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Research on Script-based software Component Development 18th

Research on Script-Based Software Component Development

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18th International conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Zhao, Yingnan Sun, Yang Fan, Bin Lu, Dan College of Computer Science and Technology Harbin Engineering University Harbin150009 China Harbin Institute of Technology Software Engineering Co. Ltd. Harbin China

ISBN: (纸本)9789819998920

As software demand proliferates and software size and complexity increase, traditional software development models face enormous challenges. As a result, new software development techniques are being explored to meet the requirements of software development. The development of new software development techniques has begun to meet the requirements of software development. based on many years of experience in object-oriented and component-oriented software design methods, we propose an innovative four-factor scripted analysis and design method for state transfer using the knowledge of computer compilation principles on finite state machine (FSM) circuit module automation. It provides a methodological and empirical reference for programmers in the design and development of application systems using dynamic languages. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer software reusability

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Revisiting Learning-based Commit Message Generation 23

Revisiting Learning-based Commit Message Generation

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

作者： Dong, Jinhao Lou, Yiling Hao, Dan Tan, Lin Peking Univ Key Lab High Confidence Software Technol Sch Comp Sci Beijing Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China Purdue Univ Dept Comp Sci W Lafayette IN USA

ISBN: (纸本)9781665457019

Commit messages summarize code changes and help developers understand the intention. To alleviate human efforts in writing commit messages, researchers have proposed various automated commit message generation techniques, among which learning-based techniques have achieved great success in recent years. However, existing evaluation on learning-based commit message generation relies on the automatic metrics (e.g., BLEU) widely used in natural language processing (NLP) tasks, which are aggregated scores calculated based on the similarity between generated commit messages and the ground truth. Therefore, it remains unclear what generated commit messages look like and what kind of commit messages could be precisely generated by existing learning-based techniques. To fill this knowledge gap, this work performs the first study to systematically investigate the detailed commit messages generated by learning-based techniques. In particular, we first investigate the frequent patterns of the commit messages generated by state-of-the-art learning-based techniques. Surprisingly, we find the majority (similar to 90%) of their generated commit messages belong to simple patterns (i.e., addition/removal/fix/avoidance patterns). To further explore the reasons, we then study the impact of datasets, input representations, and model components. We surprisingly find that existing learning-based techniques have competitive performance even when the inputs are only represented by change marks (i.e., "+"/"-"/" "). It indicates that existing learning-based techniques poorly utilize syntax and semantics in the code while mostly focusing on change marks, which could be the major reason for generating so many pattern-matching commit messages. We also find that the pattern ratio in the training set might also positively affect the pattern ratio of generated commit messages;and model components might have different impact on the pattern ratio.

关键词： Commit Message Generation Deep Learning Pattern-based

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A Graph-based Data Mining Approach for Template Recognition using Large Log Datasets in software Systems 21

A Graph-based Data Mining Approach for Template Recognition ...

引用

21st International conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

作者： Julio, Claudia Crespo Ithwaja, Ahmed S. Woungang, Isaac Anpalagan, Alagan Toronto Metropolitan Univ Dept Elect & Comp Engn Toronto ON Canada Toronto Metropolitan Univ Dept Comp Sci Toronto ON Canada

ISBN: (纸本)9798350381566;9798350381559

Log files generated by software systems can be utilized as a valuable resource in data-driven approaches to improve the system health and stability. These files often contain valuable information about runtime execution, and their effective monitoring requires analyzing an increasingly large volume of data logs. In this paper, a graph mining technique for log parsing is presented, which is source agnostic to the system. This means that the technique can function regardless of the source of the logs, making it more scalable and reusable. Unlike the existing approaches that rely heavily on domain knowledge and regular expression patterns, the proposed approach uses graph models and semantic analysis to detect data patterns with minimal user input. This makes it easy to implement it in a variety of scenarios where application-based logs may differ significantly. The proposed parsing technique is evaluated over seven datasets. It achieves the best performance on the Thunderbird dataset, where the technique takes 3.87 seconds for 2000 logs, while obtaining precision, recall and F1 measure higher than 0.99.

