Precise event spotting (PES) aims to recognize fine-grained events at exact moments and has become a key component of sports analytics. This task is particularly challenging due to rapid succession, motion blur, and subtle visual differences. Consequently, most existing methods rely on domain-specific, end-to-end training with large labeled datasets and often struggle in few-shot conditions due to their dependence on pixel- or pose-based inputs alone. However, obtaining large labeled datasets is practically hard. We propose a Unified Multi-Entity Graph Network (UMEG-Net) for few-shot PES. UMEG-Net integrates human skeletons and sport-specific object keypoints into a unified graph and features an efficient spatio-temporal extraction module based on advanced GCN and multi-scale temporal shift. To further enhance performance, we employ multimodal distillation to transfer knowledge from keypoint-based graphs to visual representations. Our approach achieves robust performance with limited labeled data and significantly outperforms baseline models in few-shot settings, providing a scalable and effective solution for few-shot PES.

Visual language model (VLM) agents show great promise in automating end-to-end (E2E) web testing against requirements in natural language. However, the probabilistic nature of language models can have inherent hallucinations. Therefore, given a detected inconsistency between the requirement and the web application, it is hard to distinguish whether it stems from the hallucination or a real application bug. Addressing this issue presents two core technical challenges: the implicit oracle inference challenge, where the agent must act as its own oracle to implicitly decide if the application's behavior is correct without guidance, and the probabilistic inference challenge, where an LLM's inconsistent reasoning undermines its trustworthiness as an oracle. Existing LLM-based approaches fail to capture such implicit oracles, either by treating any page navigation that doesn't crash as a success, or by checking each state in isolation, thus missing bugs dependent on context from prior steps. We introduce WebTestPilot, an LLM-based agent designed to address these challenges. WebTestPilot uses (1) a symbolization layer which detects and symbolizes critical GUI elements on the web application into symbols (i.e., variables) and (2) translates natural language specification into a sequence of steps, each of which is equipped with inferred pre- and post-conditions over the symbols as an oracle. This oracle captures data, temporal, and causal dependencies, enabling the validation of implicit requirements. To advance research in this area, we build a benchmark of bug-injected web apps for evaluating NL-to-E2E testing. The results show that WebTestPilot achieves a task completion rate of 99

Large Language Models (LLMs) have significantly improved programming efficiency by translating natural language into code, yet their performance deteriorates when handling large-scale, multi-modal requirement documents containing hundreds of scenarios, often producing incorrect implementations or missing critical constraints. To address this challenge, we propose ARC (Agentic Requirement Compilation), a framework that compiles DSL-based requirement documents into runnable web systems while automatically generating modular software architecture, comprehensive test suites, and traceability across requirements, design, and code. ARC adopts a bidirectional test-driven agentic workflow, combining a top-down architecture design phase with a bottom-up implementation phase to ensure that generated code satisfies synthesized tests. We evaluate ARC on six runnable web system benchmarks and the AppForge benchmark of 101 mobile app generation tasks. Across three independent trials, ARC consistently outperforms state-of-the-art LLM-based baselines, achieving 50.6

Test cases are essential for software development and maintenance. In practice, developers derive multiple test cases from an implicit pattern based on their understanding of requirements and inference of diverse test scenarios, each validating a specific behavior of the focal method. However, producing comprehensive tests is time-consuming and error-prone: many important tests that should have accompanied the initial test are added only after a significant delay, sometimes only after bugs are triggered. Existing automated test generation techniques largely focus on code coverage. Yet in real projects, practical tests are seldom driven by code coverage alone, since test scenarios do not necessarily align with control-flow branches. Instead, test scenarios originate from requirements, which are often undocumented and implicitly embedded in a project's design and implementation. However, developer-written tests are frequently treated as executable specifications; thus, even a single initial test that reflects the developer's intent can reveal the underlying requirement and the diverse scenarios that should be validated. In this work, we propose TestGeneralizer, a framework for generalizing test cases to comprehensively cover test scenarios. TestGeneralizer orchestrates three stages: (1) enhancing the understanding of the requirement and scenario behind the focal method and initial test; (2) generating a test scenario template and crystallizing it into various test scenario instances; and (3) generating and refining executable test cases from these instances. We evaluate TestGeneralizer against three state-of-the-art baselines on 12 open-source Java projects. TestGeneralizer achieves significant improvements: +31.66

