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Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases

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

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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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

Assessing the alignment between the information needs of developers and the documentation of programming languages: A case study on Rust

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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

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

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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

Practical Program Repair via Preference-based Ensemble Strategy

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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

关键词： Well testing

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

API Usage Recommendation via Multi-View Heterogeneous Graph Representation Learning

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arXiv 2022年

作者： Chen, Yujia Ren, Xiaoxue Gao, Cuiyun Peng, Yun Xia, Xin Lyu, Michael R. Harbin Institute of Technology Shenzhen China The Chinese University of Hong Kong Hong Kong Hong Kong Software Engineering Application Technology Lab Huawei China

Developers often need to decide which APIs to use for the functions being implemented. With the ever-growing number of APIs and libraries, it becomes increasingly difficult for developers to find appropriate APIs, indicating the necessity of automatic API usage recommendation. Previous studies adopt statistical models or collaborative filtering methods to mine the implicit API usage patterns for recommendation. However, they rely on the occurrence frequencies of APIs for mining usage patterns, thus prone to fail for the low-frequency APIs. Besides, prior studies generally regard the API call interaction graph as homogeneous graph, ignoring the rich information (e.g., edge types) in the structure graph. In this work, we propose a novel method named MEGA for improving the recommendation accuracy especially for the low-frequency APIs. Specifically, besides call interaction graph, MEGA considers another two new heterogeneous graphs: global API co-occurrence graph enriched with the API frequency information and hierarchical structure graph enriched with the project component information. With the three multi-view heterogeneous graphs, MEGA can capture the API usage patterns more accurately. Experiments on three Java benchmark datasets demonstrate that MEGA significantly outperforms the baseline models by at least 19% with respect to the Success Rate@1 metric. Especially, for the low-frequency APIs, MEGA also increases the baselines by at least 55% regarding the Success Rate@1 score. Copyright © 2022, The Authors. All rights reserved.

关键词： application programming interfaces (API)

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

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SeeHow: Workflow Extraction from Programming Screencasts thr...

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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