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Improvements in the performance of computing systems, driven by Moore's Law,have transformed society. As such hardware-driven gains slow down, it becomeseven more important for software developers to focus on performance andefficiency during development. While several studies have demonstrated thepotential from such improved code efficiency (e.g., 2x better generationalimprovements compared to hardware), unlocking these gains in practice has beenchallenging. Reasoning about algorithmic complexity and the interaction ofcoding patterns on hardware can be challenging for the average programmer,especially when combined with pragmatic constraints around development velocityand multi-person development.  This paper seeks to address this problem. We analyze a large competitiveprogramming dataset from the Google Code Jam competition and find thatefficient code is indeed rare, with a 2x runtime difference between the medianand the 90th percentile of solutions. We propose using machine learning toautomatically provide prescriptive feedback in the form of hints, to guideprogrammers towards writing high-performance code. To automatically learn thesehints from the dataset, we propose a novel discrete variational auto-encoder,where each discrete latent variable represents a different learned category ofcode-edit that increases performance. We show that this method represents themulti-modal space of code efficiency edits better than a sequence-to-sequencebaseline and generates a distribution of more efficient solutions.

Learning to Improve Code Efficiency