Issue localization, which identifies code locations requiring modification from issue descriptions, is a critical step in automated software maintenance. Existing approaches predominantly attempt to directly align issue descriptions with code elements, yet often struggle due to the inherent abstraction gap between the issue description and code implementation. Seeking alternative signals, our theoretical analysis suggests that test suites can serve as executable proxies for requirements, reducing localization uncertainty by 7.73 bits of entropy on average. A large-scale empirical study on 18 repositories validates this premise: existing tests cover 96.98% of ground-truth files, and the two-hop pathway yields stronger semantic connectivity than direct matching in 82.4% of cases. Despite their potential, leveraging tests for localization faces two key challenges: the semantic gap separating issue descriptions from test identifiers, and the substantial noise in execution traces from infrastructure code. To address these, we propose IssueExec, which bridges the semantic gap through domain-knowledge-enhanced test representations and filters noise via hierarchical trace analysis. Experiments on SWE-bench Lite show that IssueExec achieves state-of-the-art performance, improving function-level Recall@1 by 41.57% over the strongest baseline. When integrated into the Agentless pipeline, IssueExec resolves 17.72% more issues, demonstrating practical downstream benefits.

Semantic code search has been widely adopted in both academia and industry. These approaches embed natural-language queries and code snippets into a shared embedding space and retrieve results based on vector similarity. Despit strong performance on benchmark datasets, they often suffer from poor explainability and generalization. Retrieved code may appear semantically similar yet miss critical functional requirements of the query, while providing no explanation of why the result was retrieved. Moreover, such failures become more severe under distribution shift, where models struggle to generalize to unseen benchmarks. In this work, we propose XSearch, an intrinsically explainable code search framework. Our key insight is that by relying on global embedding similarity, existing retrievers inherently take an inductive view. They learn statistical patterns rather than truly understanding the query's functional requirements. We address this problem by reformulating code search as a deductive concept alignment problem. XSearch (i) identifies functional concepts in the query and (ii) explicitly aligns them with corresponding code statements. This explain-then-predict design produces inherent concept-level explanations and mitigates shortcut learning that harms out-of-distribution generalization. We train an encoder with explicit concept-alignment objectives and perform retrieval through explicit matching between query concepts and code statements. Experiments show that, trained on CodeSearchNet using GraphCodeBERT (125M parameters), XSearch improves performance on out-of-distribution benchmarks from 0.02 to 0.33 (15x) over eight state-of-the-art retrievers, and consistently outperforms both encoder- and decoder-based baselines with up to 7B parameters. A user study demonstrates that concept-alignment explanations enable users to evaluate retrieved results faster and more accurately.

Large Language Models (LLMs) are increasingly used in sports analytics for tasks such as coaching recommendations, video analysis, and automated commentary generation. However, their outputs are not inherently reliable due to well-known hallucination issues. Probabilistic Model Checking (PMC), by contrast, has long been employed for rigorous reliability analysis in safety-critical systems. For example, the reliability of an aircraft can be systematically derived from the reliability of its constituent components, such as engines, wings, and sensors. We extend PMC to a new domain: sports analytics. Specifically, we model a player’s overall performance (e.g., winning probability) as a function of the success rates of individual sub-skills, such as serve, forehand, and backhand in tennis. The first part of the talk highlights the limitations of LLMs in complex decision-making and video analytics, and presents our recent work integrating PMC, LLMs, and computer vision to enable principled and explainable sports analysis. The second part introduces a forward-looking vision for life after superintelligence, termed the Player Era. In this vision, human society evolves into four interconnected roles: Player, Explorer, Co-Creator, and Gatekeeper, forming the foundation of a civilization centered on meaning, creativity, and responsibility.

