Machine programming is an emerging research area that improves the software development life cycle from design through deployment. We present a tutorial on machine programming research highlighting aspects relevant to the data systems community. We divide this tutorial into three parts: We begin with an introduction to machine programming introducing its three pillars: intention, invention, and adaptation. Then, we provide an overview of the data ecosystem central to all machine programming systems, highlighting challenges and novel opportunities relevant to the data systems community. Finally, we describe recent advances in machine programming research and how these directions use various data sets to improve the ease of creating and maintaining performant software systems.

Deep learning enables numerous applications across diverse areas. Data systems researchers are also increasingly experimenting with deep learning to enhance data systems performance. We present a tutorial on deep learning, highlighting the data systems nature of neural networks as well as research opportunities for advancements through data management techniques. We focus on three critical aspects: (1) classic design tradeoffs in neural networks which we can enrich through a systems and data management perspective, e.g., thinking critically about storage, data movement, and computation; (2) classic design problems in data systems which we can reconsider with neural networks as a viable design option, e.g., to replace or help system components that make complex decisions such as database optimizers; and (3) essential considerations for responsible application of neural networks in critical human-facing problems in society and how these also link to data management and performance considerations. While these are seemingly a diverse set of rich topics, they are strongly interconnected through data management, and their combination offers rich opportunities for future research.

During exploratory statistical analysis, data scientists repeatedly compute statistics on data sets to infer knowledge. Moreover, statistics form the building blocks of core machine learning classification and filtering algorithms. Modern data systems, software libraries, and domain-specific tools provide support to compute statistics but lack a cohesive framework for storing, organizing, and reusing them. This creates a significant problem for exploratory statistical analysis as data grows: Despite existing overlap in exploratory workloads (which are repetitive in nature), statistics are always computed from scratch. This leads to repeated data movement and recomputation, hindering interactive data exploration.

Data center operators face extreme challenges in simultaneously providing low latency for short flows, high throughput for long flows, and high burst tolerance. We propose a buffer management strategy that addresses these challenges by isolating short and long flows into separate buffers, sizing these buffers based on flow requirements, and scheduling packets to meet different flow-level objectives. Our design provides new opportunities for performance improvements that complement transport layer optimisations.