A 15 Year Perspective on Automatic Programming

By R. Balzer. 1985

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Ge Li
2. Zhi Jin
3. Bolin Wei
4. Mikhail Evtikhiev
5. Xin Xia
6. Egor Bogomolov
7. Zhiyi Fu
8. Yaroslav Sokolov

Citations in the corpus

• Deep learning in neural networks: An overview (2014, methodology): “Unlike traditional methods for automatic sequential program synthesis (e.g., Balzer, 1985; Deville & Lau, 1994; Soloway, Abbreviations in alphabetical order AE: Autoencoder AI: Artificial Intelligence ANN: Artificial Neural Network BFGS: Broyden–Fletcher–Goldfarb–Shanno BNN: Biological Neural…”
• Code Generation as a Dual Task of Code Summarization (2019, background): “On the other hand, CG is an indispensable process in which programmers write code to implement specific intents [Balzer, 1985].”
• Out of the BLEU: how should we assess quality of the Code Generation models? (2022, background): “The relative error rate, however, is slightly better than the one observed for the CoNaLa dataset: ChrF and ROUGE-L exhibit the error rate of less than 60%, see column Delta: NS. 2.”