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Context-aware API recommendation using tensor factorization

science China(Information sciences) 2023年第2期66卷 78-93页

作者： Yu ZHOU Chen CHEN Yongchao WANG Tingting HAN Taolue CHEN College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics State Key Laboratory for Novel Software Technology Nanjing University Birkbeck University of London

An activity constantly engaged by most programmers in coding is to search for appropriate application programming interfaces(APIs). Contextual information is widely recognized to play a crucial role in effective API recommendation, but it is largely overlooked in practice. In this paper, we propose contextaware API recommendation using tensor factorization(CARTF), a novel API recommendation approach in considering programmers' working context. To this end, we use tensors to explicitly represent the queryAPI-context triadic relation. When a new query is made, CARTF harnesses word embeddings to retrieve similar user queries, based on which a third-order tensor is constructed. CARTF then applies non-negative tensor factorization to complete missing values in the tensor and the Smith-Waterman algorithm to identify the most matched context. Finally, the ranking of the candidate APIs can be derived based on which API sequences are recommended. Our evaluation confirms the effectiveness of CARTF for class-level and method-level API recommendations, outperforming state-of-the-art baseline approaches against a number of performance metrics, including SuccessRate, Precision, and Recall.

关键词： API recommendation tensor factorization context awareness word embedding intelligent software development

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Deep learning-based software engineering: progress,challenges, and opportunities

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science China(Information sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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How far are we to GPT-4V?Closing the gap to commercial multimodal models with open-source suites

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science China(Information sciences) 2024年第12期67卷 5-22页

作者： Zhe CHEN Weiyun WANG Hao TIAN Shenglong YE Zhangwei GAO Erfei CUI Wenwen TONG Kongzhi HU Jiapeng LUO Zheng MA Ji MA Jiaqi WANG Xiaoyi DONG Hang YAN Hewei GUO Conghui HE Botian SHI Zhenjiang JIN Chao XU Bin WANG Xingjian WEI Wei LI Wenjian ZHANG Bo ZHANG Pinlong CAI Licheng WEN Xiangchao YAN Min DOU Lewei LU Xizhou ZHU Tong LU Dahua LIN Yu QIAO Jifeng DAI Wenhai WANG State Key Laboratory for Novel Software Technology Nanjing University Shanghai AI Laboratory School of Computer Science Fudan University SenseTime Research Department of Information Engineering The Chinese University of Hong Kong Department of Electronic Engineering Tsinghua University

In this paper, we introduce InternVL 1.5, an open-source multimodal large language model(MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements.(1) Strong vision encoder: we explored a continuous learning strategy for the large-scale vision foundation model — InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs.(2) Dynamic high-resolution: we divide images into tiles ranging from 1 to 40 of 448×448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input.(3) High-quality bilingual dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images,and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in optical character recognition(OCR) and Chinese-related tasks. We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary commercial models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 multimodal benchmarks. Code and models are available at https://***/OpenGVLab/InternVL.

关键词： multimodal model open-source vision encoder dynamic resolution bilingual dataset

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Optimizing Monitoring Utility of Unmanned Aerial Vehicles Considering Adverse Effects

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5996-6013页

作者： Zhang, Haihan Dai, Haipeng Qiu, Yu Yu, Enze Zhou, Ruiben Wang, Weijun Wang, Jingwu Chen, Guihai Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China South China University of Technology School of Computer Science and Engineering Guangzhou China Beijing China

For Unmanned Aerial Vehicles (UAVs) monitoring tasks, capturing high quality images of target objects is important for subsequent recognition. Concerning the problem, many prior works study placement/trajectory planning for UAVs to maximize the quality of captured images. However, all of them overlook a fact that UAV monitoring may cause a huge risk/annoyance on living objects. In this paper, we investigate the novel problem of oPtimizing unmanned aErial vehicles plAcement by Considering both monitoring utility and adverse Effects (PEACE). We propose an approach to solve PEACE, which is proved to be NP-hard. Overall, our approach achieves a 1-1/e- approximation ratio. First, we approximate the original problem of PEACE as a classical problem of Monotone Submodular function Maximization under a Uniform Matroid constraint (MSMUM) with a controlled gap. Then, for MSMUM, we propose a combination of algorithms achieving a 1-1/e approximation and O(n\log n) time complexity considering the correlation among the UAV monitoring strategies. The proposed algorithms outperform existing algorithms for MSMUM through theoretical analysis and experimental results. Extensive simulations and field experiments demonstrate the effectiveness of our approach, achieving performance gains of 9.0% to 1434.5% compared to existing methods. © 2002-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Multi-agent policy transfer via task relationship modeling

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science China(Information sciences) 2024年第8期67卷 102-114页

