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International Conference on software engineering (ICSE)

作者： Zejun Zhang Zhenchang Xing Xin Xia Xiwei Xu Liming Zhu Qinghua Lu Australian National University Australia Data61 CSIRO Australia Software Engineering Application Technology Lab Huawei China

The usage of Python idioms is popular among Python developers in a formative study of 101 Python idiom performance related questions on Stack Overflow, we find that developers often get confused about the performance impact of Python idioms and use anecdotal toy code or rely on personal project experience which is often contradictory in performance outcomes. There has been no large-scale, systematic empirical evidence to reconcile these performance debates. In the paper, we create a large synthetic dataset with 24,126 pairs of non-idiomatic and functionally-equivalent idiomatic code for the nine unique Python idioms identified in [1], and reuse a large real-project dataset of 54,879 such code pairs provided in [1]. We develop a reliable performance measurement method to compare the speedup or slowdown by idiomatic code against non-idiomatic counterpart, and analyze the performance discrepancies between the synthetic and real-project code, the relationships between code features and performance changes, and the root causes of performance changes at the bytecode level. We summarize our findings as some actionable suggestions for using Python idioms.

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StandUp4NPR: Standardizing SetUp for Empirically Comparing Neural Program Repair Systems 22

StandUp4NPR: Standardizing SetUp for Empirically Comparing N...

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Proceedings of the 37th IEEE/ACM International Conference on Automated software engineering

作者： Wenkang Zhong Hongliang Ge Hongfei Ai Chuanyi Li Kui Liu Jidong Ge Bin Luo State Key Laboratory for Novel Software and Technology Nanjing University 22 Hankou Road Nanjing China China Huawei Software Engineering Application Technology Lab China

ISBN: (纸本)9781450394758

Recently, the emerging trend in automatic program repair is to apply deep neural networks to generate fixed code from buggy ones, called NPR (Neural Program Repair). However, the existing NPR systems are trained and evaluated under very different settings (e.g., different training data, inconsistent evaluation data, wide-ranged candidate numbers), which makes it hard to draw fair-enough conclusions when comparing them. Motivated by this, we first build a standard benchmark dataset and an extensive framework tool to mitigate threats for the comparison. The dataset consists of a training set, a validation set and an evaluation set with 144,641, 13,739 and 13,706 bug-fix pairs of Java respectively. The tool supports selecting specific training, validation, and evaluation datasets and automatically conducting the pipeline of training and evaluating NPR models, as well as easily integrating new NPR models by implementing well-defined interfaces. Then, based on the benchmark and tool, we conduct a comprehensive empirical comparison of six SOTA NPR systems w.r.t the repairability, inclination and generalizability. The experimental results reveal deeper characteristics of compared NPR systems and subvert some existing comparative conclusions, which further verify the necessity of unifying the experimental setups in exploring the progresses of NPR systems. Meanwhile, we reveal some common features of NPR systems (e.g., they are good at dealing with code-delete bugs). Finally, we identify some promising research directions derived from our findings.

关键词： neural program repair empirical study dataset

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Identify and Update Test Cases When Production Code Changes: A Transformer-Based Approach

Identify and Update Test Cases When Production Code Changes:...

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IEEE International Conference on Automated software engineering (ASE)

作者： Xing Hu Zhuang Liu Xin Xia Zhongxin Liu Tongtong Xu Xiaohu Yang School of Software Technology Zhejiang University Ningbo China College of Computer Science and Technology Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei Hangzhou China

software testing is one of the most essential parts of the software lifecycle and requires a substantial amount of time and effort. During the software evolution, test cases should co-evolve with the production code. However, the co-evolution of test cases often fails due to tight project schedules and other reasons. Obsolete test cases improve the cost of software maintenance and may fail to reveal faults and even lead to future bugs. Therefore, it is essential to detect and update these obsolete test cases in time. In this paper, we propose a novel approach Ceprot (Co-Evolution of Production-Test Code) to identify outdated test cases and update them automatically according to changes in the production code. Ceprot consists of two stages, i.e., obsolete test identification and updating. Specifically, given a production code change and a corresponding test case, Ceprot first identifies whether the test case should be updated. If the test is identified as obsolete, Ceprot will update it to a new version of test case. To evaluate the effectiveness of the two stages, we construct two datasets. Our dataset focuses on method-level production code changes and updates on their obsolete test cases. The experimental results show that Ceprot can effectively identify obsolete test cases with precision and recall of 98.3% and 90.0%, respectively. In addition, test cases generated by Ceprot are identical to the ground truth for 12.3% of samples that are identified as obsolete by Ceprot. We also conduct dynamic evaluation and human evaluation to measure the effectiveness of the updated test cases by Ceprot. 48.0% of updated test cases can be compiled and the average coverage of updated cases is 34.2% which achieves 89% coverage improvement over the obsolete tests. We believe that this study can motivate the co-evolution of production and test code.

