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检索条件"任意字段=19th International Conference on Software Engineering and Knowledge Engineering"

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Crash Deduplication using Hybrid Runtime Features and Multi-Structure Neural Network 36

Crash Deduplication using Hybrid Runtime Features and Multi-...

引用

36th international conference on software engineering and knowledge engineering, SEKE 2024

作者： Xu, Hang Ji, Jiangan Peng, Jianshan Chen, Liheng Key Laboratory of Cyberspace Security Ministry of Education China Institute for Network Sciences and Cyberspace Tsinghua University China

ISBN: (纸本)1891706594

Fuzzing, widely used across the industry, is a dynamic method to detect vulnerabilities by generating test cases causing program crashes. However, its random mutation often produces duplicate Proof-of-Concept (PoC) test cases, which can be frustrating for researchers. Modern fuzzing methods use execution feedback to deduplicate PoCs, termed crash deduplication. However, inaccurate crash deduplication may miss unique PoCs or increase the analysis workload. Existing approaches rely on limited metrics or models, thus impacting accuracy. We propose a data-driven approach, which leverages hybrid runtime features for crash deduplication. We specifically extract hybrid runtime features, including runtime register, stack, and heap operation features, using the Magma benchmark and a customized dynamic binary translator to construct a training dataset. We also introduce a Multi-Structure Neural Network (MSNN) model to handle hybrid runtime features and optimize it using contrastive learning. Experimental results show our prototype significantly outperforms mainstream industry crash deduplication methods, excelling in both deduplication rate and bug retention. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： software testing

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How does project context matter in enhancing the capabilities of code summarization models? 36

How does project context matter in enhancing the capabilitie...

引用

36th international conference on software engineering and knowledge engineering, SEKE 2024

作者： Li, Jiuli Liu, Yan School of Software Engineering Tongji University Shanghai China

ISBN: (纸本)1891706594

High-quality code summaries are vital for improving code understanding, reuse, and maintenance efficiency. With the rise of deep learning, neural networks have become a hot topic in code summarization. Utilizing lexical and structural code information enhances summary quality, but often relies solely on the source code. Recent efforts to improve semantic understanding through additional randomly selected context have faced challenges of model interference. this paper introduces the CE-GCS method, incorporating a denoising strategy to remove dataset noise and redundant information. It leverages project-level context based on calling and dependency rules to enrich code representation. the GA-FCSM model, built on the Transformer architecture, utilizes multi-head attention to capture project-level context, integrating textual token sequence and AST structure information by fusion network to improve the performance of summary generation. Evaluating CE-GCS using BLEU, ROUGE, and METEOR, the paper demonstrates the importance of project-level context in enhancing semantic understanding. GA-FCSM outperforms baseline models that use various input combinations. Ablation experiments further highlight the contribution of textual tokens, AST, and context, while confirming the benefits of the fusion network and copy mechanism. the proposed context fusion index (CSF, CNF, CCF) offers new insights into the role of project-level context in code summarization. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Semantics

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An Empirical Comparison of Pre-Trained Models of Source Code 23

An Empirical Comparison of Pre-Trained Models of Source Code

引用

45th IEEE/ACM international conference on software engineering (ICSE)

作者： Niu, Changan Li, Chuanyi Ng, Vincent Chen, Dongxiao Ge, Jidong Luo, Bin Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Univ Texas Dallas Human Language Technol Res Inst Richardson TX 75080 USA

ISBN: (纸本)9781665457019

While a large number of pre-trained models of source code have been successfully developed and applied to a variety of software engineering (SE) tasks in recent years, our understanding of these pre-trained models is arguably fairly limited. With the goal of advancing our understanding of these models, we perform the first systematic empirical comparison of 19 recently-developed pre-trained models of source code on 13 SE tasks. To gain additional insights into these models, we adopt a recently-developed 4-dimensional categorization of pre-trained models, and subsequently investigate whether there are correlations between different categories of pre-trained models and their performances on different SE tasks.

关键词： Pre-training of Source Code AI for SE

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An LLM-based Multi-stage Approach for Automated Test Case Generation from User Stories 36

An LLM-based Multi-stage Approach for Automated Test Case Ge...

