Wei Li, Xin Zhang, Zhongxin Guo, Shaoguang Mao, Wen Luo, Guangyue Peng, Yangyu Huang, Houfeng Wang, Scarlett Li. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Existing methods for code generation use code snippets as seed data, restricting the complexity and diversity of the synthesized data. In this paper, we introduce a novel feature tree-based synthesis framework, which revolves around hierarchical code features derived from high-level abstractions of code. The feature tree is constructed from raw data and refined iteratively to increase the quantity and diversity of the extracted features, which captures and recognizes more complex patterns and relationships within the code. By adjusting the depth and breadth of the sampled subtrees, our framework provides precise control over the complexity of the generated code, enabling functionalities that range from function-level operations to multi-file scenarios. We fine-tuned widely-used base models to obtain EpiCoder series, achieving state-of-the-art performance on multiple benchmarks at both the function and file levels. In particular, empirical evidence indicates that our approach shows significant potential in the synthesizing of repository-level code data. Our code and data are publicly available.

For proof-of-work blockchains such as Ethereum, the mining power decentralization is an important discussion point in the community. Previous studies mostly focus on the aggregated power of the mining pools, neglecting the pool participants who are the source of the pools' power. In this paper, we present the first large-scale study of the pool participants in Ethereum's mining pools. Pool participants are not directly observable because they communicate with their pools via private channels. However, they leave "footprints" on chain as they use Ethereum accounts to anonymously receive rewards from mining pools. For this study, we combine several data sources to identify 62,358,646 pool reward transactions sent by 47 pools to their participants over Ethereum's entire near 5-year history. Our analyses about these transactions reveal interesting insights about three aspects of pool participants: the power decentralization at the participant level, their pool-switching behavior, and why they participate in pools. Our results provide a complementary and more balanced view about Ethereum's mining power decentralization at a deeper level.

Ethereum is an emerging distributed computing platform that supports a decentralized replicated virtual machine at a large scale. Transactions in Ethereum are specified in smart contracts, disseminated through broadcast, accepted into the chain of blocks, and then executed on each node. In this new Dissemination-Consensus-Execution (DiCE) paradigm, the time interval between when a transaction is known (during the dissemination phase) to when the transaction is executed (after the consensus phase) offers a window of opportunity to accelerate transaction processing through speculative execution. However, the traditional speculative execution, which hinges on the ability to predict the future accurately, is inadequate because of DiCE's many-future nature.

Anti-piracy is fundamentally a procedure that relies on collecting data from the open anonymous population, so how to incentivize credible reporting is a question at the center of the problem. Industrial alliances and companies are running anti-piracy incentive campaigns, but their effectiveness is publicly questioned due to the lack of transparency. We believe that full transparency of a campaign is necessary to truly incentivize people. It means that every role, e.g., content owner, licensee of the content, or every person in the open population, can understand the mechanism and be assured about its execution without trusting any single role. We see this as a distributed system problem. In this paper, we present Argus, a fully transparent incentive system for anti-piracy campaigns. The groundwork of Argus is to formulate the objectives for fully transparent incentive mechanisms, which securely and comprehensively consolidate the different interests of all roles. These objectives form the core of the Argus design, highlighted by our innovations about a Sybil-proof incentive function, a commit-and-reveal scheme, and an oblivious transfer scheme. In the implementation, we overcome a set of unavoidable obstacles to ensure security despite full transparency. Moreover, we effectively optimize several cryptographic operations so that the cost for a piracy reporting is reduced to an equivalent cost of sending about 14 ETH-transfer transactions to run on the public Ethereum network, which would otherwise correspond to thousands of transactions. With the security and practicality of Argus, we hope real-world anti-piracy campaigns will be truly effective by shifting to a fully transparent incentive mechanism.

Trusted Execution Environments (TEEs) ensure strong data confidentiality for applications running in the TEEs even on untrusted servers. In particular, TEEs are expected to bring significant benefits to blockchain workloads for enterprise because it ensures confidentiality and correctness of transaction records without any heavy-weight data verification process such as proof-of-work. For example, Coco [3] improves both confidentiality and transaction throughput of existing blockchain protocols by utilizing TEE features.

Large-scale public cloud providers invest billions of dollars into their cloud infrastructure and operate hundreds of thousands of servers across the globe. For various reasons, much of this provisioned server capacity runs at low average utilization, and there is tremendous competitive pressure to increase utilization. Conceptually, the way to increase utilization is clear: Run time-insensitive batch-job workloads as secondary background tasks whenever server capacity is underutilized; and evict these workloads when the server's primary task requires more resources. Big data analytic tasks would seem to be an ideal fit to run opportunistically on such transient resources in the cloud. In reality, however, modern distributed data processing systems such as MapReduce or Spark are designed to run as the primary task on dedicated hardware, and they perform badly on transiently available resources because of the excessive cost of cascading re-computations in case of evictions.