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arXiv 2022年

作者： Cogo, Filipe R. Xia, Xin Hassan, Ahmed E. Centre for Software Excellence HI Canada Software Engineering Application Technology Lab Huawei China School of Computing Queen's University Canada

Programming language documentation refers to the set of technical documents that provide application developers with a description of the high-level concepts of a language. Such documentation is essential to support application developers in the effective use of a programming language. One of the challenges faced by documenters (i.e., personnel that produce documentation) is to ensure that documentation has relevant information that aligns with the concrete needs of developers. In this paper, we present an automated approach to support documenters in evaluating the differences and similarities between the concrete information need of developers and the current state of documentation (a problem that we refer to as the topical alignment of a programming language documentation). Our approach leverages semi-supervised topic modelling to assess the similarities and differences between the topics of Q&A posts and the official documentation. To demonstrate the application of our approach, we perform a case study on the documentation of Rust. Our results show that there is a relatively high level of topical alignment in Rust documentation. Still, information about specific topics is scarce in both the Q&A websites and the documentation, particularly related topics with programming niches such as network, game, and database development. For other topics (e.g., related topics with language features such as structs, patterns and matchings, and foreign function interface), information is only available on Q&A websites while lacking in the official documentation. Finally, we discuss implications for programming language documenters, particularly how to leverage our approach to prioritize topics that should be added to the documentation. © 2022, CC BY-NC-SA.

关键词： Websites

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Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

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Digital Communications and Networks 2023年第1期9卷 125-137页

作者： Debasmita Mishra Bighnaraj Naik Janmenjoy Nayak Alireza Souri Pandit Byomakesha Dash S.Vimal Department of Computer Application Veer Surendra Sai University of TechnologyBurlaSambalpur768018OdishaIndia Department of Computer Science Maharaja Sriram Chandra Bhanja Deo(MSCB)UniversityBaripada757003OdishaIndia Department of Software Engineering HaliçUniversity34394IstanbulTurkey Department of Information Technology Aditya Institute of Technology and Management(AITAM)Tekkali532201Andhra PradeshIndia Data Analytics Lab Department of Artificial Intelligence and Data ScienceRamco Institute of TechnologyNorth Venganallur VillageRajapalayam 626117 Virudhunagar DistrictTamilnaduIndia

In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)*** followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected ***,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and *** outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.

关键词： IoT security Ensemble method Light gradient boosting machine Machine learning Intrusion detection

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Making Python Code Idiomatic by Automatic Refactoring Non-Idiomatic Python Code with Pythonic Idioms

arXiv

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arXiv 2022年

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

Compared to other programming languages (e.g., Java), Python has more idioms to make Python code concise and efficient. Although pythonic idioms are well accepted in the Python community, Python programmers are often faced with many challenges in using them, for example, being unaware of certain pythonic idioms or do not know how to use them properly. Based on an analysis of 7,638 Python repositories on GitHub, we find that non-idiomatic Python code that can be implemented with pythonic idioms occurs frequently and widely. Unfortunately, there is no tool for automatically refactoring such non-idiomatic code into idiomatic code. In this paper, we design and implement an automatic refactoring tool to make Python code idiomatic. We identify nine pythonic idioms by systematically contrasting the abstract syntax grammar of Python and Java. Then we define the syntactic patterns for detecting nonidiomatic code for each pythonic idiom. Finally, we devise atomic AST-rewriting operations and refactoring steps to refactor nonidiomatic code into idiomatic code. We test and review over 4,115 refactorings applied to 1,065 Python projects from GitHub, and submit 90 pull requests for the 90 randomly sampled refactorings to 84 projects. These evaluations confirm the high-accuracy, practicality and usefulness of our refactoring tool on real-world Python code. Our refactoring tool can be accessed at 47.242.131.128:5000. Copyright © 2022, The Authors. All rights reserved.

关键词： Python

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SeeHow: Workflow Extraction from Programming Screencasts through Action-Aware Video Analytics

SeeHow: Workflow Extraction from Programming Screencasts thr...

