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How are We Detecting Inconsistent Method Names? An Empirical Study from Code Review Perspective

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arXiv 2023年

作者： Kim, Kisub Zhou, Xin Kim, Dongsun Lawall, Julia Liu, Kui Bissyandé, Tegawendé F. Klein, Jacques Lee, Jaekwon Lo, David Singapore Management University Singapore Kyungpook National University Korea Republic of Inria France Huawei Software Engineering Application Technology Lab China University of Luxembourg Luxembourg University of Ottawa Canada

Proper naming of methods can make program code easier to understand, and thus enhance software maintainability. Yet, developers may use inconsistent names due to poor communication or a lack of familiarity with conventions within the software development lifecycle. To address this issue, much research effort has been invested into building automatic tools that can check for method name inconsistency and recommend consistent names. However, existing datasets generally do not provide precise details about why a method name was deemed improper and required to be changed. Such information can give useful hints on how to improve the recommendation of adequate method names. Accordingly, we construct a sample method-naming benchmark, ReName4J, by matching name changes with code reviews. We then present an empirical study on how state-of-the-art techniques perform in detecting or recommending consistent and inconsistent method names based on ReName4J. The main purpose of the study is to reveal a different perspective based on reviewed names rather than proposing a complete benchmark. We find that the existing techniques underperform on our review-driven benchmark, both in inconsistent checking and the recommendation. We further identify potential biases in the evaluation of existing techniques, which future research should consider thoroughly. © 2023, CC BY-SA.

关键词： Life cycle

Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases

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arXiv 2023年

作者： Tang, Ze Ge, Jidong Liu, Shangqing Zhu, Tingwei Xu, Tongtong Huang, Liguo Luo, Bin 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 United States

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 iteration. To 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. Copyright © 2023, The Authors. All rights reserved.

关键词： Domain Knowledge

PS3: Precise Patch Presence Test Based on Semantic Symbolic Signature

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

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

Clean Code in Practice: Challenges and Opportunities

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SSRN

SSRN 2023年

作者： Yan, Dapeng Liu, Kui Wan, Zhiyuan Liu, Zhiming Bissyandé, Tegawendé F. Nanjing University of Aeronautics and Astronautics Nanjing China Huawei Software Engineering Application Technology Lab Hangzhou China Zhejiang University Hangzhou China Southwest University Chongqing China University of Luxembourg Luxembourg City Luxembourg

Clean code generally refers to the property of code that can be easily understood and evolved by any developer in the team and beyond. With the increasing scale and requirements of software, clean code is becoming an ultimate target of maintainers. Unfortunately, the definition of clean code does not have clear constraints, which challenges research around the topic. Our work provides a broad understanding of clean code in practice. To this end, we start by re-visiting the definition of clean code, looking back at how similar concepts have evolved in the literature. We then explore a developer community forum and investigate the usual indicators of function sizes among practitioners. Subsequently, we apply typical metrics related to code readability to open source projects led by software development giants and examine whether their code evolves towards clean code. Our empirical results reveal that clean code baselines in clean code conventions can guide developers for better clean code practices. We also provide measurable and referable baselines for clean code practice from the aspects of function sizes, file sizes, and code line lengths. Finally, we summarize the challenges and opportunities of the current implementation of clean code through a questionnaire responded by industry practitioners. © 2023, The Authors. All rights reserved.

关键词： Open systems

Parallel implementation of dense optical flow computation on many-core processor 1

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15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Chen, Wenjie Yu, Jin Zhang, Weihua Jiang, Linhua Zhang, Guanhua Chai, Zhilei MoE Engineering Research Center for Software/Hardware Co-design Technology and Application East China Normal University Shanghai200061 China School of IoT Engineering Jiangnan University Wuxi214122 China Parallel Processing Institute Fudan University Shanghai200433 China Shanghai Key Lab of Modern Optical Systems University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (数字)9783319271194

