Recent advances in multi-agent Large Language Model-based code generation enable collaborative software development through role-specialized agents. However, failure localization of code generation remains challenging due to inter-agent dependencies and solution-path multiplicity. Consequently, existing prompting-based localization methods exhibit vulnerability towards semantically valid but non-canonical strategies. To address this, we propose FLKR (Failure Localization via Knowledge-guided Reasoning), an self-supervised framework that combines behavior encoding, knowledge-strategy alignment, and consistency scoring for solution-path invariant localization. To evaluate, we also introduce COFL (Code Oriented Failure Localization), the first expert-annotated benchmark for fine-grained failure localization. Experiments show FLKR outperforms state-of-the-art prompting-based baselines by up to 14 points in Fault Localization Accuracy and 45 points in Top-1 accuracy, with strong performance in divergent, real-world, and refinement-critical cases. Such results demonstrate that our proposed FLKR generalizes well to real-world software development scenarios and opens up a new direction for failure-aware refinement recommendation by providing precise and interpretable responsibility signals.

The World Wide Web thrives on intelligent services that rely on accurate time series classification, which has recently witnessed significant progress driven by advances in deep learning. However, existing studies face challenges in domain incremental learning. In this paper, we propose a lightweight and robust dual-causal disentanglement framework (DualCD) to enhance the robustness of models under domain incremental scenarios, which can be seamlessly integrated into time series classification models. Specifically, DualCD first introduces a temporal feature disentanglement module to capture class-causal features and spurious features. The causal features can offer sufficient predictive power to support the classifier in domain incremental learning settings. To accurately capture these causal features, we further design a dual-causal intervention mechanism to eliminate the influence of both intra-class and inter-class confounding features. This mechanism constructs variant samples by combining the current class's causal features with intra-class spurious features and with causal features from other classes. The causal intervention loss encourages the model to accurately predict the labels of these variant samples based solely on the causal features. Extensive experiments on multiple datasets and models demonstrate that DualCD effectively improves performance in domain incremental scenarios. We summarize our rich experiments into a comprehensive benchmark to facilitate research in domain incremental time series classification.

Although Transformers excel in natural language processing, their extension to time series forecasting remains challenging due to insufficient consideration of the differences between textual and temporal modalities. In this paper, we develop a novel Transformer architecture designed for time series data, aiming to maximize its representational capacity. We identify two key but often overlooked characteristics of time series: (1) unidirectional influence from the past to the future, and (2) the phenomenon of decaying influence over time. These characteristics are introduced to enhance the attention mechanism of Transformers. We propose TimeFormer, whose core innovation is a self-attention mechanism with two modulation terms (MoSA), designed to capture these temporal priors of time series under the constraints of the Hawkes process and causal masking. Additionally, TimeFormer introduces a framework based on multi-scale and subsequence analysis to capture semantic dependencies at different temporal scales, enriching the temporal dependencies. Extensive experiments conducted on multiple real-world datasets show that TimeFormer significantly outperforms state-of-the-art methods, achieving up to a 7.45% reduction in MSE compared to the best baseline and setting new benchmarks on 94.04% of evaluation metrics. Moreover, we demonstrate that the MoSA mechanism can be broadly applied to enhance the performance of other Transformer-based models.

Stock trend prediction involves forecasting the future price movements by analyzing historical data and various market indicators. With the advancement of machine learning, graph neural networks (GNNs) have been extensively employed in stock prediction due to their powerful capability to capture spatiotemporal dependencies of stocks. However, despite the efforts of various GNN stock predictors to enhance predictive performance, the improvements remain limited, as they focus solely on analyzing historical spatiotemporal dependencies, overlooking the correlation between historical and future patterns. In this study, we propose a novel distillation-based future-aware GNN framework (DishFT-GNN) for stock trend prediction. Specifically, DishFT-GNN trains a teacher model and a student model, iteratively. The teacher model learns to capture the correlation between distribution shifts of historical and future data, which is then utilized as intermediate supervision to guide the student model to learn future-aware spatiotemporal embeddings for accurate prediction. Through extensive experiments on two real-world datasets, we verify the state-of-the-art performance of DishFT-GNN.