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Image super‐resolution via dynamic network

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CAAI Transactions on Intelligence technology 2024年第4期9卷 837-849页

作者： Chunwei Tian Xuanyu Zhang Qi Zhang Mingming Yang Zhaojie Ju School of Software Northwestern Polytechnical UniversityXi'anChina National Engineering Laboratory for Integrated Aero‐Space‐Ground‐Ocean Big Data Application Technology Xi'anChina Research&Development Institute Northwestern Polytechnical UniversityShenzhenChina School of Economics and Management Harbin Institute of Technology at WeihaiWeihaiChina Tencent AI Lab ShenzhenChina School of Computing University of PortsmouthPortsmouthUK

Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐***,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction *** residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐*** enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying *** prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained ***,a residual learning operation is embedded in the refinement block to prevent long‐term dependency ***,a construction block is responsible for reconstructing high‐quality *** heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital *** results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and *** code of DSRNet can be obtained at https://***/hellloxiaotian/DSRNet.

关键词： CNN dynamic network image super‐resolution lightweight network

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Introducing on-chain graph data to consortium blockchain for commercial transactions

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Frontiers of Computer Science 2024年第2期18卷 255-257页

作者： Pengyi ZHANG Yuchen YUAN Jie SONG Yu GU Qiang QU Yongjie BAI Software College Northeastern UniversityShenyang 110819China School of Computer Science and Engineering Northeastern UniversityShenyang 110819China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Blockchain Lab Huawei Cloud Computing Technologies Co.Ltd.Shenzhen 518101China

1 Introduction A blockchain can be referred to as a distributed ledger[1].Nowadays,according to our previous work[2],the graphrelated blockchain grows efficiently in both academic studies and commercial *** data,algor... 详细信息

关键词： transactions ledge commercial

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Fast Point-to-Surface Projection for Large CAD Models and Quality Testing 3

Fast Point-to-Surface Projection for Large CAD Models and Qu...

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3rd International Conference on Optics and Image Processing, ICOIP 2023

作者： Liu, Wu Bao, Ziheng Yang, Chunhui Liu, Yusheng Wu, Lei Fifth Electronic Research Institute of Ministry of Industry and Information Technology MIIT Key Lab of Industrial Software Engineering Application Technology Guangzhou510610 China State Key Lab of CAD&CG Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781510667426

Point projection for Non-Uniform Rational B-Splines (NURBS) surfaces is a fundamental operator in Computer-Aided modeling (CAD) modeling. This operator takes as input a query point and a NURBS surface in 3, and outputs the UV parameter values whose corresponding 3D point (one on the surface) has the minimum distance between the query point and a variable point on the surface. Existing projection methods consistently employ an iterative searching strategy, which suffers from efficiency issues, especially for tasks involving enormous query points such as 3D CAD model quality testing. This paper proposes a parallel, subdivision-based strategy to increase query speed. It uses geometric subdivision to quickly cull out potential parameter regions where a query point’s corresponding UV parameters reside, then utilizes the Gauss-Newton method to quickly march to the precise UV values. All steps are done completely on GPU: the geometric subdivision is shared across all query points, and the Gauss-Newton marching is done in parallel as well. Experimental results show that a significant increase of at least 11x can be attained using the proposed method. © 2023 SPIE. All rights reserved.

关键词： 3D modeling

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Practical Program Repair via Preference-based Ensemble Strategy

Practical Program Repair via Preference-based Ensemble Strat...

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International Conference on software engineering (ICSE)

作者： Wenkang Zhong Chuanyi Li Kui Liu Tongtong Xu Jidong Ge Tegawendé F. Bissyandé Bin Luo Vincent Ng National Key Laboratory for Novel Software Technology Nanjing University China Nanjing China Huawei Software Engineering Application Technology Lab Hangzhou China University of Luxembourg Luxembourg Human Language Technology Research Institute University of Texas at Dallas Richardson Texas USA

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

To date, over 40 Automated Program Repair (APR) tools have been designed with varying bug-fixing strategies, which have been demonstrated to have complementary performance in terms of being effective for different bug classes. Intuitively, it should be feasible to improve the overall bug-fixing performance of APR via assembling existing tools. Unfortunately, simply invoking all available APR tools for a given bug can result in unacceptable costs on APR execution as well as on patch validation (via expensive testing). Therefore, while assembling existing tools is appealing, it requires an efficient strategy to reconcile the need to fix more bugs and the requirements for practicality. In light of this problem, we propose a Preference-based Ensemble Program Repair framework (P-EPR), which seeks to effectively rank APR tools for repairing different bugs. P-EPR is the first non-learning-based APR ensemble method that is novel in its exploitation of repair patterns as a major source of knowledge for ranking APR tools and its reliance on a dynamic update strategy that enables it to immediately exploit and benefit from newly derived repair results. Experimental results show that P-EPR outperforms existing strategies significantly both in flexibility and effectiveness.

