It is commonly known that any Bayesian network can be implemented as a probabilistic program, but the reverse direction is not so clear. In this work, we address the open question to what extent a probabilistic program with user-labelled sample statements and while loops – features found in languages like Gen, Turing, and Pyro – can be represented graphically. To this end, we extend existing operational semantics to support these language features. By translating a program to its control-flow graph, we define a sound static analysis that approximates the dependency structure of the random variables in the program. As a result, we obtain a static factorisation of the implicitly defined program density, which is equivalent to the known Bayesian network factorisation for programs without loops and constant labels, but constitutes a novel graphical representation for programs that define an unbounded number of random variables via loops or dynamic labels. We further develop a sound program slicing technique to leverage this structure to statically enable three well-known optimisations for the considered program class: we reduce the variance of gradient estimates in variational inference and we speed up both single-site Metropolis Hastings and sequential Monte Carlo. These optimisations are proven correct and empirically shown to match or outperform existing techniques.

Probabilistic programming allows developers to focus on the modeling aspect in the Bayesian workflow by abstracting away the posterior inference machinery. In practice, however, programming errors specific to the probabilistic environment are hard to fix without deep knowledge of the underlying systems. Like in classical software engineering, static program analysis methods could be employed to catch many of these errors. In this work, we present the first framework to formulate static analyses for probabilistic programs in a language-agnostic manner: LASAPP. While prior work focused on specific languages, all analyses written with our framework can be readily applied to new languages by adding easy-to-implement API bindings. Our prototype supports five popular probabilistic programming languages out-of-the-box. We demonstrate the effectiveness and expressiveness of the LASAPP framework by presenting four provably-correct language-agnostic probabilistic program analyses that address problems discussed in the literature and evaluate them on over 200 real-world programs.

Performance is key to the success and adoption of software systems. In video games, performance is commonly highlighted as one of the top quality concerns raised by players. To check the performance of their systems, development teams tend to rely on profiling and monitoring tools, which observe program executions to identify regressions. The usage of static analysis tools for this purpose has been so far limited. Lately, the success of Large Language Models in many code analytics tools led to attempts to leverage them in static performance analysis. These studies showed promising results in predicting runtime and regressions on large public datasets. In this paper, we evaluate the usability of such models in practice, and particularly in the domain of video games. We train a state-of-the-art neural network on the Code4Bench dataset to predict runtime regressions for programming competition programs, then evaluate its ability to generalize to new domains. Our results show that these models achieve great results (e.g. 95.73% accuracy for performance comparison) on the original domain for programs solving in-sample programming tasks, yet fail to generalize to out-of-sample tasks. Furthermore, we show that transfer techniques such as domain adversarial adaptation and model fine-tuning are not sufficient to transfer these models to the target industrial domain of AAA games.