关键词： Data mining template recognition log datasets graph theory semantic analysis

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Unmasking Data Secrets: An Empirical Investigation into Data Smells and Their Impact on Data Quality 24

Unmasking Data Secrets: An Empirical Investigation into Data...

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IEEE/ACM 3rd International conference on AI engineering - software engineering for AI (CAIN)

作者： Recupito, Gilberto Rapacciuolo, Raimondo Di Nucci, Dario Palomba, Fabio Univ Salerno Sesa Lab Salerno Italy Univ Salerno Salerno Italy

ISBN: (纸本)9798400705915

Artificial Intelligence (AI) is rapidly advancing with a data-centered approach suitable for various domains. Nevertheless, AI faces significant challenges, particularly in data quality. Data collection from diverse sources can introduce quality issues that may threaten the development of AI-enabled systems. A growing concern in this context is the emergence of data smells - issues specific to the data used in building AI models, which can have long-term consequences. In this paper, we aim at enlarging the current body of knowledge on data smells, by proposing a two-step investigation into the matter. First, we updated an existing literature review in an effort of cataloguing the currently existing data smells and the tools to detect them. Afterward, we assess the prevalence of data smells and their correlation with data quality metrics. We identify a novel set composed of 12 data smells distributed across three additional categories. Secondly, we observe that the correlation between data smells and data quality is notably impactful, exhibiting a pronounced and substantial effect, especially in highly diffused data smell instances. This research sheds light on the complex relationship between data smells and data quality, providing valuable insights into the challenges of maintaining AI-enabled systems.

关键词： AI Technical Debt Data Smells Data Quality software engineering for Artificial Intelligence Empirical software engineering

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Research on Directed Grey-box Fuzzing Technology based on Target 35

Research on Directed Grey-box Fuzzing Technology Based on Ta...

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35th International conference on software engineering and knowledge engineering, SEKE 2023

作者： Sun, Liang Wu, Peng Shu, Shaoxian National University of Defense Technology Changsha China Hunan Institute of Traffic Engineering Hengyang China

In recent years, grey-box fuzzing has been proven to be the most effective method for discovering vulnerabilities in software. However, the present grey-box fuzzing still has some shortcomings. Most existing grey-box fuzzers are coverage guided and consider the program code equally, and spend a lot of time on improving the code coverage. However, most of the code in the program does not contain bugs and only a small percentage of the code may have bugs. Therefore, blindly improving code coverage can waste limited resources on a large number of bug independent locations and reduce the efficiency of fuzzing. In order to solve the above problems, we propose a targeted mutation strategy for continuous target exploration. By identifying the key bytes in the input seeds, a mutation algorithm for different stages of mutation is proposed to ensure that the subsequent generation of seeds can still hit the target location as much as possible, to realize the continuous exploration of the target location. In addition, based on this mutation strategy, we propose a fuzzing optimization method based on multi-factor seed selection, using LLVM framework to insert the target location information into the test program for preprocessing, and then the multi-factor seed selection strategy is used to select better seeds to explore the target location. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Location

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Intelligent Contract Timestamp Vulnerability Detection based on Key Control Flow Graph 36

Intelligent Contract Timestamp Vulnerability Detection Based...

