Successful approaches to program induction require a hand-engineered domain-specific language (DSL), constraining the space of allowed programs and imparting prior knowledge of the domain. We contribute a program induction algorithm that learns a DSL while jointly training a neural network to efficiently search for programs in the learned DSL. We use our model to synthesize functions on lists, edit text, and solve symbolic regression problems, showing how the model learns a domain-specific library of program components for expressing solutions to problems in the domain.

Search is an important technique in program synthesis that allows for adaptive strategies such as focusing on particular search directions based on execution results. Several prior works have demonstrated that neural models are effective at guiding program synthesis searches.

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Domain-specific languages (DSLs) have become integral to various software workflows. Such languages offer domain-specific optimizations and abstractions that improve code readability and maintainability. However, leveraging these languages requires developers to rewrite existing code using the specific DSL's API. While large language models (LLMs) have shown some success in automatic code transpilation, none of them provide any functional correctness guarantees on the rewritten code. Another approach for automating this task is verified lifting, which relies on program synthesis to find programs in the target language that are functionally equivalent to the source language program.

The rise of Artificial Intelligence (AI) is reshaping how software systems are developed and maintained. However, AI-based systems give rise to new software issues that existing detection tools often miss. Among these, we focus on AI-specific code smells, recurring patterns in the code that may indicate deeper problems such as unreproducibility, silent failures, or poor model generalization. We introduce SpecDetect4AI, a tool-based approach for the specification and detection of these code smells at scale. This approach combines a high-level declarative Domain-Specific Language (DSL) for rule specification with an extensible static analysis tool that interprets and detects these rules for AI-based systems. We specified 22 AI-specific code smells and evaluated SpecDetect4AI on 826 AI-based systems (20M lines of code), achieving a precision of 88.66% and a recall of 88.89%, outperforming other existing detection tools. Our results show that SpecDetect4AI supports the specification and detection of AI-specific code smells through dedicated rules and can effectively analyze large AI-based systems, demonstrating both efficiency and extensibility (SUS 81.7/100).

Successful approaches to program induction require a hand-engineered domain-specific language (DSL), constraining the space of allowed programs and imparting prior knowledge of the domain. We contribute a program induction algorithm that learns a DSL while jointly training a neural network to efficiently search for programs in the learned DSL. We use our model to synthesize functions on lists, edit text, and solve symbolic regression problems, showing how the model learns a domain-specific library of program components for expressing solutions to problems in the domain.