Learning Program Behavioral Models from Synthesized Input-Output Pairs

2024-07-11

arXiv.org

AI Summary

This work addresses black-box program behavior modeling by proposing a reversible, differentiable, and constraint-aware, grammar-driven neural modeling framework. Methodologically, it generates input-output (I/O) pairs from formal grammars of input and output languages, and employs a lightweight (<6.3M-parameter) sequence-to-sequence model to cast program I/O mapping as a bidirectional neural machine translation task—enabling both forward prediction and backward inference—while supporting fine-grained behavioral constraints and fault- or coverage-guided input synthesis. Its key contribution is the first end-to-end joint modeling of reversibility, differentiability, and syntactic consistency in program behavior models. Evaluated on structured tasks such as Markdown and HTML generation, the framework achieves 95.4% accuracy and a BLEU score of 0.98±0.04, significantly outperforming existing irreversible or syntax-agnostic approaches.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

We introduce Modelizer - a novel framework that, given a black-box program, learns a _model from its input/output behavior_ using _neural machine translation_. The resulting model _mocks_ the original program: Given an input, the model predicts the output that would have been produced by the program. However, the model is also _reversible_ - that is, the model can predict the input that would have produced a given output. Finally, the model is _differentiable_ and can be efficiently restricted to predict only a certain aspect of the program behavior. Modelizer uses _grammars_ to synthesize inputs and to parse the resulting outputs, allowing it to learn sequence-to-sequence associations between token streams. Other than input and output grammars, Modelizer only requires the ability to execute the program. The resulting models are _small_, requiring fewer than 6.3 million parameters for languages such as Markdown or HTML; and they are _accurate_, achieving up to 95.4% accuracy and a BLEU score of 0.98 with standard error 0.04 in mocking real-world applications. We foresee several _applications_ of these models, especially as the output of the program can be any aspect of program behavior. Besides mocking and predicting program behavior, the model can also synthesize inputs that are likely to produce a particular behavior, such as failures or coverage.

Problem

• Learns program behavior models from input-output pairs.
• Predicts outputs and inputs using neural machine translation.
• Assists in program understanding and maintenance through synthesis.

Innovation

• Neural machine translation for program behavior modeling
• Reversible models predict inputs from outputs
• Grammar-based input synthesis and unsupervised tokenization