ISBN: (纸本)9783319271187

Computation of optical flow is a fundamental step in computer vision applications. However, due to its high complexity, it is difficult to compute a high-accuracy optical flow field in real time. This paper proposes a parallel computing approach for fast computation of high-accuracy optical flow field. It is specially designed for Tilera, a typical many-core processor with 36 tiles. By efficiently exploiting the advantages of the mesh architecture of Tilera, and by appropriately handling the parallelism inherent in the optical flow computation, the proposed implemention is able to significantly reduce the computation time while keep a low power consumption. Experiment shows that, for a 640×480 image, the computation time is only 0. 80 seconds per frame. It is 2. 56 times faster than on a typical CPU i3-3240 (3. 4GHz), and the power consumption as less as 1/6. Experimental results also show that the proposed parallel approach is highly scalable for variable requirements on computation speeds and power consumptions, since it can flexibly selects a proper number of computing cores. © Springer International Publishing Switzerland 2015.

关键词： Parallel processing systems

Multi-Granularity Detector for Vulnerability Fixes

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arXiv 2023年

作者： Nguyen, Truong Giang Le-Cong, Thanh Kang, Hong Jin Widyasari, Ratnadira Yang, Chengran Zhao, Zhipeng Xu, Bowen Zhou, Jiayuan Xia, Xin Hassan, Ahmed E. Le, Xuan-Bach D. Lo, David The School of Computing and Information Systems Singapore Management University Singapore The Software Engineering Application Technology Lab Huawei China School of Computing Queen’s University Canada School of Computing and Information Systems The University of Melbourne Australia

With the increasing reliance on Open Source software, users are exposed to third-party library vulnerabilities. software Composition Analysis (SCA) tools have been created to alert users of such vulnerabilities. SCA requires the identification of vulnerability-fixing commits. Prior works have proposed methods that can automatically identify such vulnerability-fixing commits. However, identifying such commits is highly challenging, as only a very small minority of commits are vulnerability fixing. Moreover, code changes can be noisy and difficult to analyze. We observe that noise can occur at different levels of detail, making it challenging to detect vulnerability fixes accurately. To address these challenges and boost the effectiveness of prior works, we propose MiDas (Multi-Granularity Detector for Vulnerability Fixes). Unique from prior works, MiDas constructs different neural networks for each level of code change granularity, corresponding to commit-level, file-level, hunk-level, and line-level, following their natural organization. It then utilizes an ensemble model that combines all base models to generate the final prediction. This design allows MiDas to better handle the noisy and highly imbalanced nature of vulnerability-fixing commit data. Additionally, to reduce the human effort required to inspect code changes, we have designed an effort-aware adjustment for MiDas’s outputs based on commit length. The evaluation results demonstrate that MiDas outperforms the current state-of-the-art baseline in terms of AUC by 4.9% and 13.7% on Java and Python-based datasets, respectively. Furthermore, in terms of two effort-aware metrics, EffortCost@L and Popt@L, MiDas also outperforms the state-of-the-art baseline, achieving improvements of up to 28.2% and 15.9% on Java, and 60% and 51.4% on Python, respectively. © 2023, CC BY.

关键词： Python

Are We Building on the Rock? On the Importance of Data Preprocessing for Code Summarization

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arXiv 2022年

作者： Shi, Lin Mu, Fangwen Chen, Xiao Wang, Song Wang, Junjie Yang, Ye Li, Ge Xia, Xin Wang, Qing Institute of Software Chinese Academy of Sciences Beijing China Lassonde School of Engineering York University Toronto Canada School of Systems and Enterprises Stevens Institute of Technology HobokenNJ United States Key Lab of High Confidence Software Technology Peking University Beijing China Software Engineering Application Technology Lab Huawei China Laboratory for Internet Software Technologies Institute of Software CAS China University of Chinese Academy of Sciences China Science & Technology on Integrated Information System Laboratory Institute of Software CAS China

