When using observational causal models, practitioners often want to disentangle the effects of many related, partially-overlapping treatments. Examples include estimating treatment effects of different marketing touchpoints, ordering different types of products, or signing up for different services. Common approaches that estimate separate treatment coefficients are too noisy for practical decision-making. We propose a computationally light model that uses a customized ridge regression to move between a heterogeneous and a homogenous model: it substantially reduces MSE for the effects of each individual sub-treatment while allowing us to easily reconstruct the effects of an aggregated treatment. We demonstrate the properties of this estimator in theory and simulation, and illustrate how it has unlocked targeted decision-making at Wayfair.

Testing is an essential part of modern software engineering to build reliable programs. As testing the software is important but expensive, automatic test case generation methods have become popular in software development. Unlike traditional search-based coverage-guided test generation like fuzzing, neural test generation backed by large language models can write tests that are semantically meaningful and can be understood by other maintainers. However, compared to regular code corpus, unit tests in the datasets are limited in amount and diversity. In this paper, we present a novel data augmentation technique **FuzzAug**, that combines the advantages of fuzzing and large language models. FuzzAug not only keeps valid program semantics in the augmented data, but also provides more diverse inputs to the function under test, helping the model to associate correct inputs embedded with the function's dynamic behaviors with the function under test. We evaluate FuzzAug's benefits by using it on a neural test generation dataset to train state-of-the-art code generation models. By augmenting the training set, our model generates test cases with $11\%$ accuracy increases. Models trained with FuzzAug generate unit test functions with double the branch coverage compared to those without it. FuzzAug can be used across various datasets to train advanced code generation models, enhancing their utility in automated software testing. Our work shows the benefits of using dynamic analysis results to enhance neural test generation. Code and data will be publicly available.

The remarkable capability of large language models (LLMs) in generating high-quality code has drawn increasing attention in the software testing community. However, existing code LLMs often demonstrate unsatisfactory capabilities in generating accurate and complete tests since they were trained on code snippets collected without differentiating between code for testing purposes and other code. In this paper, we present a large-scale dataset UniTSyn, which is capable of enhancing the prowess of LLMs for Unit Test Synthesis. Associating tests with the tested functions is crucial for LLMs to infer the expected behavior and the logic paths to be verified. By leveraging Language Server Protocol, UniTSyn achieves the challenging goal of collecting focal-test pairs without per-project execution setups or per-language heuristics that tend to be fragile and difficult to scale. It contains 2.7 million focal-test pairs across five mainstream programming languages, making it possible to be utilized for enhancing the test generation ability of LLMs. The details of UniTSyn can be found in Table 1. Our experiments demonstrate that, by building an autoregressive model based on UniTSyn, we can achieve significant benefits in learning and understanding unit test representations, resulting in improved generation accuracy and code coverage across all evaluated programming languages. Code and data will be publicly available.