Supervised fine-tuning (SFT) on long teacher trajectories is the dominant way to instill investigation and reasoning in open software-engineering (SWE) agents. Since every retained response becomes an imitation target, the student inherits the final outcome and intermediate flaws, including ungrounded leaps and redundant loops. High-quality training data must be effective(each step is grounded and narrows the agent's epistemic gap to the correct fix) and efficient(each step is information-bearing rather than redundant or looping). Existing recipes filter or relabel teacher rollouts using only a binary terminal verifier, which does not directly target these axes and provides no supervision on instances where the teacher fails. Most real issue includes a developer-authored reference patch, p^⋆, revealing the file paths, runtime behaviors, and coding conventions presupposed by the correct fix, yet standard pipelines discard it. We propose Patches-to-Trajectories (P2T), which uses p^⋆ as privileged information during curation and formulates trajectory construction as bi-objective optimization over per-step effectiveness and trajectory length. A reverse phase distills p^⋆ into a latent process graph, G^⋆, of contextual facts and solution milestones. A forward phase curates trajectories from blinded teacher continuations by scoring per-step progress against G^⋆ under a leakage-blocking groundedness check and retaining the shortest effective segments. Using only 1.8k curated SWE-Gym instances, P2T improves effectiveness and efficiency over outcome-filtered SFT and its tool-error-masking variant. On SWE-bench Verified, it raises Pass@1 by up to 10.8 points while reducing per-instance inference cost by 15

Large language models (LLMs) for code editing have achieved remarkable progress, yet recent empirical I studies reveal a fundamental disconnect between technical accuracy and developer productivity. Despite their I strong benchmark performance, developers complete tasks 19% slower when using Al assistance, with over I 68.81% of recommendations disrupting their mental flow. This misalignment stems from the use of static I commit snapshots that lack temporal information, causing models to optimize for end results rather than the incremental, context-sensitive steps that align with developers' natural reasoning process. To bridge this gap, we present EditFlow, which benchmarks and optimizes subsequent code edit rec-& ocy;& kcy;& ocy;& zhcy;& iecy;& ncy;dation systems through the construction-of-developer oditing-fleave Belitelow addresses-three-key challenges. First, collecting edit-order data that reflects developers' flow is inherently difficult: manual annota-tion introduces prohibitive overhead, while development logs capture only single trajectories instead of all plausible editing flows. Second, benchmarking recommendation performance against developers' ongoing editing flow requires a digital-twin-like simulation that can faithfully simulate the editing process. Third, existing heterogeneous systems vary drastically in scale and architecture, posing challenges for developing a unified optimization strategy that endows all models with mental-flow awareness regardless of design or capability. To overcome these challenges, we propose three tightly coupled components: (1) a prompt auto-tuning mechanism that learns an optimized prompt for inferring the relative order between two edits, (2) a digital twin that replays reconstructed edit sequences to simulate developers' editing process, and (3) EditFlow, a unified optimization strategy that optimizes the flow continuity of subsequent edit suggestions based on developers' ongoing flow. Evaluations across diverse benchmarks, including manually annotated commits, real-world industrial code, and open-source repositories, show that EditFlow improves order reconstruction accuracy by 63.81%, reduces flow violations by over 75%, and boosts recommendation precision by 66.99%. A user study with 32 developers further demonstrates 25.11% faster task completion and significantly higher perceived recommendation quality. To the best of our knowledge, EditFlow is the first to evaluate and optimize code edit recommendation systems from the perspective of developers' mental flow, establishing flow-awareness as a new dimension for advancing human-Al code collaboration.

Practical classification requires both high predictive accuracy and reliable confidence for human-AI collaboration. Given that a high-quality dataset is expensive and sometimes impossible, learning with noisy labels (LNL) is of great importance. The state-of-the-art works propose many denoising approaches by categorically correcting the label noise, i.e., change a label from one class to another. While effective in improving accuracy, they are less effective for learning reliable confidence. This happens especially when the number of classes grows, giving rise to more ambiguous samples. In addition, traditional approaches usually curate the training dataset (e.g., reweighting samples or correcting data labels) by intrinsically learning normalities from the noisy dataset. The curation performance can suffer when the noisy ratio is high enough to form a polluting normality. In this work, we propose a training-time data-curation framework, TrainRef, to uniformly address predictive accuracy and confidence calibration by (1) an extrinsic small set of reference samples $D_{{ref}}$ to avoid normality pollution and (2) curate labels into a class distribution instead of a categorical class to handle sample ambiguity. Our insights lie in that the extrinsic information allows us to select more precise clean samples even when $|D_{{ref}}|$ equals to the number of classes (i.e., one sample per class). Technically, we design (1) a reference augmentation technique to select clean samples from the dataset based on $D_{{ref}}$; and (2) a model-dataset co-evolving technique for a near-perfect embedding space, which is used to vote on the class-distribution for the label of a noisy sample. Extensive experiments on CIFAR-100, Animal10N, and WebVision demonstrate that TrainRef outperform the state-of-the-art denoising techniques (DISC, L2B, and DivideMix) and model calibration techniques (label smoothing, Mixup, and temperature scaling). Furthermore, our user study shows that the model confidence trained by TrainRef well aligns with human intuition. More demonstration, proof, and experimental details are available at https://sites.google.com/view/train-ref.