作者： Rongjun QIN Feng CHEN Tonghan WANG Lei YUAN Xiaoran WU Yipeng KANG Zongzhang ZHANG Chongjie ZHANG Yang YU National Key Laboratory for Novel Software Technology Nanjing University Polixir Technologies School of Engineering and Applied Sciences Harvard University Department of Computer Science and Technology Tsinghua University Institute for Interdisciplinary Information Sciences Tsinghua University

Team adaptation to new cooperative tasks is a hallmark of human intelligence, which has yet to be fully realized in learning agents. Previous studies on multi-agent transfer learning have accommodated teams of different sizes but heavily relied on the generalization ability of neural networks for adapting to unseen tasks. We posit that the relationship among tasks provides key information for policy *** utilize this relationship for efficient transfer by attempting to discover and exploit the knowledge among tasks from different teams, proposing to learn an effect-based task representation as a common latent space among tasks, and using it to build an alternatively fixed training scheme. Herein, we demonstrate that task representation can capture the relationship among teams and generalize to unseen tasks. Thus, the proposed method helps transfer the learned cooperation knowledge to new tasks after training on a few source ***, the learned transferred policies help solve tasks that are difficult to learn from scratch.

关键词： multi-agent reinforcement learning cooperative transfer learning task relationship modeling multi-agent policy reuse multi-agent multi-task learning

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A Combinatorial Interaction Testing Method for Multi-Label Image Classifier 35

A Combinatorial Interaction Testing Method for Multi-Label I...

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35th IEEE International Symposium on software Reliability Engineering, ISSRE 2024

作者： Wang, Peng Hu, Shengyou Wu, Huayao Niu, Xintao Nie, Changhai Chen, Lin Nanjing University State Key Laboratory for Novel Software Technology and School of Computer Science China

ISBN: (纸本)9798350353884

Multi-label image classification is a critical task in computer vision, in which the correlations between labels are typically exploited by modern classifiers for an effective classification. In this study, we propose LV-CIT, a black-box testing method that applies Combinatorial Interaction Testing (CIT) to systematically test the ability of classifiers to handle such correlations. Specifically, LV-CIT views each label of the label space as an input-parameter taking binary values (indicating whether an object appears in an image), and manages to generate a label value covering array as the set of test cases to cover certain combinations of label values. Then, for each test case, LV-CIT relies on an object library to generate composite test images that perfectly match the specified labels, and reports classification errors if such labels cannot be correctly recognised. The experimental results on two popular datasets with six state-of-The-Art image classifiers show that LV-CIT is more efficient than the existing CIT tools in generating label value covering arrays. LV-CIT is also effective in errors revelation, as it can find 111% more errors by using 20% fewer test images than the existing methods for testing multi-label image classifiers. © 2024 IEEE.

关键词： Black-box testing

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Empirically revisiting and enhancing automatic classification of bug and non-bug issues

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Frontiers of computer science 2024年第5期18卷 25-44页

作者： Zhong LI Minxue PAN Yu PEI Tian ZHANG Linzhang WANG Xuandong LI State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Department of Computer Science and Technology Nanjing UniversityNanjing 210023China Software Institute Nanjing UniversityNanjing 210093China Department of Computing The Hong Kong Polytechnic UniversityHong KongChina

A large body of research effort has been dedicated to automated issue classification for Issue Tracking Systems(ITSs).Although the existing approaches have shown promising performance,the different design choices,including the different textual fields,feature representation methods and machine learning algorithms adopted by existing approaches,have not been comprehensively compared and *** fill this gap,we perform the first extensive study of automated issue classification on 9 state-of-the-art issue classification *** experimental results on the widely studied dataset reveal multiple practical guidelines for automated issue classification,including:(1)Training separate models for the issue titles and descriptions and then combining these two models tend to achieve better performance for issue classification;(2)Word embedding with Long Short-Term Memory(LSTM)can better extract features from the textual fields in the issues,and hence,lead to better issue classification models;(3)There exist certain terms in the textual fields that are helpful for building more discriminating classifiers between bug and non-bug issues;(4)The performance of the issue classification model is not sensitive to the choices of ML *** on our study outcomes,we further propose an advanced issue classification approach,DEEPLABEL,which can achieve better performance compared with the existing issue classification approaches.

关键词： issue tracking issue type prediction empirical study

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ON THE EFFECT OF BATCH SIZE IN BYZANTINE-ROBUST DISTRIBUTED LEARNING 12

ON THE EFFECT OF BATCH SIZE IN BYZANTINE-ROBUST DISTRIBUTED ...