引用

36th international conference on software engineering and knowledge engineering, SEKE 2024

作者： Zhou, Qixiang Li, Jiaxuan Sun, Jiahong Li, Tong Beijing University of Technology Beijing China

ISBN: (纸本)1891706594

Requirements-driven testing is crucial for validating functionality and maintaining software quality during the early stages of development. It is usually done manually by experienced experts, which is time-consuming and thus affects development efficiency. Although a number of approaches have been proposed to automate the generation of test cases, they primarily rely on (semi-)formalized requirements and thus cannot be applied to user stories and other natural language-based requirements specifications that are more prevalent in practice. In this paper, we propose an incrementally refined multi-stage method that utilizes LLM to generate test cases from user stories automatically. Specifically, we design a systematic process to incrementally refine user stories and transform them into test cases in multiple stages. We compare the consistency, reasonable, testability, coverage, and time consumption of test cases generated by our method to those designed by experts and find that our proposal outperforms baseline methods in terms of all metrics, achieving 4.8 points testability and 86.36% coverage compared to the baseline’s 4.5 points and 44.64%. this indicates a promising direction for improving the practicality and efficiency of test case generation from user stories. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： software testing

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knowledge-aware Alert Aggregation in Large-scale Cloud Systems: a Hybrid Approach 44

Knowledge-aware Alert Aggregation in Large-scale Cloud Syste...

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ACM/IEEE 46th international conference on software engineering - software engineering in Practice (ICSE-SEIP)

作者： Kuang, Jinxi Liu, Jinyang Huang, Junjie Zhong, Renyi Gu, Jiazhen Yu, Lan Tan, Rui Yang, Zengyin Lyu, Michael R. Chinese Univ Hong Kong Hong Kong Peoples R China Huawei Cloud Comp Technol Co Ltd Comp & Networking Innovat Lab Shenzhen Peoples R China

ISBN: (纸本)9798400702174

Due to the scale and complexity of cloud systems, a system failure would trigger an "alert storm", i.e., massive correlated alerts. Although these alerts can be traced back to a few root causes, the overwhelming number makes it infeasible for manual handling. Alert aggregation is thus critical to help engineers concentrate on the root cause and facilitate failure resolution. Existing methods typically utilize semantic similarity-based methods or statistical methods to aggregate alerts. However, semantic similarity-based methods overlook the causal rationale of alerts, while statistical methods can hardly handle infrequent alerts. To tackle these limitations, we introduce leveraging external knowledge, i.e., Standard Operation Procedure (SOP) of alerts as a supplement. We propose COLA, a novel hybrid approach based on correlation mining and LLM (Large Language Model) reasoning for online alert aggregation. the correlation mining module effectively captures the temporal and spatial relations between alerts, measuring their correlations in an efficient manner. Subsequently, only uncertain pairs with low confidence are forwarded to the LLM reasoning module for detailed analysis. this hybrid design harnesses both statistical evidence for frequent alerts and the reasoning capabilities of computationally intensive LLMs, ensuring the overall efficiency of COLA in handling large volumes of alerts in practical scenarios. We evaluate COLA on three datasets collected from the production environment of a large-scale cloud platform. the experimental results show COLA achieves F1-scores from 0.901 to 0.930, outperforming state-of-the-art methods and achieving comparable efficiency. We also share our experience in deploying COLA in our real-world cloud system, Cloud X-1.

关键词： Alert aggregation cloud systems software reliability

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Fuzzy Information Entropy and Region Biased Matrix Factorization for Web Service QoS Prediction 36

Fuzzy Information Entropy and Region Biased Matrix Factoriza...

引用

36th international conference on software engineering and knowledge engineering, SEKE 2024

作者： Tang, Guoxing Du, Yugen Chen, Xia Luo, Yingwei Ma, Benchi Software Engineering Institute East China Normal University Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China

ISBN: (纸本)1891706594

Nowadays, there are many similar services available on the internet, making Quality of Service (QoS) a key concern for users. Since collecting QoS values for all services through user invocations is impractical, predicting QoS values is a more feasible approach. Matrix factorization is considered an effective prediction method. However, most existing matrix factorization algorithms focus on capturing global similarities between users and services, overlooking the local similarities between users and their similar neighbors, as well as the non-interactive effects between users and services. this paper proposes a matrix factorization approach based on user information entropy and region bias, which utilizes a similarity measurement method based on fuzzy information entropy to identify similar neighbors of users. Simultaneously, it integrates the region bias between each user and service linearly into matrix factorization to capture the non-interactive features between users and services. this method demonstrates improved predictive performance in more realistic and complex network environments. Additionally, numerous experiments are conducted on real-world QoS datasets. the experimental results show that the proposed method outperforms some of the state-of-the-art methods in the field at matrix densities ranging from 5% to 20%. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Matrix factorization

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Decoding Code Quality: A software Metric Analysis of Open-Source JavaScript Projects 19

Decoding Code Quality: A Software Metric Analysis of Open-So...

