Hao Zhong

Tong Zhang, Vikram Sharma Mailthody, Fei Sun et al.

In 1987, Jim Gray and Gianfranco Putzolu introduced the five-minute rule, a simple, storage-memory-economics-based heuristic for deciding when data should live in DRAM rather than on storage. Subsequent revisits to the rule largely retained that economics-only view, leaving host costs, feasibility limits, and workload behavior out of scope. This paper revisits the rule from first principles, integrating host costs, DRAM bandwidth/capacity, and physics-grounded models of SSD performance and cost, and then embedding these elements in a constraint- and workload-aware framework that yields actionable provisioning guidance. We show that, for modern AI platforms, especially GPU-centric hosts paired with ultra-high-IOPS SSDs engineered for fine-grained random access, the DRAM$\leftrightarrow$flash caching threshold collapses from minutes to a few seconds. This shift reframes NAND flash memory as an \emph{active data tier} and exposes a broad research space across the hardware-software stack. We further introduce MQSim-Next, a calibrated SSD simulator that supports validation and sensitivity analysis and facilitates future architectural and system research. Finally, we present two concrete case studies that showcase the software system design space opened by such memory hierarchy paradigm shift. Overall, we turn a classical heuristic into an actionable, feasibility-aware analysis and provisioning framework and set the stage for further research on AI-era memory hierarchy.

Shouliang Yang, Junming Cao, Hushuang Zeng et al.

IR-based fault localization approaches achieves promising results when locating faulty files by comparing a bug report with source code. Unfortunately, they become less effective to locate faulty methods. We conduct a preliminary study to explore its challenges, and identify three problems: the semantic gap problem, the representation sparseness problem, and the single revision problem. To tackle these problems, we propose MRAM, a mixed RNN and attention model, which combines bug-fixing features and method structured features to explore both implicit and explicit relevance between methods and bug reports for method level fault localization task. The core ideas of our model are: (1) constructing code revision graphs from code, commits and past bug reports, which reveal the latent relations among methods to augment short methods and as well provide all revisions of code and past fixes to train more accurate models; (2) embedding three method structured features (token sequences, API invocation sequences, and comments) jointly with RNN and soft attention to represent source methods and obtain their implicit relevance with bug reports; and (3) integrating multirevision bug-fixing features, which provide the explicit relevance between bug reports and methods, to improve the performance. We have implemented MRAM and conducted a controlled experiment on five open-source projects. Comparing with stateof-the-art approaches, our MRAM improves MRR values by 3.8- 5.1% (3.7-5.4%) when the dataset contains (does not contain) localized bug reports. Our statistics test shows that our improvements are significant

Zijian Jiang, Hao Zhong, Na Meng

JavaScript (JS) is one of the most popular programming languages due to its flexibility and versatility, but maintaining JS code is tedious and error-prone. In our research, we conducted an empirical study to characterize the relationship between co-changed software entities (e.g., functions and variables), and built a machine learning (ML)-based approach to recommend additional entity to edit given developers' code changes. Specifically, we first crawled 14,747 commits in 10 open-source projects; for each commit, we created one or more change dependency graphs (CDGs) to model the referencer-referencee relationship between co-changed entities. Next, we extracted the common subgraphs between CDGs to locate recurring co-change patterns between entities. Finally, based on those patterns, we extracted code features from co-changed entities and trained an ML model that recommends entities-to-change given a program commit. According to our empirical investigation, (1) three recurring patterns commonly exist in all projects; (2) 80%--90% of co-changed function pairs either invoke the same function(s), access the same variable(s), or contain similar statement(s); (3) our ML-based approach CoRec recommended entity changes with high accuracy (73%--78%). CoRec complements prior work because it suggests changes based on program syntax, textual similarity, as well as software history; it achieved higher accuracy than two existing tools in our evaluation.

Zheng Huang, Mingyu Liu, Xiaoyi Lin et al.

Vision-Language-Action (VLA) models represent a pivotal advance in embodied intelligence, yet they confront critical barriers to real-world deployment, most notably catastrophic forgetting. This issue stems from their overreliance on continuous action sequences or action chunks, which inadvertently create isolated data silos that disrupt knowledge retention across tasks. To tackle these challenges, we propose the Narrowing of Trajectory VLA (NoTVLA) framework: a novel approach that narrows its focus to sparse trajectories, thereby avoiding the catastrophic forgetting associated with dense trajectory fine-tuning. A key innovation of NoTVLA lies in its trajectory planning strategy: instead of centering on the target object's trajectory, it leverages temporal compression and spatial reasoning pruning specifically for the robot end effector's trajectory. Furthermore, training is conducted using these sparse trajectories rather than dense action trajectories, an optimization that delivers remarkable practical advantages with better performance in zero-shot. In multi-task evaluation scenarios, NoTVLA achieves superior performance and generalization compared to pi0 while operating under two critical constraints: it uses over an order of magnitude less computing power than pi0 and requires no wrist-mounted camera. This design ensures that NoTVLA's operational accuracy closely approximates that of single-task expert models. Crucially, it also preserves the model's inherent language capabilities, enabling zero-shot generalization in specific scenarios, supporting unified model deployment across multiple robot platforms, and fostering a degree of generalization even when perceiving tasks from novel perspectives.

Hao Zhong, Na Meng

API developers evolve software libraries to fix bugs, add new features, or refactor code. To benefit from such library evolution, the programmers of client projects have to repetitively upgrade their library usages and adapt their codebases to any library API breaking changes (e.g., API renaming). Such adaptive changes can be tedious and error-prone. Existing tools provide limited support to help programmers migrate client projects from old library versions to new ones. For instance, some tools extract API mappings be-tween library versions and only suggest simple adaptive changes (i.e., statement updates); other tools suggest or automate more complicated edits (e.g., statement insertions) based on user-provided exemplar code migrations. However, when new library versions are available, it is usually cumbersome and time-consuming for users to provide sufficient human-crafted samples in order to guide automatic migration. In this paper, we propose a novel approach, AutoUpdate, to further improve the state of the art. Instead of learning from change examples, we designed AutoUpdate to automate migration in a compiler-directed way. Namely, given a compilation error triggered by upgrading libraries, AutoUpdate exploits 13 migration opera-tors to generate candidate edits, and tentatively applies each edit until the error is resolved or all edits are explored. We conducted two experiments. The first experiment involves migrating 371 tutorial examples between versions of 5 popular libraries. AutoUpdate reduced migration-related compilation errors for 92.7% of tasks. It eliminated such errors for 32.4% of tasks, and 33.9% of the tasks have identical edits to manual migrations. In the second experiment, we applied AutoUpdate to migrate two real client projects of lucene. AutoUpdate successfully migrated both projects, and the migrated code passed all tests.

Shengjun Wei, Hao Zhong, Chun Shan et al.

To build a secure communications software, Vulnerability Prediction Models (VPMs) are used to predict vulnerable software modules in the software system before software security testing. At present many software security metrics have been proposed to design a VPM. In this paper, we predict vulnerable classes in a software system by establishing the system's weighted software network. The metrics are obtained from the nodes' attributes in the weighted software network. We design and implement a crawler tool to collect all public security vulnerabilities in Mozilla Firefox. Based on these data, the prediction model is trained and tested. The results show that the VPM based on weighted software network has a good performance in accuracy, precision, and recall. Compared to other studies, it shows that the performance of prediction has been improved greatly in Pr and Re.