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.
1. Introduction - p. xxix 2. Perspectives on data science for software engineering - p. 3 3. Software analytics and its application in practice - p. 7 4. Seven principles of inductive software engineering: What we do is different - p. 13 5. The need for data analysis patterns (in software engineering) - p. 19 6. From software data to software theory: The path less traveled - p. 25 7. Why theory matters - p. 29 8. Success Stories/Applications - p. 33 9. Mining apps for anomalies - p. 37 10. Embrace dynamic artifacts - p. 43 11. Mobile app store analytics - p. 47 12. The naturalness of software - p. 51 13. Advances in release readiness - p. 57 14. How to tame your online services - p. 63 15. Measuring individual productivity - p. 67 16. Stack traces reveal attack surfaces - p. 73 17. Visual analytics for software engineering data - p. 77 18. Gameplay data plays nicer when divided into cohorts - p. 81 19. A success story in applying data science in practice - p. 85 20. There's never enough time to do all the testing you want - p. 91 21. The perils of energy mining: measure a bunch, compare just once - p. 97 22. Identifying fault-prone files in large industrial software systems - p. 103 23. A tailored suit: The big opportunity in personalizing issue tracking - p. 107 24. What counts is decisions, not numbers—Toward an analytics design sheet - p. 111 25. A large ecosystem study to understand the effect of programming languages on code quality - p. 115 26. Code reviews are not for finding defects—Even established tools need occasional evaluation - p. 119 27. Techniques - p. 122 28. Interviews - p. 125 29. Look for state transitions in temporal data - p. 133 30. Card-sorting: From text to themes - p. 137 31. Tools! Tools! We need tools! - p. 143 32. Evidence-based software engineering - p. 149 33. Which machine learning method do you need? - p. 155 34. Structure your unstructured data first!: The case of summarizing unstructured data with tag clouds - p. 161 35. Parse that data! Practical tips for preparing your raw data for analysis - p. 169 36. Natural language processing is no free lunch - p. 175 37. Aggregating empirical evidence for more trustworthy decisions - p. 181 38. If it is software engineering, it is (probably) a Bayesian factor - p. 187 39. Becoming Goldilocks: Privacy and data sharing in “just right” conditions - p. 193 40. The wisdom of the crowds in predictive modeling for software engineering - p. 199 41. Combining quantitative and qualitative methods (when mining software data) - p. 205 42. A process for surviving survey design and sailing through survey deployment - p. 213 43. Wisdom - p. 219 44. Log it all? - p. 223 45. Why provenance matters - p. 227 46. Open from the beginning - p. 233 47. Reducing time to insight - p. 239 48. Five steps for success: How to deploy data science in your organizations - p. 245 49. How the release process impacts your software analytics - p. 249 50. Security cannot be measured - p. 255 51. Gotchas from mining bug reports - p. 261 52. Make visualization part of your analysis process - p. 267 53. Don't forget the developers! (and be careful with your assumptions) - p. 271 54. Limitations and context of research - p. 277 55. Actionable metrics are better metrics - p. 283 56. Replicated results are more trustworthy - p. 289 57. Diversity in software engineering research - p. 295 58. Once is not enough: Why we need replication - p. 299 59. Mere numbers aren't enough: A plea for visualization - p. 303 60. Don’t embarrass yourself: Beware of bias in your data - p. 309 61. Operational data are missing, incorrect, and decontextualized - p. 317 62. Data science revolution in process improvement and assessment? - p. 323 63. Correlation is not causation (or, when not to scream “Eureka!”) - p. 327 64. Software analytics for small software companies: More questions than answers - p. 331 65. Software analytics under the lamp post (or what star trek teaches us about the importance of asking the right questions) - p. 337 66. What can go wrong in software engineering experiments? - p. 341 67. One size does not fit all - p. 347 68. While models are good, simple explanations are better - p. 349 69. The white-shirt effect: Learning from failed expectations - p. 353 70. Simpler questions can lead to better insights - p. 359 71. Continuously experiment to assess values early on - p. 365 72. Lies, damned lies, and analytics: Why big data needs thick data - p. 369 73. The world is your test suite - p. 375
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.