引用

19th international conference on Evaluation of Novel Approaches to software engineering, ENASE 2024

作者： Mohammad, Suzad Al Jobair, Abdullah Abedeen, Iftekharul International University of Business Agriculture and Technology Dhaka Bangladesh United International University Dhaka Bangladesh

ISBN: (纸本)9789897586965

the popularity of web-based solutions has seen rapid growth in the last decade, which has raised the demand for JavaScript (JS) usage in personal projects and enterprise solutions. While the extensive demand for JS has elevated, studies have yet to be done on how JS development follows the rules and guides for writing code to meet quality standards. Consequently, we choose to investigate the practice of JS on different project sizes, the developers’ experience, and their impact on code quality and development. To achieve this goal, we perform the code quality analysis of 200 open-source JS projects from Github on 10 code quality metrics. We design our research study to examine the influence of project size on issue density, find relationships among 10 code metrics, how code quality changes with developer experience, and determine the capabilities of existing source code evaluation tools. Our findings reveal that issue density decreases with increasing developer experience and project size. In addition, our quantitative study suggests that with the increase in project size and line of code (LOC), project maintainability decreases, leading to more issues such as errors, complexity, code smell, and duplication. However, as developers become more experienced, they face fewer coding challenges, enhance code quality, and reduce code smell per line of code (LOC). Our study also offers valuable insights into the capabilities of the 6 tools mentioned above to advance code evaluation practices. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Open source software

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Emotions in Requirements engineering: A Systematic Mapping Study 16

Emotions in Requirements Engineering: A Systematic Mapping S...

引用

16th IEEE/ACM international conference on Cooperative and Human Aspects of software engineering (CHASE)

作者： Iqbal, Tahira Anwar, Hina Filzah, Syazwanie Gharib, Mohamad Mooses, Kerli Taveter, Kuldar Univ Tartu Inst Comp Sci Tartu Estonia

ISBN: (纸本)9798350337341

the purpose of requirements engineering (RE) is to make sure that the expectations and needs of the stakeholders of a software system are met. Emotional needs can be captured as emotional requirements that represent how the end user should feel when using the system. Differently from functional and quality (non-functional) requirements, emotional requirements have received relatively less attention from the RE community. this study is motivated by the need to explore and map the literature on emotional requirements. the study applies the systematic mapping study technique for surveying and analyzing the available literature to identify the most relevant publications on emotional requirements. We identified 34 publications that address a wide spectrum of practices concerned with engineering emotional requirements. the identified publications were analyzed with respect to the application domains, instruments used for eliciting and artefacts used for representing emotional requirements, and the state of the practice in emotion-related requirements engineering. this analysis serves to identify research gaps and research directions in engineering emotional requirements. To the best of the knowledge by the authors, no other similar study has been conducted on emotional requirements.

关键词： Requirements engineering Emotion Emotional Requirement Mapping Study

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CryptOpt: Automatic Optimization of Straightline Code 23

CryptOpt: Automatic Optimization of Straightline Code

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45th IEEE/ACM international conference on software engineering (ICSE)

作者： Kuepper, Joel Wu, David Erbsen, Andres Gross, Jason Conoly, Owen Sun, Chuyue Tian, Samuel Chlipala, Adam Chuengsatiansup, Chitchanok Genkin, Daniel Wagner, Markus Yarom, Yuval Univ Adelaide Adelaide SA Australia MIT Cambridge MA USA Stanford Univ Stanford CA USA Univ Melbourne Melbourne Vic Australia Georgia Tech Atlanta GA USA Monash Univ Clayton Vic Australia

ISBN: (纸本)9798350322637

Manual engineering of high-performance implementations typically consumes many resources and requires in-depth knowledge of the hardware. Compilers try to address these problems;however, they are limited by design in what they can do. To address this, we present CryptOpt, an automatic optimizer for long stretches of straightline code. Experimental results across eight hardware platforms show that CryptOpt achieves a speed-up factor of up to 2.56 over current off-the-shelf compilers.

关键词： Automatic Performance Optimization Search Based software engineering Local Search Elliptic Curve Cryptography

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A Fault Analysis and Reasoning Method for Vehicle Information Systems Based on knowledge Graphs 24

A Fault Analysis and Reasoning Method for Vehicle Informatio...

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24th IEEE international conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Sun, Tu Zeng, Fuping Liu, Xiaoze School of Reliability and System Engineering Beihang University Beijing China

ISBN: (纸本)9798350365658

In the information age, vehicle information systems are evolving with increased complexity. Traditional fault diagnosis methods struggle to keep pace with new software faults. Leveraging knowledge graphs, which can represent intricate connections and rules within language knowledge, offers a fresh approach. this paper introduces a comprehensive fault analysis and reasoning framework for vehicle information systems using knowledge graphs. By gathering internet data, conducting entity and relationship extraction, and constructing a knowledge graph, the model enables intelligent question-answering capabilities based on logical rule reasoning. Deep learning techniques, specifically the TransE model for knowledge representation learning, facilitate entity prediction, relationship prediction, and triple correctness verification. this framework aims to enhance fault diagnosis and maintenance guidance in vehicle information systems. © 2024 IEEE.

关键词： knowledge graph

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