The art and science of analyzing software data
著者
書誌事項
The art and science of analyzing software data
Morgan Kaufmann, c2015
- : pbk
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注記
Includes bibliographical references
Includes index
内容説明・目次
内容説明
The Art and Science of Analyzing Software Data provides valuable information on analysis techniques often used to derive insight from software data. This book shares best practices in the field generated by leading data scientists, collected from their experience training software engineering students and practitioners to master data science.
The book covers topics such as the analysis of security data, code reviews, app stores, log files, and user telemetry, among others. It covers a wide variety of techniques such as co-change analysis, text analysis, topic analysis, and concept analysis, as well as advanced topics such as release planning and generation of source code comments. It includes stories from the trenches from expert data scientists illustrating how to apply data analysis in industry and open source, present results to stakeholders, and drive decisions.
目次
Past, Present, and Future of Analyzing Software Data
Part 1 TUTORIAL-TECHNIQUES
Mining Patterns and Violations Using Concept Analysis
Analyzing Text in Software Projects
Synthesizing Knowledge from Software Development Artifacts
A Practical Guide to Analyzing IDE Usage Data
Latent Dirichlet Allocation: Extracting Topics from Software Engineering Data
Tools and Techniques for Analyzing Product and Process Data
PART 2 DATA/PROBLEM FOCUSSED
Analyzing Security Data
A Mixed Methods Approach to Mining Code Review Data: Examples and a Study of Multicommit Reviews and Pull Requests
Mining Android Apps for Anomalies
Change Coupling Between Software Artifacts: Learning from Past Changes
PART 3 STORIES FROM THE TRENCHES
Applying Software Data Analysis in Industry Contexts: When Research Meets Reality
Using Data to Make Decisions in Software Engineering:
Providing a Method to our Madness
Community Data for OSS Adoption Risk Management
Assessing the State of Software in a Large Enterprise: A 12-Year Retrospective
Lessons Learned from Software Analytics in Practice
PART 4 ADVANCED TOPICS
Code Comment Analysis for Improving Software Quality
Mining Software Logs for Goal-Driven Root Cause Analysis
Analytical Product Release Planning
PART 5 DATA ANALYSIS AT SCALE (BIG DATA)
Boa: An Enabling Language and Infrastructure for Ultra-Large-Scale MSR Studies
Scalable Parallelization of Specification Mining Using Distributed Computing
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