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Selecting the best code solution from multiple generated ones is an essential task in code generation, which can be achieved by using some reliable validators (e.g., developer-written test cases) for assistance. Since reliable test cases are not always ...

Index tuning is crucial for database performance, but existing algorithms are often tailored for single instances, posing challenges in cluster architectures. While uniform tuning simplifies matters by applying identical configurations across ...

Recent years have witnessed the success of introducing deep learning models to time series forecasting. From a data generation perspective, we illustrate that existing models are susceptible to distribution shifts driven by temporal contexts, whether ...

Inspired by the great potential of Large Language Models (LLMs) for solving complex coding tasks, in this paper, we propose a novel approach, named Code2API, to automatically perform APIzation for Stack Overflow code snippets. Code2API does not require ...

Graph contrastive learning often faces challenges when data augmentations compromise the graph's critical attributes, introducing the risk of generating noise-positive pairs. Although recent methods have attempted to address these issues, they either ...

Graph label propagation (<monospace>LP</monospace>) is a core component in many downstream applications such as fraud detection, recommendation and image segmentation. In this paper, we propose <monospace>GLP</monospace>, a GPU-based framework to enable ...

Indexes are vital for data retrieval performance. For online scenarios with dynamic workloads, index tuning is challenging. A commonly used strategy is to launch tuning requests periodically, yet resource-intensive tuning sessions can obstruct it, ...

Cloud-native databases have become the de-facto choice for mission-critical applications on the cloud due to the need for high availability, resource elasticity, and cost efficiency. Meanwhile, driven by the increasing connectivity between data ...

Recent studies have highlighted the limitations of message-passing based graph neural networks (GNNs), e.g., limited model expressiveness, over-smoothing, over-squashing, etc. To alleviate these issues, Graph Transformers (GTs) have been proposed which ...

Natural language to SQL (NL2SQL) techniques provide a convenient interface to access databases, especially for non-expert users, to conduct various data analytics. Existing methods often employ either a rule-base approach or a deep learning based ...

Using Unbiased Learning to Rank (ULTR) to train the ranking model with biased click logs has attracted increased research interest. The key idea is to explicitly model the user's observation behavior when building the ranker with a large number of click ...

Unbiased learning to rank (ULTR) aims to train an unbiased ranking model from biased user click logs. Most of the current ULTR methods are based on the examination hypothesis (EH), which assumes that the click probability can be factorized into two ...

A significant fraction of data in cloud storage is rarely accessed, referred to as cold data. Accurately identifying and efficiently managing cold data on cost-effective storages is one of the major challenges for cloud providers, which balances between ...

The prevalence of graph structures attracts a surge of investigation on graph data, enabling several downstream tasks such as multi-graph classification. However, in the multi-graph setting, graphs usually follow a long-tailed distribution in terms of ...

Application Programming Interfaces (APIs) typically come with usage constraints. The violations of these constraints (i.e. API misuses) can cause significant problems in software development. Existing methods mine frequent API usage patterns from ...

Counterfactual Learning to Rank (CLTR) becomes an attractive research topic due to its capability of training ranker with click logs. However, CLTR inherently suffers from a large amount of bias caused by confounders, variables that affect both the ...

Fraud detection is a pressing challenge for most financial and commercial platforms. In this paper, we study the processing pipeline of fraud detection in a large e-commerce platform of TaoBao. Graph label propagation (LP) is a core component in this ...

API misuses cause significant problem in software development. Existing methods detect API misuses against frequent API usage patterns mined from codebase. They make a naive assumption that API usage that deviates from the most-frequent API usage is a ...

Sequential user behavior modeling plays a crucial role in online user-oriented services, such as product purchasing, news feed consumption, and online advertising. The performance of sequential modeling heavily depends on the scale and quality of ...

API usage directives in official API documentation describe the contracts, constraints and guidelines for using APIs in natural language. Through the investigation of API misuse scenarios on Stack Overflow, we identify three barriers that hinder the ...