Wednesday 09 April 2025
A recent study has shed new light on how computers can be programmed to solve complex problems by breaking them down into smaller, more manageable tasks. This approach is known as decomposition, and it’s a key component of program synthesis – the process of generating code that meets specific requirements.
The research team used two domains to test their model: Robustfill, which involves manipulating strings of text, and Deepcoder, which deals with dynamic data structures and complex task structures. The results showed that the model was able to decompose tasks more effectively in Robustfill, where the intermediate states remained constant, whereas it struggled in Deepcoder, where the data changed dynamically.
In both domains, the team used a technique called ExeDec, which involves creating subprograms that can be combined to solve a problem. The model is trained on examples of how these subprograms should behave and then generates new code by combining them in different ways.
The study found that the model was able to generate accurate solutions for many tasks, but it also struggled with others. For example, in Robustfill, the model was able to decompose tasks more effectively when they involved manipulating strings of text, but it had trouble with tasks that required more complex operations.
In Deepcoder, the model’s performance was even more varied. It was able to generate accurate solutions for some tasks, but it struggled with others. This is likely due to the dynamic nature of the data in these tasks, which makes it harder for the model to accurately decompose them.
The researchers believe that their study could have important implications for the development of artificial intelligence. By improving the ability of computers to decompose complex problems into smaller tasks, they may be able to create more accurate and efficient AI systems.
Overall, the study highlights the potential benefits of using decomposition techniques in program synthesis, but also shows that there is still much work to be done to improve their effectiveness.
Cite this article: “Breaking Down Barriers in Program Synthesis: A Study on Decomposition and Composition”, The Science Archive, 2025.
Program Synthesis, Decomposition, Artificial Intelligence, Computer Programming, Code Generation, Subprograms, Exedec, Robustfill, Deepcoder, Machine Learning
