Number of papers: 2
- Authors: Peng, Dinglan and Zheng, Shuxin and Li, Yatao and Ke, Guolin and He, Di and Liu, Tie-Yan
- Abstract: Semantic understanding of programs is a fundamental problem for programming language processing (PLP). Recent works that learn representations of code based on pre-training techniques in NLP have pushed the frontiers in this direction. However, the semantics of PL and NL have essential differences. These being ignored, we believe it is difficult to build a model to better understand programs, by either directly applying off-the-shelf NLP pre-training techniques to the source code, or adding feat...
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- Labels: general coding task, code model, code model training, IR code model
- Authors: Cummins, Chris and Fisches, Zacharias V and Ben-Nun, Tal and Hoefler, Torsten and O’Boyle, Michael FP and Leather, Hugh
- Abstract: Machine learning (ML) is increasingly seen as a viable approach for building compiler optimization heuristics, but many ML methods cannot replicate even the simplest of the data flow analyses that are critical to making good optimization decisions. We posit that if ML cannot do that, then it is insufficiently able to reason about programs. We formulate data flow analyses as supervised learning tasks and introduce a large open dataset of programs and their corresponding labels from several analys...
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- Labels: static analysis, data-flow analysis, program optimization, code model, code model training, IR code model