关键词： Measurement Costs Computer bugs Maintenance engineering software Ensemble learning Testing

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Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases 23

Domain Adaptive Code Completion via Language Models and Deco...

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Proceedings of the 38th IEEE/ACM International Conference on Automated software engineering

作者： Ze Tang Jidong Ge Shangqing Liu Tingwei Zhu Tongtong Xu Liguo Huang Bin Luo National Key Laboratory for Novel Software Technology Nanjing University China Nanyang Technological University Singapore Huawei Software Engineering Application Technology China Department of Computer Science Southern Methodist University USA

ISBN: (纸本)9798350329964

Large Language Models (LLMs) have demonstrated remarkable performance in code completion. However, due to the lack of domain-specific knowledge, they may not be optimal in completing code that requires intensive domain knowledge for example completing the library names. Although there are several works that have confirmed the effectiveness of fine-tuning techniques to adapt language models for code completion in specific domains. They are limited by the need for constant fine-tuning of the model when the project is in constant *** address this limitation, in this paper, we propose kNM-LM, a retrieval-augmented language model (R-LM), that integrates domain knowledge into language models without fine-tuning. Different from previous techniques, our approach is able to automatically adapt to different language models and domains. Specifically, it utilizes the in-domain code to build the retrieval-based database decoupled from LM, and then combines it with LM through Bayesian inference to complete the code. The extensive experiments on the completion of intra-project and intra-scenario have confirmed that kNM-LM brings about appreciable enhancements when compared to CodeGPT and UnixCoder. A deep analysis of our tool including the responding speed, storage usage, specific type code completion, and API invocation completion has confirmed that kNM-LM provides satisfactory performance, which renders it highly appropriate for domain adaptive code completion. Furthermore, our approach operates without the requirement for direct access to the language model's parameters. As a result, it can seamlessly integrate with black-box code completion models, making it easy to integrate our approach as a plugin to further enhance the performance of these models.

关键词： domain adaptive code completion

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Multi-view Transformer-Based Network for Prerequisite Learning in Concept Graphs 23rd

Multi-view Transformer-Based Network for Prerequisite Learn...

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23rd International Semantic Web Conference, ISWC 2024

作者： Wang, Zhichun Shao, Yifeng Peng, Boci Li, Bangui Li, Yun Wang, Qianren Li, Nijun School of Artificial Intelligence Beijing Normal University Beijing China Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing China School of Intelligence Science and Technology Peking University Beijing China School of Computer Science and Engineering Beihang University Beijing China Advanced Cognitive AI Lab Shanghai Huawei Technologies Shanghai China

ISBN: (纸本)9783031778438

Prerequisite learning is the task of identifying prerequisite relations among concepts, which is important for many AI-based educational applications. Previous approaches explore different kinds of learning resources to obtain useful features for predicting prerequisites. Early approaches use handcrafted features while recent approaches use neural networks to encode information of concepts. To further improve the results of prerequisite learning, we build concept graphs to include comprehensive information about concepts from open data and learning resources, and propose a Multi-view Transformer-based Network (MTN) to encode multi-view features of concepts in the graph. Multi-view features are fused to make accurate predictions of concept prerequisites. We evaluate our approach on four public datasets and compare it with recently published approaches. The results show that our approach can achieve state-of-the-art results in the task of prerequisite learning. The source code and data are available at https://***/kg-bnu/MTN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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PS3: Precise Patch Presence Test Based on Semantic Symbolic Signature

PS3: Precise Patch Presence Test Based on Semantic Symbolic ...

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International Conference on software engineering (ICSE)

作者： Qi Zhan Xing Hu Zhiyang Li Xin Xia David Lo Shanping Li The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei China Singapore Management University Singapore

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

During software development, vulnerabilities have posed a significant threat to users. Patches are the most effective way to combat vulnerabilities. In a large-scale software system, testing the presence of a security patch in every affected binary is crucial to ensure system security. Identifying whether a binary has been patched for a known vulnerability is challenging, as there may only be small differences between patched and vulnerable versions. Existing approaches mainly focus on detecting patches that are compiled in the same compiler options. However, it is common for developers to compile programs with very different compiler options in different situations, which causes inaccuracy for existing methods. In this paper, we propose a new approach named $PS^{3}$ , referring to precise patch presence test based on semantic-level symbolic signature. $PS^{3}$ exploits symbolic emulation to extract signatures that are stable under different compiler options. Then $PS^{3}$ can precisely test the presence of the patch by comparing the signatures between the reference and the target at semantic level. To evaluate the effectiveness of our approach, we constructed a dataset consisting of 3,631 (CVE, binary) pairs of 62 recent CVEs in four C/C++ projects. The experimental results show that $PS^{3}$ achieves scores of 0.82, 0.97, and 0.89 in terms of precision, recall, and F1 score, respectively. $PS^{3}$ outperforms the state-of-the-art baselines by improving 33% in terms of F1 score and remains stable in different compiler options.

关键词： Program processors Semantics Emulation software systems Security Testing software engineering

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