Standing out as one of the most widely used tools in Cross-Domain Few-Shot Learning (CDFSL), data augmentation forms the bedrock of numerous recent advancements. However, the current augmentations in CDFSL are limited in their ability to modify high-level semantic attributes, resulting in a lack of diversity along key semantic dimensions. One of the most promising tools to edit images with key semantic attributes, e.g. backgrounds, is image-to-image generation via large multimodal models (LMMs). Given the promising image editing results of recent LMMs, we delve into leveraging LMMs to augment data diversity for CDFSL. We propose a novel method named, Multimodal Few-shot Image Editing (MFIE), which uses LMMs to automatically translate class-specific images into class-agnostic natural language descriptions for various key semantic attributes in target domains and editing origin images based on class-agnostic natural language descriptions. To filter out corrupted data that disturbs the class-specific information, we apply semantic filtering using image-language similarity. Experiments on Meta-Datset show that MFIE surpasses SOTA CDFSL algorithms.

Code comment generation aims at generating natural language descriptions for a code snippet to facilitate developers' program comprehension activities. Despite being studied for a long time, a bottleneck for existing approaches is that given a code snippet, they can only generate one comment while developers usually need to know information from diverse perspectives such as what is the functionality of this code snippet and how to use it. To tackle this limitation, this study empirically investigates the feasibility of utilizing large language models (LLMs) to generate comments that can fulfill developers' diverse intents. Our intuition is based on the facts that (1) the code and its pairwise comment are used during the pre-training process of LLMs to build the semantic connection between the natural language and programming language, and (2) comments in the real-world projects, which are collected for the pre-training, usually contain different developers' intents. We thus postulate that the LLMs can already understand the code from different perspectives after the pre-training. Indeed, experiments on two large-scale datasets demonstrate the rationale of our insights: by adopting the in-context learning paradigm and giving adequate prompts to the LLM (e.g., providing it with ten or more examples), the LLM can significantly outperform a state-of-the-art supervised learning approach on generating comments with multiple intents. Results also show that customized strategies for constructing the prompts and post-processing strategies for reranking the results can both boost the LLM's performances, which shed light on future research directions for using LLMs to achieve comment generation.

Code search, which consists in retrieving relevant code snippets from a codebase based on a given query, provides developers with useful references during software development. Over the years, techniques alternatively adopting different mechanisms to compute the relevance score between a query and a code snippet have been proposed to advance the state of the art in this domain, including those relying on information retrieval, supervised learning, and pre-training. Despite that, the usefulness of existing techniques is still compromised since they cannot effectively handle all the diversified queries and code in practice. To tackle this challenge, we presentDancer, a data fusion based code searcher. Our intuition (also the basic hypothesis of this study) is that existing techniques may complement each other because of the intrinsic differences in their working mechanisms. We have validated this hypothesis via an exploratory study. Based on that, we propose to fuse the results generated by different code search techniques so that the advantage of each standalone technique can be fully leveraged. Specifically, we treat each technique as a retrieval system and leverage well-known data fusion approaches to aggregate the results from different systems. We evaluate six existing code search techniques on two large-scale datasets, and exploit eight classic data fusion approaches to incorporate their results. Our experiments show that the best fusion approach is able to outperform the standalone techniques by 35% - 550% and 65% - 825% in terms of MRR (mean reciprocal rank) on the two datasets, respectively.

