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Research Square 2021年

作者： Shi, Congming Wang, Wen Wei, Shoulin Lv, Feiya School of Software Engineering Anyang Normal University China Faculty of Information Engineering and Automation Kunming University of Science and Technology China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology China

With the evolution of 5G technology, the telecom industry has influenced the livelihood of people and impacted the development of the national economy significantly. To increase revenue per customer and secure long-term contracts of users, telecommunications firms and enterprises have launched different types of telecom packages to satisfy the varying requirements of users. Although several recommender systems have been proposed in recent years for telecommunication package recommendation, extracting effective feature information from large and complex consumption data remains challenging. Considering the telecom package recommendation problems, traditional recommendation methods either use complex expert feature engineering or fail to perform end-to-end deep learning training. In this study, a recommender system based on deep & cross network (DCN), deep belief network (DBN), Embedding, and Word2Vec is proposed using the powerful learning abilities of deep learning. The proposed system fits the telecom package recommender system in terms of click-through rate prediction to provide a potential solution for the recommendation challenges faced by telecom enterprises. The proposed model can effectively capture the finite order interactional features and deep hidden features. Additionally, the text information in the data is completely used to further improve the recommendation ability of the model. Moreover, the proposed method does not require feature engineering. We conducted comprehensive experiments using real-world datasets, and the results verify that our method can generate improved recommendation accuracy in comparison with those observed in DBN, DCN, deep factorization machine, and deep neural network models individually. © 2021, CC BY.

关键词： Recommender systems

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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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Practical Program Repair via Preference-based Ensemble Strategy

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

引用

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

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

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Code Structure Guided Transformer for Source Code Summarization

arXiv

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arXiv 2021年

作者： Gao, Shuzheng Gao, Cuiyun He, Yulan Zeng, Jichuan Nie, Lun Yiu Xia, Xin Lyu, Michael R. Harbin Institute of Technology Shenzhen China University of Warwick United Kingdom The Chinese University of Hong Kong Hong Kong Tsinghua University China Software Engineering Application Technology Lab Huawei China

Code summaries help developers comprehend programs and reduce their time to infer the program functionalities during software maintenance. Recent efforts resort to deep learning techniques such as sequence-to-sequence models for generating accurate code summaries, among which Transformer-based approaches have achieved promising performance. However, effectively integrating the code structure information into the Transformer is under-explored in this task domain. In this paper, we propose a novel approach named SG-Trans to incorporate code structural properties into Transformer. Specifically, we inject the local symbolic information (e.g., code tokens and statements) and global syntactic structure (e.g., data flow graph) into the self-attention module of Transformer as inductive bias. To further capture the hierarchical characteristics of code, the local information and global structure are designed to distribute in the attention heads of lower layers and high layers of Transformer. Extensive evaluation shows the superior performance of SG-Trans over the state-of-the-art approaches. Compared with the best-performing baseline, SG-Trans still improves 1.4% and 2.0% in terms of METEOR score, a metric widely used for measuring generation quality, respectively on two benchmark datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jia Cui Weiping Wang Dan Meng Zhenyan Liu University of Chinese Academy of Sciences Beijing China National Engineering Lab of Info-Security Technology Institute of Information Engineering CAS School of Software Beijing Institute of Technology Beijing China Center of Computer Application Research Institute of Computing Technology CAS

ISBN: (纸本)9781479976164

Similarity join plays an important role in many applications, such as data cleaning and integration, to address the poor data quality problem. Most of the existing studies focused on performing similarity join on static datasets but few studies realized running it on dynamic data streams. With the development of network technology, the data accessing paradigm has transferred from disk-oriented mode to online data streams, which makes performing similarity join in continuous query on data streams become a novel query processing paradigm. Different from static dataset, data stream is unbounded, continuous and unpredictable. The significant differences pose serious challenges, such as real-time query performance. To this end, we study the problem of continuous similarity join on data streams in this paper, which is based on edit distance metric and filter-and-verify framework with sliding-window semantics. Two subcases of this problem are studied, including self similarity join on a single data stream and similarity join on two streams. We introduced the basic window based sliding window model to facilitate the update of sliding window and its index. More details of our method, including signature extraction schemes, filtering and verification algorithms, re-evaluation strategies are discussed respectively. Finally, extensive experimental results show that our method works efficiently on real data streams.

关键词： Filtering algorithms Filtering Query processing Indexing Heuristic algorithms Real-time systems

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

arXiv

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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

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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.

关键词：

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

arXiv

引用

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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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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PS3: Precise Patch Presence Test based on Semantic Symbolic Signature

arXiv

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arXiv 2023年

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

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 PS3, referring to precise patch presence test based on semantic-level symbolic signature. PS3 exploits symbolic emulation to extract signatures that are stable under different compiler options. Then PS3 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 PS3 achieves scores of 0.82, 0.97, and 0.89 in terms of precision, recall, and F1 score, respectively. PS3 outperforms the state-of-the-art baselines by improving 33% in terms of F1 score and remains stable in different compiler options. © 2023, CC BY.

关键词： software design

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