_9958145839502883
Format:
1 online resource (410 pages) :
,
illustrations (some color), photographs, graphs, tables
Edition:
1st edition
ISBN:
0-12-804261-3
,
0-12-804206-0
Content:
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
Note:
Front Cover -- Perspectives on Data Science for Software Engineering -- Copyright -- Contents -- Contributors -- Acknowledgments -- Introduction -- Perspectives on data science for software engineering -- Why This Book? -- About This Book -- The Future -- References -- Software analytics and its application in practice -- Six Perspectives of Software Analytics -- Experiences in Putting Software Analytics into Practice -- References -- Seven principles of inductive software engineering: What we do is different -- 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 -- References -- The need for data analysis patterns (in software engineering) -- The Remedy Metaphor -- Software Engineering Data -- Needs of Data Analysis Patterns -- Building Remedies for Data Analysis in Software Engineering Research -- References -- From software data to software theory: The path less traveled -- Pathways of Software Repository Research -- From Observation, to Theory, to Practice -- References -- Why theory matters -- Introduction -- How to Use Theory -- How to build theory -- Constructs -- Propositions -- Explanation -- Scope -- In Summary: Find a Theory or Build One Yourself -- Further Reading -- Success stories/applications -- Mining apps for anomalies -- The Million-Dollar Question -- App Mining -- Detecting Abnormal Behavior -- A Treasure Trove of Data -- ... But Also Obstacles -- Executive Summary -- Further Reading -- Embrace dynamic artifacts -- Can We Minimize the USB Driver Test Suite? -- Yes, Lets Observe Interactions -- Why Did Our Solution Work? -- Still Not Convinced? Heres More.
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Dynamic Artifacts Are Here to Stay -- Acknowledgments -- References -- Mobile app store analytics -- Introduction -- Understanding End Users -- Conclusion -- References -- The naturalness of software* -- Introduction -- Transforming Software Practice -- Porting and Translation -- The ``Natural Linguistics´´ of Code -- Analysis and Tools -- Assistive Technologies -- Conclusion -- References -- Advances in release readiness -- Predictive Test Metrics -- Universal Release Criteria Model -- Best Estimation Technique -- Resource/Schedule/Content Model -- Using Models in Release Management -- Research to Implementation: A Difficult (but Rewarding) Journey -- How to tame your online services -- Background -- Service Analysis Studio -- Success Story -- References -- Measuring individual productivity -- No Single and Simple Best Metric for Success/Productivity -- Measure the Process, Not Just the Outcome -- Allow for Measures to Evolve -- Goodharts Law and the Effect of Measuring -- How to Measure Individual Productivity? -- References -- Stack traces reveal attack surfaces -- Another Use of Stack Traces? -- Attack Surface Approximation -- References -- Visual analytics for software engineering data -- References -- Gameplay data plays nicer when divided into cohorts -- Cohort Analysis as a Tool for Gameplay Data -- Play to Lose -- Forming Cohorts -- Case Studies of Gameplay Data -- Challenges of using cohorts -- Summary -- References -- A success story in applying data science in practice -- Overview -- Analytics Process -- Data Collection -- Exploratory Data Analysis -- Model Selection -- Performance Measures and Benefit Analysis -- Communication Process-Best Practices -- Problem Selection -- Managerial Support -- Project Management -- Trusted Relationship -- Summary -- References -- There's never enough time to do all the testing you want.