This paper explores Deployment Authorization (DPA) as a means of restricting the generalization capabilities of vision models on certain domains to protect intellectual property. Nevertheless, the current advancements in DPA are predominantly confined to fully supervised settings. Such settings require the accessibility of annotated images from any unauthorized domain, rendering the DPA approaches impractical for real-world applications due to its exorbitant costs.To address this issue, we propose Source-Only Deployment Authorization (SDPA), which assumes that only authorized domains are accessible during training phases, and the model’s performance on unauthorized domains must be suppressed in inference stages. Drawing inspiration from distributional robust statistics, we present a lightweight method called Domain-Specified Optimization (DSO) for SDPA that degrades the model’s generalization over a divergence ball. DSO comes with theoretical guarantees on the convergence property and its authorization performance. As a complementary of SDPA, we also propose Target-Combined Deployment Authorization (TPDA), where unauthorized domains are partially accessible, and simplify the DSO method to a perturbation operation on the pseudo predictions, referred to as Target-Dependent Domain-Specified Optimization (TDSO). We demonstrate the effectiveness of our proposed DSO and TDSO methods through extensive experiments on six image benchmarks, achieving dominant performance on both SDPA and TDPA settings.

Code comment, i.e., the natural language text to describe the semantic of a code snippet, is an important way for developers to comprehend the code. Recently, a number of approaches have been proposed to automatically generate the comment given a code snippet, aiming at facilitating the comprehension activities of developers. Despite that state-of-the-art approaches have already utilized advanced machine learning techniques such as the Transformer model, they often ignore critical information of the source code, leading to the inaccuracy of the generated summarization. In this paper, to boost the effectiveness of code summarization, we propose a two-stage paradigm, where in the first stage, we train an off-the-shelf model and then identify its focuses when generating the initial summarization, through a model interpretation approach, and in the second stage, we reinforce the model to generate more qualified summarization based on the source code and its focuses. Our intuition is that in such a manner the model could learn to identify what critical information in the code has been captured and what has been missed in its initial summarization, and thus revise its initial summarization accordingly, just like how a human student learns to write high-quality summarization for a natural language text. Extensive experiments on two large-scale datasets show that our approach can boost the effectiveness of five state-of-the-art code summarization approaches significantly. Specifically, for the well-known code summarizer, DeepCom, utilizing our two-stage paradigm can increase its BLEU-4 values by around 30% and 25% on the two datasets, respectively.

In causal inference, it is common to select a subset of observed covariates, named the adjustment features, to be adjusted for estimating the treatment effect. For real-world applications, the abundant covariates are usually observed, which contain extra variables partially correlating to the treatment (treatment-only variables, e.g., instrumental variables) or the outcome (outcome-only variables, e.g., precision variables) besides the confounders (variables that affect both the treatment and outcome). In principle, unbiased treatment effect estimation is achieved once the adjustment features contain all the confounders. However, the performance of empirical estimations varies a lot with different extra variables. To solve this issue, variable separation/selection for treatment effect estimation has received growing attention when the extra variables contain instrumental variables and precision variables.

A longstanding dream in software engineering research is to devise effective approaches for automating development tasks based on developers' informally-specified intentions. Such intentions are generally in the form of natural language descriptions. In recent literature, a number of approaches have been proposed to automate tasks such as code search and even code generation based on natural language inputs. While these approaches vary in terms of technical designs, their objective is the same: transforming a developer's intention into source code. The literature, however, lacks a comprehensive understanding towards the effectiveness of existing techniques as well as their complementarity to each other. We propose to fill this gap through a large-scale empirical study where we systematically evaluate natural language to code techniques. Specifically, we consider six state-of-the-art techniques targeting code search, and four targeting code generation. Through extensive evaluations on a dataset of 22K+ natural language queries, our study reveals the following major findings: (1) code search techniques based on model pre-training are so far the most effective while code generation techniques can also provide promising results; (2) complementarity widely exists among the existing techniques; and (3) combining the ten techniques together can enhance the performance for 35% compared with the most effective standalone technique. Finally, we propose a post-processing strategy to automatically integrate different techniques based on their generated code. Experimental results show that our devised strategy is both effective and extensible.