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

作者： Quan, Qing Yu Zeng, Cheng Wang, Yi Feng Wang, Jiao Ran School of Computer Science and Information Engineering Hubei University Wuhan China School of Artificial Intelligence Hubei University Wuhan China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Wuhan China

ISBN: (纸本)1891706594

The extensive application of smart contract technology in the blockchain domain has positioned it as a key component of the digital economy. However, as the application scope of smart contracts expands, security issues have become increasingly evident, resulting in substantial economic losses. Current vulnerability detection methods largely depend on analyzing complete smart contract source code, which struggles to filter out extensive redundant information when identifying individual vulnerabilities. This leads to an insufficient exploration of the characteristics of smart contract code, which in turn results in lower overall accuracy of detection methods. In this context, the study introduces a method that transforms smart contract source code into a control flow graph, filtering out the main basic blocks based on key information about timestamp vulnerabilities. Subsequently, individual operation audits are conducted on these basic blocks, combined with the graphical structure of the control flow graph, utilizing gated graph neural networks for detecting timestamp vulnerabilities in smart contracts. Comparative experiments were performed on two public datasets, and the results indicate that compared to state-of-the-art detection methods, the proposed approach has improved the accuracy and F1 score for detecting timestamp vulnerabilities in smart contracts by 1.65% and 6.51%, providing valuable application prospects for the security of smart contracts. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Graph neural networks

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software Product Management in Large-Scale Agile 25th

Software Product Management in Large-Scale Agile

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25th International conference on Agile software Development (XP)

作者： Moe, Nils Brede Berntzen, Marthe Barbala, Astri Stray, Viktoria SINTEF Strindveien 4 N-7465 Trondheim Norway Univ Oslo Gaustadalleen 23B N-0373 Oslo Norway Knowit Solut AS Univ Gata 1 N-0164 Oslo Norway

ISBN: (纸本)9783031611537;9783031611544

Large-scale agile software development is increasingly being organized with product management. Although product management is familiar to software engineering, we need research-based knowledge about organizing product management in an agile development context. This study focuses on product management challenges and configurations in a Nordic fintech organization with 10,000 employees. We conducted 19 interviews with participants involved in core product management activities. The study identifies ten key factors that hinder product management performance in a large-scale agile product company. Further, we present six product management configurations used in the fintech organization. The most suitable setup depends on the product lifecycle stage, product size, and development team setup. Our summary of the product management configurations and challenges can guide software product managers working in large-scale agile companies.

关键词： Product Management Team Product Manager Product Owner Agile software Development Coordination

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An Approach for Providing Recommendation for Requirements Non-Conformant with Requirement Templates (RTs) 24

An Approach for Providing Recommendation for Requirements No...

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17th Innovations in software engineering conference (ISEC)

作者： Balwani, Shivani Tiwari, Saurabh Dasgupta, Sourish Sharma, Akhilesh DA IICT Gandhinagar Software Engn Res Lab Gandhinagar India DA IICT Gandhinagar Knowledge Discovery & Management Lab Gandhinagar India Indian Space Res Org ISRO Space Applicat Ctr SAC Ahmadabad Gujarat India

ISBN: (纸本)9798400717673

RTs generally possess a fixed syntactic structure and comprise predefined slots, and requirements written in the format of RTs must conform with the template structure. Suppose the requirements do not conform to the RT. In that case, manually verifying the conformity of requirements to RTs becomes a tedious task due to the large size of industry requirement documents and introduces the possibility of errors. Furthermore, rewriting requirements to conform to the template structure when they initially do not conform presents a significant challenge. This paper proposes a tool-based approach that automatically verifies whether Functional Requirements (FRs) conform to RTs. It recommends a Template Conformance (TC) requirement by generating a semantically identical requirement that Conforms to the template structure. Our study focused on two well-known RTs, Easy Approach to Requirements Syntax (EARS) and RUPPs, for checking conformance and making recommendations. We utilized Natural Language Processing (NLP) techniques and applied our approach to industrial and publicly available case studies. Our results demonstrate that the proposed tool-based approach facilitates requirement analysis and aids in recommending requirements based on their conformity with RTs. Our results show an accuracy of 83.9% for providing recommendations to non-conformant requirements with RTs.

关键词： Requirement Templates (RTs) Recommendation Quality Analysis Natural Language Processing (NLP)

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Developer's Cognitive Effort Maintaining Monoliths vs. Microservices - An Eye-Tracking Study 30

Developer's Cognitive Effort Maintaining Monoliths vs. Micro...

