Uplift modeling has obtained significant attention, with broad applications in medicine, economics, and marketing. For example, in a push notification scenario, accurately estimating the uplift of different push frequencies on user activation and notification switch close rate is critical for balancing user experience and business goals. Existing methods only use binary labels, i.e., convert or not within the observational window. However, they ignore time information (e.g., users who convert on day 1 vs. day 14 reflect different sensitivities) and fail to model potential closures outside the window, i.e., due to treatments always taking time to manifest causal impacts on outcomes, the potential outcomes of interest cannot be observed promptly and accurately. Failing to account for these issues can result in skewed uplift modeling. To address this gap, this work examines how observation timing influences the assessment of uplift by explicitly modeling the potential response time. Theoretical analysis establishes the conditions for identifiability under delayed feedback scenarios. We introduce CFR-DF (Counterfactual Regression with Delayed Feedback), a systematic framework that jointly learns both the latent response times and the underlying potential outcomes. Empirical evaluations on synthetic and real-world datasets, including an A/B test with over 1 billion users for 14 days, validate the approach, demonstrating its ability to handle temporal delays and improve estimation accuracy compared to previous uplift modeling methods.

Code retrieval and summarization are two tasks often employed by software developers to reuse code that spreads over online repositories. In this paper, we present a neural framework that allows bidirectional mapping between source code and natural language to improve these two tasks. Our framework, BVAE, is designed to have two Variational AutoEncoders (VAEs) to model bimodal data: C-VAE for source code and L-VAE for natural language. Both VAEs are trained jointly to reconstruct their input as much as possible with regularization that captures the closeness between the latent variables of code and description. BVAE could learn semantic vector representations for both code and description and generate completely new descriptions for arbitrary code snippets. We design two instance models of BVAE for retrieval and summarization tasks respectively and evaluate their performance on a benchmark which involves two programming languages: C# and SQL. Experiments demonstrate BVAE’s potential on the two tasks.

Open source software ecosystems evolve ways to balance the workload among groups of participants ranging from core groups to peripheral groups. As ecosystems grow, it is not clear whether the mechanisms that previously made them work will continue to be relevant or whether new mechanisms will need to evolve. The impact of failure for critical ecosystems such as Linux is enormous, yet the understanding of why they function and are effective is limited. We, therefore, aim to understand how the Linux kernel sustains its growth, how to characterize the workload of maintainers, and whether or not the existing mechanisms are scalable. We quantify maintainers' work through the files that are maintained, and the change activity and the numbers of contributors in those files. We find systematic differences among modules; these differences are stable over time, which suggests that certain architectural features, commercial interests, or module-specific practices lead to distinct sustainable equilibria. We find that most of the modules have not grown appreciably over the last decade; most growth has been absorbed by a few modules. We also find that the effort per maintainer does not increase, even though the community has hypothesized that required effort might increase. However, the distribution of work among maintainers is highly unbalanced, suggesting that a few maintainers may experience increasing workload. We find that the practice of assigning multiple maintainers to a file yields only a power of 1/2 increase in productivity. We expect that our proposed framework to quantify maintainer practices will help clarify the factors that allow rapidly growing ecosystems to be sustainable.