1. Introduction - p. xxix 2. Perspectives on data science for software engineering - p. 3 3. Software analytics and its application in practice - p. 7 4. Seven principles of inductive software engineering: What we do is different - p. 13 5. The need for data analysis patterns (in software engineering) - p. 19 6. From software data to software theory: The path less traveled - p. 25 7. Why theory matters - p. 29 8. Success Stories/Applications - p. 33 9. Mining apps for anomalies - p. 37 10. Embrace dynamic artifacts - p. 43 11. Mobile app store analytics - p. 47 12. The naturalness of software - p. 51 13. Advances in release readiness - p. 57 14. How to tame your online services - p. 63 15. Measuring individual productivity - p. 67 16. Stack traces reveal attack surfaces - p. 73 17. Visual analytics for software engineering data - p. 77 18. Gameplay data plays nicer when divided into cohorts - p. 81 19. A success story in applying data science in practice - p. 85 20. There's never enough time to do all the testing you want - p. 91 21. The perils of energy mining: measure a bunch, compare just once - p. 97 22. Identifying fault-prone files in large industrial software systems - p. 103 23. A tailored suit: The big opportunity in personalizing issue tracking - p. 107 24. What counts is decisions, not numbers—Toward an analytics design sheet - p. 111 25. A large ecosystem study to understand the effect of programming languages on code quality - p. 115 26. Code reviews are not for finding defects—Even established tools need occasional evaluation - p. 119 27. Techniques - p. 122 28. Interviews - p. 125 29. Look for state transitions in temporal data - p. 133 30. Card-sorting: From text to themes - p. 137 31. Tools! Tools! We need tools! - p. 143 32. Evidence-based software engineering - p. 149 33. Which machine learning method do you need? - p. 155 34. Structure your unstructured data first!: The case of summarizing unstructured data with tag clouds - p. 161 35. Parse that data! Practical tips for preparing your raw data for analysis - p. 169 36. Natural language processing is no free lunch - p. 175 37. Aggregating empirical evidence for more trustworthy decisions - p. 181 38. If it is software engineering, it is (probably) a Bayesian factor - p. 187 39. Becoming Goldilocks: Privacy and data sharing in “just right” conditions - p. 193 40. The wisdom of the crowds in predictive modeling for software engineering - p. 199 41. Combining quantitative and qualitative methods (when mining software data) - p. 205 42. A process for surviving survey design and sailing through survey deployment - p. 213 43. Wisdom - p. 219 44. Log it all? - p. 223 45. Why provenance matters - p. 227 46. Open from the beginning - p. 233 47. Reducing time to insight - p. 239 48. Five steps for success: How to deploy data science in your organizations - p. 245 49. How the release process impacts your software analytics - p. 249 50. Security cannot be measured - p. 255 51. Gotchas from mining bug reports - p. 261 52. Make visualization part of your analysis process - p. 267 53. Don't forget the developers! (and be careful with your assumptions) - p. 271 54. Limitations and context of research - p. 277 55. Actionable metrics are better metrics - p. 283 56. Replicated results are more trustworthy - p. 289 57. Diversity in software engineering research - p. 295 58. Once is not enough: Why we need replication - p. 299 59. Mere numbers aren't enough: A plea for visualization - p. 303 60. Don’t embarrass yourself: Beware of bias in your data - p. 309 61. Operational data are missing, incorrect, and decontextualized - p. 317 62. Data science revolution in process improvement and assessment? - p. 323 63. Correlation is not causation (or, when not to scream “Eureka!”) - p. 327 64. Software analytics for small software companies: More questions than answers - p. 331 65. Software analytics under the lamp post (or what star trek teaches us about the importance of asking the right questions) - p. 337 66. What can go wrong in software engineering experiments? - p. 341 67. One size does not fit all - p. 347 68. While models are good, simple explanations are better - p. 349 69. The white-shirt effect: Learning from failed expectations - p. 353 70. Simpler questions can lead to better insights - p. 359 71. Continuously experiment to assess values early on - p. 365 72. Lies, damned lies, and analytics: Why big data needs thick data - p. 369 73. The world is your test suite - p. 375