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International Conference on software engineering (ICSE)

作者： Dehai Zhao Zhenchang Xing Xin Xia Deheng Ye Xiwei Xu Liming Zhu Australian National University and CSIRO's data61 Australia Software Engineering Application Technology Lab Huawei China Tencent AI Lab China CSIRO's data61 Australia

Programming screencasts (e.g., video tutorials on Youtube or live coding stream on Twitch) are important knowledge source for developers to learn programming knowledge, especially the workflow of completing a programming task. Nonetheless, the image nature of programming screencasts limits the accessibility of screencast content and the workflow embedded in it, resulting in a gap to access and interact with the content and workflow in programming screencasts. Existing non-intrusive methods are limited to extract either primitive human-computer interaction (HCI) actions or coarse-grained video fragments. In this work, we leverage Computer Vision (CV) techniques to build a programming screencast analysis tool which can automatically extract code-line editing steps (enter text, delete text, edit text and select text) from screencasts. Given a programming screencast, our approach outputs a sequence of coding steps and code snippets involved in each step, which we refer to as programming workflow. The proposed method is evaluated on 41 hours of tutorial videos and live coding screencasts with diverse programming environments. The results demonstrate our tool can extract code-line editing steps accurately and the extracted workflow steps can be intuitively understood by developers.

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Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases

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

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IEEE International Conference on Automated software engineering (ASE)

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

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 $k$ NM-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 $k$ NM-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 $k$ NM-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.

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SeeHow: Workflow Extraction from Programming Screencasts through Action-Aware Video Analytics

arXiv

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arXiv 2023年

作者： Zhao, Dehai Xing, Zhenchang Xia, Xin Ye, Deheng Xu, Xiwei Zhu, Liming Australian National University CSIRO’s data61 Australia Tencent AI Lab China Software Engineering Application Technology Lab Huawei China CSIRO’s data61 Australia

关键词： Extraction

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Practical Program Repair via Preference-based Ensemble Strategy

arXiv

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arXiv 2023年

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

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

关键词： Well testing

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

arXiv

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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

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Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases

arXiv

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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

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SuperDetector: A Framework for Performance Detection on Vulnerabilities of Smart Contracts 6

SuperDetector: A Framework for Performance Detection on Vuln...

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2022 6th International Conference on Data Mining, Communications and Information technology, DMCIT 2022

作者： Dai, Meiyi Yang, Zhe Guo, Jian Software/Hardware Co-design Technology and Application Engineering Research Center East China Normal University China Shanghai Key Lab for Trustworthy Computing East China Normal University China

The technology of Ethereum blockchain enables the implementation of smart contracts. Nowadays smart contracts are one of the most successful applications of blockchain technology, which are widely used in many fields, such as finance, energy and services. The decentralization and immutability properties of Ethereum blockchain provide security for transactions from smart contracts. However, these properties are possible to lead to unfixable vulnerabilities of smart contracts. In recent years, vulnerability detection on smart contracts has attracted more attention from researchers and many related tools have emerged. Nevertheless, the existing vulnerability detection tools have not yet been put into formal use. Due to the lack of suitable smart contract sets and fair metrics with other factors, it remains complex work to conduct authoritative performance tests on these tools. In this paper, we first summarize eight common vulnerabilities of smart contracts, and then divide them into call-related and call-irrelated vulnerabilities according to whether they involve caller functions or not. In addition, we propose a detection framework called SuperDetector, which combines a variety of vulnerability detection tools based on static analysis technology. And the framework mainly measures the detection performance, which contains vulnerability coverage, detection accuracy, usability and execution time on the two types of vulnerabilities. The results indicate that the framework can analyze the performance from different perspectives, and the performance of vulnerability coverage and detection accuracy on call-related vulnerabilities is much better than that on call-irrelated vulnerabilities. Finally, we design a selector based on the detection results in the framework for improving the vulnerability coverage and detection accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Smart contract

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