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54. An End-to-End Framework for Productive Use of Machine Learning in Software Analytics and Business Intelligence Solutions

55. From a Data Science Driven Process to a Continuous Delivery Process for Machine Learning Systems

56. Data Labeling: An Empirical Investigation into Industrial Challenges and Mitigation Strategies

57. Data Pipeline Management in Practice: Challenges and Opportunities

60. Towards AIOps enabled services in continuously evolving software‐intensive embedded systems.

61. Customer Churn Prediction in B2B Contexts

62. Leveraging Business Transformation with Machine Learning Experiments

63. Dynamic Data Management for Machine Learning in Embedded Systems: A Case Study

64. API Management Challenges in Ecosystems

66. A Taxonomy of Software Engineering Challenges for Machine Learning Systems: An Empirical Investigation

68. Data Driven Development: Challenges in Online, Embedded and On-Premise Software

69. Continuous Experimentation for Software Organizations with Low Control of Roadmap and a Large Distance to Users: An Exploratory Case Study

70. Excellence in Exploratory Testing: Success Factors in Large-Scale Industry Projects

71. ACE: Easy Deployment of Field Optimization Experiments

81. Contributing Authors

83. Towards a Digital Business Operating System

84. Optimization Experiments in the Continuous Space : The Limited Growth Optimistic Optimization Algorithm

85. An Activity and Metric Model for Online Controlled Experiments

87. Opportunities and barriers for prehospital emergency medical services research in the Netherlands; results of a mixed-methods consensus study.

98. Exploratory Testing of Large-Scale Systems – Testing in the Continuous Integration and Delivery Pipeline

99. Differentiating Feature Realization in Software Product Development

100. More for Less: Automated Experimentation in Software-Intensive Systems

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