With the rapid increase of public code repositories, developers maintain a great desire to retrieve precise code snippets by using natural language. Despite existing deep learning-based approaches that provide end-to-end solutions (i.e., accept natural language as queries and show related code fragments), the performance of code search in the large-scale repositories is still low in accuracy because of the code representation (e.g., AST) and modeling (e.g., directly fusing features in the attention stage). In this paper, we propose a novel learnable de ep G raph for C ode S earch (called deGraphCS ) to transfer source code into variable-based flow graphs based on an intermediate representation technique, which can model code semantics more precisely than directly processing the code as text or using the syntax tree representation. Furthermore, we propose a graph optimization mechanism to refine the code representation and apply an improved gated graph neural network to model variable-based flow graphs. To evaluate the effectiveness of deGraphCS , we collect a large-scale dataset from GitHub containing 41,152 code snippets written in the C language and reproduce several typical deep code search methods for comparison. The experimental results show that deGraphCS can achieve state-of-the-art performance and accurately retrieve code snippets satisfying the needs of the users.

Code comment, i.e., the natural language text to describe code, is considered as a killer for program comprehension. Current literature approaches mainly focus on comment generation or comment update, and thus fall short on explaining which part of the code leads to a specific content in the comment. In this paper, we propose that addressing such a challenge can better facilitate code understanding. We propose Fosterer, which can build fine-grained semantic interactions between code statements and comment tokens. It not only leverages the advanced deep learning techniques like cross-modal learning and contrastive learning, but also borrows the weapon of pre-trained vision models. Specifically, it mimics the comprehension practice of developers, treating code statements as image patches and comments as texts, and uses contrastive learning to match the semantically-related part between the visual and textual information. Experiments on a large-scale manually-labelled dataset show that our approach can achieve an F1-score around 80%, and such a performance exceeds a heuristic-based baseline to a large extent. We also find that Fosterer can work with a high efficiency, i.e., it only needs 1.5 seconds for inferring the results for a code-comment pair. Furthermore, a user study demonstrates its usability: for 65% cases, its prediction results are considered as useful for improving code understanding. Therefore, our research sheds light on a promising direction for program comprehension.

Domain adaptation aims at learning a predictive model that can generalize to a new target domain different from the source (training) domain. To mitigate the domain gap, adversarial training has been developed to learn domain invariant representations. State-of-the-art methods further make use of pseudo labels generated by the source domain classifier to match conditional feature distributions between the source and target domains. However, if the target domain is more complex than the source domain, the pseudo labels are unreliable to characterize the class-conditional structure of the target domain data, undermining prediction performance. To resolve this issue, we propose a Pairwise Similarity Regularization (PSR) approach that exploits cluster structures of the target domain data and minimizes the divergence between the pairwise similarity of clustering partition and that of pseudo predictions. Therefore, PSR guarantees that two target instances in the same cluster have the same class prediction and thus eliminate the negative effect of unreliable pseudo labels. Extensive experimental results show that our PSR method significantly boosts the current adversarial domain adaptation methods by a large margin on four visual benchmarks. In particular, PSR achieves a remarkable improvement of more than 5% over the state-of-the-art on several hard-to-transfer tasks.

Following trails in the wild is an essential capability of out-door autonomous mobile robots. Recently, deep learningbased approaches have made great advancements in this field. However, the existing research only focuses on the trail following with a single robot. In contrast, many robotic tasks in the reality, such as search and patrolling, are conducted by a group of robots. While these robots are grouped to move in the wild, they can cooperate to significantly promote the trail following accuracy, for example, by sharing images of different view angles or real-time decision fusion. This paper proposes such an approach named DL-Cooper that enables multi-robot visionbased trail following based on deep learning algorithms. It allows each robot to make a decision respectively with deep neural network and then fusion the decisions on the collective level with the support of back-end cloud computing infrastructure. It also takes Quality of Service (QoS) assurance, a very essential property of robotic software, into consideration. By limiting the condition to fusion decisions, the time latency can be minimally sacrificed. Experiments on the real-world dataset show that our approach has significantly improved the accuracy of the singlerobot system.

In China, the member of student in a class ranges from 40 to 100. How to manage the physical learning space is really critical to learning in 21st century. Does changing the physical learning space really change learning experience? In this research, we want to find out What differences in classroom layout are identified in original versus active classrooms and What positive impact on learning experience is observed in traditional and collaborative classrooms. After data analysis, we identified the view, interaction with teacher and mobility is has impact on learning in different layout.