# The Three Pillars of Machine Programming

arXiv: https://arxiv.org/abs/1803.07244 · HTML: https://mlsanity.com/paper/1803.07244/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2018.

## Built on by (most cited first, 12 of 12)

- [CrossBeam: Learning to Search in Bottom-Up Program Synthesis](https://mlsanity.com/paper/2203.10452/) (2022)
- [Toward Code Generation: A Survey and Lessons from Semantic Parsing](https://mlsanity.com/paper/2105.03317/) (2021)
- [PerfRL: A Small Language Model Framework for Efficient Code Optimization](https://mlsanity.com/paper/2312.05657/) (2023)
- [Compositional Generalization and Decomposition in Neural Program Synthesis](https://mlsanity.com/paper/2204.03758/) (2022)
- [LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas](https://mlsanity.com/paper/2306.02049/) (2023)
- [MPI-rical: Data-Driven MPI Distributed Parallelism Assistance with Transformers](https://mlsanity.com/paper/2305.09438/) (2023)
- [Neural Machine Translation for Code Generation](https://mlsanity.com/paper/2305.13504/) (2023)
- [Synthesizing Programs with Continuous Optimization](https://mlsanity.com/paper/2211.00828/) (2022)
- [Are Machine Programming Systems using Right Source-Code Measures to Select Code Repositories?](https://mlsanity.com/paper/2209.11946/) (2022)
- [Report of the Workshop on Program Synthesis for Scientific Computing](https://mlsanity.com/paper/2102.01687/) (2021)
- [ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation](https://mlsanity.com/paper/2601.04327/) (2026)
- [Piece of CAKE: Adaptive Execution Engines via Microsecond-Scale Learning](https://mlsanity.com/paper/2602.04181/) (2026)

## Abstract (arXiv)

In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research. Those pillars are: (i) intention, (ii) invention, and(iii) adaptation. Intention emphasizes advancements in the human-to-computer and computer-to-machine-learning interfaces. Invention emphasizes the creation or refinement of algorithms or core hardware and software building blocks through machine learning (ML). Adaptation emphasizes advances in the use of ML-based constructs to autonomously evolve software.