Code summarization, the task of generating useful comments given the code, has long been of interest. Most of the existing code summarization models are trained and validated on widely-used code comment benchmark datasets. However, little is known about the quality of the benchmark datasets built from real-world projects. Are the benchmark datasets as good as expected? To bridge the gap, we conduct a systematic research to assess and improve the quality of four benchmark datasets widely used for code summarization tasks. First, we propose an automated code-comment cleaning tool that can accurately detect noisy data caused by inappropriate data preprocessing operations from existing benchmark datasets. Then, we apply the tool to further assess the data quality of the four benchmark datasets, based on the detected noises. Finally, we conduct comparative experiments to investigate the impact of noisy data on the performance of code summarization models. The results show that these data preprocessing noises widely exist in all four benchmark datasets, and removing these noisy data leads to a significant improvement on the performance of code summarization. We believe that the findings and insights will enable a better understanding of data quality in code summarization tasks, and pave the way for relevant research and practice. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

Preventing "bad" content dispersal in Named Data Networking 17

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Preventing "bad" content dispersal in Named Data Networking

50th ACM Turing Conference - China, ACM TUR-C 2017

作者： Wang, Yi Qi, Zhuyun Lei, Kai Liu, Bin Tian, Chen Huawei Future Network Theory Lab Hong Kong School of Electronics and Computer Engineering Peking University Shenzhen518055 China Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing100084 China State Key Laboratory for Novel Software Technology Nanjing University Najing210023 China

ISBN: (纸本)9781450348737

Named Data Networking (NDN) improves the data delivery efficiency by caching contents in routers. To prevent corrupted and faked contents be spread in the network, NDN routers should verify the digital signature of each published content. Since the verification scheme in NDN applies the asymmetric encryption algorithm to sign contents, the speed of content verification is too slow to satisfy the high speed requirement. In this paper, we propose two schemes to improve the verification performance of NDN routers to prevent content poisoning. The first content verification scheme, called "user-assisted", leads to the best performance, but can be bypassed if the clients and the content producer collude. To prevent the aforementioned collusion attack, we improve the user-assisted content verification scheme and propose the second content verification scheme, named "Router-Cooperation", in which the edge routers verify the contents independently without the assistance of users and the core routers no longer verify the contents. The Router-Cooperation verification scheme reduces the computing complexity of cryptographic operation by replacing the asymmetric encryption algorithm with symmetric encryption algorithm. The simulation results demonstrate that this Router-Cooperation scheme can speed up 145.5× (in hardware) and 18.85× (in software) of the original content verification scheme with merely extra transmission overhead. © 2017 ACM.

关键词： Verification

Defect Identification, Categorization, and Repair: Better Together

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arXiv 2022年

作者： Ni, Chao Yang, Kaiwen Xia, Xin Lo, David Chen, Xiang Yang, Xiaohu School of Software Technology Zhejiang University Zhejiang Hangzhou310007 China College of Computer Science and Technology Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei Hangzhou China Singapore Management University Singapore School of Information Science and Technology Nantong University Nantong China Computer Science and Technology Zhejiang University Hangzhou China

Just-In-Time defect prediction (JIT-DP) models can identify defect-inducing commits at check-in time. Even though previous studies have achieved a great progress, these studies still have the following limitations: 1) useful information (e.g., semantic information and structure information) are not fully used;2) existing work can only predict a commit as buggy one or clean one without more information about what type of defect it is;3) a commit may involve changes in many files, which cause difficulty in locating the defect;4) prior studies treat defect identification and defect repair as separate tasks, none aims to handle both tasks simultaneously. In this paper, to handle aforementioned limitations, we propose a comprehensive defect prediction and repair framework named CompDefect, which can identify whether a changed function (a more fine-grained level) is defect-prone, categorize the type of defect, and repair such a defect automatically if it falls into several scenarios, e.g., defects with single statement fixes, or those that match a small set of defect templates. Generally, the first two tasks in CompDefect are treated as a multiclass classification task, while the last one is treated as a sequence generation task. The whole input of CompDefect consists of three parts (exampled with positive functions): the clean version of a function (i.e., the version before defect introduced), the buggy version of a function and the fixed version of a function. In multiclass classification task, CompDefect categorizes the type of defect via multiclass classification with the information in both the clean version and the buggy version. In code sequence generation task, CompDefect repairs the defect once identified or keeps it unchanged. To verify the effectiveness of CompDefect, we first build a large-scale function-level dataset named Function-SStuBs4J, which totally contains 21,047 instances with three versions of modified functions and we then evaluate CompDefect with

关键词： Just in time production