Another embodiment is directed to automatically resolving semantic errors in a software routine. A computer system provides the software routine with known inputs and corresponding expected outputs for portions of a program fragment where an error has been localized. The computer system learns a ...
Enabled by the rise of transformers inNatural Language Processing (NLP), we’ve seen a flurry of astounding deep learning models for writing code in recent years. Computer programs that can write computer programs, generally known as the program synthesis problem, have been of research interest si...
Program synthesis is the task of automatically discovering an executable piece of code given user intent expressed using various forms of constraints such as input-output examples, demonstrations, natural language, etc. Program synthesis has direct applications for various classes of users in the technol...
In particular, the space of all programs is infinitely large and lacks the local smoothness exploited by local optimization algorithms like gradient descent or Markov Chain Monte Carlo. We adopt a strategy based on constraint-based program synthesis, where the optimization problem is translated into ...
Conclusion: 作者强调了一下自己使用了MaxSAT,这是和作者PLDI‘13工作的一个主要的区别。 笔者对于这个方法是不是还可以拓展到更多的带有logic reasoning的searching任务(例如program synthesis)感到好奇。 编辑于 2024-05-27 10:40・美国 程序分析 Datalog(编程语言) 编程语言 ...
It uses anti- unification—an important program synthesis technique78,79—to com- pute a least-general generalization that systematically aligns shared structure across rules into a single general rule. For example, com- paring F[1∣[3, 9, 7]] ≈ [1, 1∣(F[3, 9, 7])] and F[3∣[...
Program synthesis is the task of automatically generating a program consistent with a specification. Recent years have seen proposal of a number of neural approaches for program synthesis, many of which adopt a sequence generation paradigm similar to neural machine translation, in which sequ...
The primary goal of program synthesis is to minimize human intervention in the coding process, reduce errors, and improve productivity. Program synthesis often involves the use of advanced algorithms, artificial intelligence, and machine learning techniques to search the space of possible programs that ...
Program synthesis aims to create accurate, executable programs from problem specifications, specifically from natural language descriptions in our context. Recent studies have leveraged the power of reinforcement learning (RL) in conjunction with large language models (LLMs), significantly enhancing code ge...
Another approach is to use a combination of rule-based and machine learningtechniques to generate code that is both efficient and easy to understand. In conclusion, program synthesis with large language models is a promising approach to programming that has the potential to revolutionize the field....