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12th International Conference on Learning Representations, ICLR 2024

作者： Yang, Yi-Rui Shi, Chang-Wei Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

Byzantine-robust distributed learning (BRDL), in which computing devices are likely to behave abnormally due to accidental failures or malicious attacks, has recently become a hot research topic. However, even in the independent and identically distributed (i.i.d.) case, existing BRDL methods will suffer a significant drop on model accuracy due to the large variance of stochastic gradients. Increasing batch size is a simple yet effective way to reduce the variance. However, when the total number of gradient computation is fixed, a too-large batch size will lead to a too-small iteration number (update number), which may also degrade the model accuracy. In view of this challenge, we mainly study the effect of batch size when the total number of gradient computation is fixed in this work. In particular, we show that when the total number of gradient computation is fixed, the optimal batch size corresponding to the tightest theoretical upper bound in BRDL increases with the fraction of Byzantine workers. Therefore, compared to the case without attacks, a larger batch size is preferred when under Byzantine attacks. Motivated by the theoretical finding, we propose a novel method called Byzantine-robust stochastic gradient descent with normalized momentum (ByzSGDnm) in order to further increase model accuracy in BRDL. We theoretically prove the convergence of ByzSGDnm for general non-convex cases under Byzantine attacks. Empirical results show that when under Byzantine attacks, using a relatively large batch size can significantly increase the model accuracy, which is consistent with our theoretical results. Moreover, ByzSGDnm can achieve higher model accuracy than existing BRDL methods when under deliberately crafted attacks. In addition, we empirically show that increasing batch size has the bonus of training acceleration. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Stochastic systems

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Question Selection for Multi-Modal Code Search Synthesis using Probabilistic Version Spaces

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IEEE Transactions on software Engineering 2025年第6期51卷 1724-1744页

作者： Wu, Jiarong Jiang, Yanyan Wei, Lili Xu, Congying Cheung, Shing-Chi Xu, Chang The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong McGill University Department of Electrical and Computer Engineering Montreal Canada Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing China

Searching the occurrences of specific code patterns (code search) is a common task in software engineering, and programming by example (PBE) techniques have been applied to ease customizing code patterns. However, previous PBE tools only synthesize programs meeting the input-output examples, which may not always align with the user intent. To bridge this gap, this paper proposes Excalibur, a multi-modal (example and natural language description) and interactive synthesizer for code search. Excalibur ensures that the generated programs are correct for the provided examples (soundness) and include the user-intended program (bounded completeness). Furthermore, Excalibur helps the user identify the user-intended program through question-answer interaction. To minimize the required interaction efforts, question selection is crucial. To improve question selection for code search, we propose probabilistic version spaces (ProbVS), in which the user-intended program’s probability is high and others are low. ProbVS combines traditional version spaces for compactly representing extensive programs and large language models (on the user-provided natural language description) for adjusting programs’ probabilities to align with users’ intents. Extensive experiments on a benchmark of 44 tasks demonstrated the effectiveness of Excalibur and ProbVS and demystified how ProbVS affects probability distributions and how the configurable parameters affect ProbVS. © 1976-2012 IEEE.

关键词： Normal distribution

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XRL-SHAP-Cache:an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

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science China(Information sciences) 2024年第7期67卷 46-71页

作者： Xiaolong XU Fan WU Muhammad BILAL Xiaoyu XIA Wanchun DOU Lina YAO Weiyi ZHONG School of Software Nanjing University of Information Science and Technology School of Computing and Communications Lancaster University School of Computing Technologies Royal Melbourne Institute of Technology State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering University of New South Wales Data 61 Commonwealth Scientific and Industrial Research Organization School of Computer Science Qufu Normal University

Content delivery networks(CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions. As an essential component of CDNs, the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers. With the emergence of 5G technology, traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments. Consequently, deep reinforcement learning(DRL) offers a promising solution for intelligent zero-touch network governance. However, the blackbox nature of DRL models poses challenges in understanding and making trusting decisions. In this paper,we propose an explainable reinforcement learning(XRL)-based intelligent edge service caching approach,namely XRL-SHAP-Cache, which combines DRL with an explainable artificial intelligence(XAI) technique for cache management in CDNs. Instead of focusing solely on achieving performance gains, this study introduces a novel paradigm for providing interpretable caching strategies, thereby establishing a foundation for future transparent and trustworthy edge caching solutions. Specifically, a multi-level cache scheduling framework for CDNs was formulated theoretically, with the D3QN-based caching scheme serving as the targeted interpretable model. Subsequently, by integrating Deep-SHAP into our framework, the contribution of each state input feature to the agent's Q-value output was calculated, thereby providing valuable insights into the decision-making process. The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental *** results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio, quality of service(QoS),a

关键词： deep reinforcement learning (DRL) explainable artificial intelligence (XAI) multi-level cache content delivery network (CDN) D3QN algorithm Deep-SHAP

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