GUI test migration aims to produce test cases with events and assertions to test specific functionalities of a target app. Existing migration approaches typically focus on the widget-mapping paradigm that maps widgets from source apps to target apps. However, since different apps may implement the same functionality in different ways, direct mapping may result in incomplete or buggy test cases, thus significantly impacting the effectiveness of testing the target functionality and the practical applicability of migration approaches. In this article, we propose a new migration paradigm (i.e., the abstraction-concretization paradigm) that first abstracts the test logic for the target functionality and then utilizes this logic to generate the concrete GUI test case. Furthermore, we introduce MACdroid, the first approach that migrates GUI test cases based on this paradigm. Specifically, we propose an abstraction technique that utilizes source test cases from source apps targeting the same functionality to extract a general test logic for that functionality. Then, we propose a concretization technique that utilizes the general test logic to guide an LLM in generating the corresponding GUI test case (including events and assertions) for the target app. We evaluate MACdroid on two widely used datasets (including 31 apps, 34 functionalities, and 123 test cases). On the FrUITeR dataset, the test cases generated by MACdroid successfully test 64% of the target functionalities, improving the baselines by 191%. On the Lin dataset, MACdroid successfully tests 75% of the target functionalities, outperforming the baselines by 42%. These results underscore the effectiveness of MACdroid in GUI test migration.

Efficient CUDA implementations of attention mechanisms are critical to modern deep learning systems, yet supporting diverse and evolving attention variants remains challenging. Existing frameworks and compilers trade performance for flexibility, while expert-written kernels achieve high efficiency but are difficult to adapt. Recent work explores large language models (LLMs) for GPU kernel generation, but prior studies report unstable correctness and significant performance gaps for complex operators such as attention.We present CuBridge, an LLM-based framework that adapts expert-written attention kernels through a structured lift–transfer–lower workflow. CuBridge starts from expert-written CUDA attention kernels and lifts them into an executable intermediate representation that makes execution orchestration explicit while abstracting low-level CUDA syntax. Given a user-provided PyTorch specification, CuBridge generates and verifies a target IR program, then reconstructs optimized CUDA code via reference-guided lowering. Across diverse attention variants and GPU platforms, CuBridge consistently produces correct kernels and substantially outperforms general frameworks, compiler-based approaches, and prior LLM-based methods.

This short report presents the 2025 edition of the Java Unit Testing Competition in which four test generation tools (EVOFUZZ, EVOSUITE, BBC, and RANDOOP) were benchmarked on a freshly selected set of 55 Java classes from six different open source projects. The benchmarking was based on structural metrics, such as code and mutation coverage of the classes under test, as well as on the readability of the generated test cases.

Recently, the rise of code-centric Large Language Models (LLMs) has reshaped the software engineering world with low-barrier tools like Copilot that can easily generate code. However, there is no correctness guarantee for the code generated by LLMs, which suffer from the hallucination problem, and their output is fraught with risks. Besides, the end-to-end process from specification to code through LLMs is a non-transparent and uncontrolled black box. This opacity makes it difficult for users to understand and trust the generated code. Addressing these challenges is both necessary and critical. In contrast, program refinement transforms high-level specification statements into executable code while preserving correctness. Traditional tools for program refinement are primarily designed for formal methods experts and lack automation and extensibility. We apply program refinement to guide LLM and validate the LLM-generated code while transforming refinement into a more accessible and flexible framework. To initiate this vision, we propose Refine4LLM, an approach that aims to: (1) Formally refine the specifications, (2) Automatically prompt and guide the LLM using refinement calculus, (3) Interact with the LLM to generate the code, (4) Verify that the generated code satisfies the constraints, thus guaranteeing its correctness, (5) Learn and build more advanced refinement laws to extend the refinement calculus. We evaluated Refine4LLM against the state-of-the-art baselines on program refinement and LLMs benchmarks.The experiment results show that Refine4LLM can efficiently generate more robust code and reduce the time for refinement and verification.