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The Impact of Short Release Cycles (There's Not Enough Time) -- Testing Is More Than Functional Correctness (All the Testing You Want) -- Learn From Your Test Execution History -- Test Effectiveness -- Test Reliability/Not Every Test Failure Points to a Defect -- The Art of Testing Less -- Without Sacrificing Code Quality -- Tests Evolve Over Time -- In Summary -- References -- The perils of energy mining: measure a bunch, compare just once -- A Tale of TWO HTTPs -- Let's energise your software energy experiments -- Environment -- N-Versions -- Energy or Power -- Repeat! -- Granularity -- Idle Measurement -- Statistical Analysis -- Exceptions -- Summary -- References -- Identifying fault-prone files in large industrial software systems -- Acknowledgment -- References -- A tailored suit: The big opportunity in personalizing issue tracking -- Many Choices, Nothing Great -- The Need for Personalization -- Developer Dashboards or ``A Tailored Suit´´ -- Room for Improvement -- References -- What counts is decisions, not numbers-Toward an analytics design sheet -- Decisions Everywhere -- The Decision-Making Process -- The Analytics Design Sheet -- Example: App Store Release Analysis -- References -- A large ecosystem study to understand the effect of programming languages on code quality -- Comparing Languages -- Study Design and Analysis -- Results -- Summary -- References -- Code reviews are not for finding defects-Even established tools need occasional evaluation -- Results -- Effects -- Conclusions -- References -- Techniques -- Interviews -- Why Interview? -- The Interview Guide -- Selecting Interviewees -- Recruitment -- Collecting Background Data -- Conducting the Interview -- Post-Interview Discussion and Notes -- Transcription -- Analysis -- Reporting -- Now Go Interview! -- References -- Look for state transitions in temporal data.
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Bikeshedding in Software Engineering -- Summarizing Temporal Data -- Recommendations -- Reference -- Card-sorting: From text to themes -- Preparation Phase -- Execution Phase -- Analysis Phase -- References -- Tools! Tools! We need tools! -- Tools in Science -- The Tools We Need -- Recommendations for Tool Building -- References -- Evidence-based software engineering -- Introduction -- The Aim and Methodology of EBSE -- Contextualizing Evidence -- Strength of Evidence -- Evidence and Theory -- References -- Which machine learning method do you need? -- 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 ... -- References -- Structure your unstructured data first! -- Unstructured Data in Software Engineering -- Summarizing Unstructured Software Data -- As Simple as Possible... But not Simpler! -- You Need Structure! -- Conclusion -- References -- Parse that data! Practical tips for preparing your raw data for analysis -- 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 -- Natural Language Data in Software Projects -- Natural Language Processing -- How to Apply NLP to Software Projects -- Do Stemming First.
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Check the Level of Abstraction -- Dont Expect Magic -- Dont Discard Manual Analysis of Textual Data -- Summary -- References -- Aggregating empirical evidence for more trustworthy decisions -- What's Evidence? -- What Does Data From Empirical Studies Look Like? -- The Evidence-Based Paradigm and Systematic Reviews -- How Far Can We Use the Outcomes From Systematic Review to Make Decisions? -- References -- If it is software engineering, it is (probably) a Bayesian factor -- Causing the Future With Bayesian Networks -- The Need for a Hybrid Approach in Software Analytics -- Use the Methodology, Not the Model -- References -- Becoming Goldilocks: Privacy and data sharing in ``just right´´ conditions -- The ``Data Drought´´ -- Change is Good -- Dont Share Everything -- Share Your Leaders -- Summary -- Acknowledgments -- References -- The wisdom of the crowds in predictive modeling for software engineering -- The Wisdom of the Crowds -- So... How is That Related to Predictive Modeling for Software Engineering? -- Examples of Ensembles and Factors Affecting Their Accuracy -- Crowds for transferring knowledge and dealing with changes -- Crowds for Multiple Goals -- A Crowd of Insights -- Ensembles as Versatile Tools -- References -- Combining quantitative and qualitative methods (when mining software data) -- Prologue: We Have Solid Empirical Evidence! -- Correlation is Not Causation and, Even If We Can Claim Causation... -- Collect your data: People and artifacts -- Source 1: Dig Into Software Artifacts and Data -- ...but be careful about noise and incompleteness! -- Source 2: Getting Feedback From Developers -- ...and dont be afraid if you collect very little data! -- How Much to Analyze, and How? -- Build a theory upon your data -- Conclusion: The Truth is Out There! -- Suggested Readings -- References.
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A process for surviving survey design and sailing through survey deployment.
Language:
English
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