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30th Asia-Pacific software engineering conference (APSEC)

作者： Simhandl, Georg Paulweber, Philipp Zdun, Uwe Univ Vienna Res Grp Software Architecture Fac Comp Sci Vienna Austria Fiskaly GmbH Vienna Austria

ISBN: (纸本)9798350344172

The microservices architectural style improves flexibility and performance but might come at the cost of complexity and cognitive effort. Understanding how architectural design decision affects cognitive effort can support software engineers in designing and building more maintainable systems. However, little research exists measuring the impact of architectural styles on developers' cognitive processes. To the best of our knowledge, no empirical study on navigation and comprehension strategies compares a microservice-based and monolithic variant of a trading system, which is widely used for empirical research on information system evolution. To assess the cognitive processes, we conducted an eye-tracking study (n=42) of typical maintenance tasks. We randomly assigned participants to two groups performing a feature location and a code change task using the trading system's monolithic or microservices-based implementation. Efficacy is significantly higher in the monolith variant. We investigate the reasons and propose a cognitively-grounded method to analyse comprehension strategies and estimate maintenance effort. We measure differences in comprehension strategies (top-down and bottom-up comprehension) and conclude that 1) there is a significant difference in visual effort and time taken to identify a specific feature, and 2) bottom-up comprehension is more frequently applied in microservices than in the monolith variant. Finally, we discuss our findings and their implications for practitioners and the research community.

关键词： software architecture microservices monolith eye-tracking

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PATUNTRACK: Automated Generating Patch Examples for Issue Reports without Tracked Insecure Code 24

PATUNTRACK: Automated Generating Patch Examples for Issue Re...

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39th ACM/IEEE International conference on Automated software engineering (ASE)

作者： Jiang, Ziyou She, Lin Yang, Guowei Wang, Qing State Key Lab Intelligent Game Beijing Peoples R China Chinese Acad Sci Sci & Technol Integrated Informat Syst Lab Inst Software Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Beihang Univ Sch Software Beijing Peoples R China Univ Queensland Brisbane Qld Australia

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Security patches are essential for enhancing the stability and robustness of projects in the open-source software community. While vulnerabilities are officially expected to be patched before being disclosed, patching vulnerabilities is complicated and remains a struggle for many organizations. To patch vulnerabilities, security practitioners typically track vulnerable issue reports (IRs), and analyze their relevant insecure code to generate potential patches. However, the relevant insecure code may not be explicitly specified and practitioners cannot track the insecure code in the repositories, thus limiting their ability to generate patches. In such cases, providing examples of insecure code and the corresponding patches would benefit the security developers to better locate and resolve the actual insecure code. In this paper, we propose PATUNTRACK, an automated approach to generating patch examples from IRs without tracked insecure code. PATUNTRACK utilizes auto-prompting to optimize the Large Language Model (LLM) to make it applicable for analyzing the vulnerabilities described in IRs and generating appropriate patch examples. Specifically, it first generates the completed description of the Vulnerability-Triggering Path (VTP) from vulnerable IRs. Then, it corrects potential hallucinations in the VTP description with external golden knowledge. Finally, it generates Top-K pairs of Insecure Code and Patch Example based on the corrected VTP description. To evaluate the performance of PATUNTRACK, we conducted experiments on 5,465 vulnerable IRs. The experimental results show that PATUNTRACK can obtain the highest performance and improve the traditional LLM baselines by +17.7% (MatchFix) and +14.6% (Fix@10) on average in patch example generation. Furthermore, PATUNTRACK was applied to generate patch examples for 76 newly disclosed vulnerable IRs. 27 out of 37 replies from the authors of these IRs confirmed the usefulness of the patch examples generated by PATUNTRACK,

关键词： Codes Limiting Large language models Organizations Stability analysis Robustness Security Open source software software engineering

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