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.
Presents best practices, hints, and tips to analyze data and apply tools in data science projects
Presents research methods and case studies that have emerged over the past few years to further understanding of software data
Shares stories from the trenches of successful data science initiatives in industry
Comments about oreilly The Art and Science of Analyzing Software Data:
While I was initially excited to read this book (the table of contents looks like it covers a number of topics I'd like to read about), it was not really what I was looking for.
I did not like the style of the book where each chapter was written by a different set of authors. This led to the book feeling like a collection of research articles which had been stapled together. I would have preferred a more unified book which described analysis techniques and gave examples of how they had been applied in real-world software development firms.
Some of the chapters were interesting but I ended up skimming quite a lot of the book, unfortunately.
Bottom Line No, I would not recommend this to a friend