Password strength meters (PSMs) have been widely used by websites to gauge password strength, encouraging users to create stronger passwords. Popular data-driven PSMs, e.g., based on Markov, Probabilistic Context-free Grammar (PCFG) and neural networks, alarm strength based on a model learned from real passwords. Despite their proven effectiveness, the secure utility that arises from the leakage of trained passwords remains largely overlooked. To address this gap, we analyze 11 PSMs and find that 5 data-driven meters are vulnerable to membership inference attacks that expose their trained passwords, and seriously, 3 rule-based meters openly disclose their blocked passwords. We specifically design a PSM privacy leakage evaluation approach, and uncover that a series of general data-driven meters are vulnerable to leaking between 10^4 to 10^5 trained passwords, with the PCFG-based models being more vulnerable than other counterparts; furthermore, we aid in deriving insights that the inherent utility-privacy tradeoff is not as severe as previously thought. To further exploit the risks, we develop novel meter-aware attacks when a clever attacker can filter the used passwords during compromising accounts on websites using the meter, and experimentally show that attackers targeting websites that deployed the popular Zxcvbn meter can compromise an additional 5.84% user accounts within 10 attempts, demonstrating the urgent need for privacy-preserving PSMs that protect the confidentiality of the meter's used passwords. Finally, we sketch some counter-measures to mitigate these threats.

With the rapid advancement of cloud-native computing, securing cloud environments has become an important task. Log-based Anomaly Detection (LAD) is the most representative technique used in different systems for attack detection and safety guarantee, where multiple LAD methods and relevant datasets have been proposed. However, even though some of these datasets are specifically prepared for cloud systems, they only cover limited cloud behaviors and lack information from a whole-system perspective. Another critical issue to consider is normality shift, which implies that the test distribution could differ from the training distribution and highly affect the performance of LAD. Unfortunately, existing works only focus on simple shift types such as chronological changes, while other cloud-specific shift types are ignored, e.g., different deployed cloud architectures. Therefore, a dataset that captures diverse cloud system behaviors and various types of normality shifts is essential. To fill this gap, we construct a dataset CAShift to evaluate the performance of LAD in cloud, which considers different roles of software in cloud systems, supports three real-world normality shift types (application shift, version shift, and cloud architecture shift), and features 20 different attack scenarios in various cloud system components. Based on CAShift, we conduct a comprehensive empirical study to investigate the effectiveness of existing LAD methods in normality shift scenarios. Additionally, to explore the feasibility of shift adaptation, we further investigate three continuous learning approaches, which are the most common methods to mitigate the impact of distribution shift. Results demonstrated that 1) all LAD methods suffer from normality shift where the performance drops up to 34%, and 2) existing continuous learning methods are promising to address shift drawbacks, but the ratio of data used for model retraining and the selection of algorithms highly affect the shift adaptation, with an increase in the F1-Score of up to 27%. Based on our findings, we offer valuable implications for future research in designing more robust LAD models and methods for LAD shift adaptation.

Test cases are valuable assets for maintaining software quality. State-of-the-art automated test generation techniques typically focus on maximizing program branch coverage or translating focal methods into test code. However, in contrast to branch coverage or code-to-test translation, practical tests are written out of the need to validate whether a requirement has been fulfilled. Specifically, each test usually reflects a developer's validation intention for a program function, regarding (1) what is the test scenario of a program function? and (2) what is expected behavior under such a scenario? Without taking such intention into account, generated tests are less likely to be adopted in practice. In this work, we propose IntentionTest, which generates project-specific tests given the description of validation intention. IntentionTest adopts a retrieval-and-edit manner. First, given a focal code and a description of validation intention consisting of a test objective with test precondition and expected results, IntentionTest retrieves a reusable test in the project as the test reference. Then, IntentionTest edits the test reference with an LLM regarding the validation intention toward the target test. We extensively evaluate IntentionTest against four baselines on 3,680 test cases. Compared to state-of-the-art baselines, IntentionTest can (1) generate tests far more semantically relevant to ground-truth tests by (i) killing 28.1