Automated Software Engineering最新文献_第6页

Semantic context based coincidental correct test cases detection for fault localization 基于语义上下文的故障定位重合正确测试用例检测

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-08-18 DOI: 10.1007/s10515-024-00466-5

Jian Hu

{"title":"Semantic context based coincidental correct test cases detection for fault localization","authors":"Jian Hu","doi":"10.1007/s10515-024-00466-5","DOIUrl":"10.1007/s10515-024-00466-5","url":null,"abstract":"<div><p>Fault localization is a process that aims to identify the potentially faulty statements responsible for program failures by analyzing runtime information. Therefore, the input code coverage matrix plays a crucial role in FL. However, the effectiveness of fault localization is compromised by the presence of coincidental correct test cases (CCTC) in the coverage matrix. These CCTC execute faulty code but do not result in program failures. To address this issue, many existing methods focus on identifying CCTC through cluster analysis. However, these methods have three problems. Firstly, identifying the optimal cluster count poses a considerable challenge in CCTC detection. Secondly, the effectiveness of CCTC detection is heavily influenced by the initial centroid selection. Thirdly, the presence of abundant fault-irrelevant statements within the raw coverage matrix introduces substantial noise for CCTC detection. To overcome these challenges, we propose SCD4FL: a semantic context-based CCTC detection method to enhance the coverage matrix for fault localization. SCD4FL incorporates and implements two key ideas: (1) SCD4FL uses the intersection of execution slices to construct a semantic context from the raw coverage matrix, effectively reducing noise during CCTC detection. (2) SCD4FL employs an expert-knowledge-based K-nearest neighbors (KNN) algorithm to detect the CCTC, effectively eliminating the requirement of determining the cluster number and initial centroid. To evaluate the effectiveness of SCD4FL, we conducted extensive experiments on 420 faulty versions of nine benchmarks using six state-of-the-art fault localization methods and two representative CCTC detection methods. The experimental results validate the effectiveness of our method in enhancing the performance of the six fault localization methods and two CCTC detection methods, e.g., the RNN method can be improved by 53.09% under the MFR metric.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"31 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142198469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A study on cross-project fault prediction through resampling and feature reduction along with source projects selection 通过重采样和特征缩减以及源项目选择进行跨项目故障预测的研究

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-08-16 DOI: 10.1007/s10515-024-00465-6

Pravali Manchala, Manjubala Bisi

{"title":"A study on cross-project fault prediction through resampling and feature reduction along with source projects selection","authors":"Pravali Manchala, Manjubala Bisi","doi":"10.1007/s10515-024-00465-6","DOIUrl":"10.1007/s10515-024-00465-6","url":null,"abstract":"<div><p>Software Fault Prediction is an efficient strategy to improve the quality of software systems. In reality, there won’t be adequate software fault data for a recently established project where the Cross-Project Fault Prediction (CPFP) model plays an important role. CPFP model utilizes other finished projects data to predict faults in ongoing projects. Existing CPFP methods concentrate on discrepancies in distribution between projects without exploring relevant source projects selection combined with distribution gap minimizing methods. Additionally, performing imbalance learning and feature extraction in software projects only balances the data and reduces features by eliminating redundant and unrelated features. This paper proposes a novel SRES method called Similarity and applicability based source projects selection, REsampling, and Stacked autoencoder (SRES) model. To analyze the performance of relevant source projects over CPFP, we proposed a new similarity and applicability based source projects selection method to automatically select sources for the target project. In addition, we introduced a new resampling method that balances source project data by generating data related to the target project, eliminating unrelated data, and reducing the distribution gap. Then, SRES uses the stacked autoencoder to extract informative intermediate feature data to further improve the prediction accuracy of the CPFP. SRES performs comparable to or superior to the conventional CPFP model on six different performance indicators over 24 projects by effectively addressing the issues of CPFP. In conclusion, we can ensure that resampling and feature reduction techniques, along with source projects selection can improve cross-project prediction performance.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"31 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142198467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy efficient resource allocation based on virtual network embedding for IoT data generation 基于虚拟网络嵌入的节能资源分配，用于物联网数据生成

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-08-12 DOI: 10.1007/s10515-024-00463-8

Lizhuang Tan, Amjad Aldweesh, Ning Chen, Jian Wang, Jianyong Zhang, Yi Zhang, Konstantin Igorevich Kostromitin, Peiying Zhang

{"title":"Energy efficient resource allocation based on virtual network embedding for IoT data generation","authors":"Lizhuang Tan, Amjad Aldweesh, Ning Chen, Jian Wang, Jianyong Zhang, Yi Zhang, Konstantin Igorevich Kostromitin, Peiying Zhang","doi":"10.1007/s10515-024-00463-8","DOIUrl":"10.1007/s10515-024-00463-8","url":null,"abstract":"<div><p>The Internet of Things (IoT) has become a core driver leading technological advancements and social transformations. Furthermore, data generation plays multiple roles in IoT, such as driving decision-making, achieving intelligence, promoting innovation, improving user experience, and ensuring security, making it a critical factor in promoting the development and application of IoT. Due to the vast scale of the network and the complexity of device interconnection, effective resource allocation has become crucial. Leveraging the flexibility of Network Virtualization technology in decoupling network functions and resources, this work proposes a Multi-Domain Virtual Network Embedding algorithm based on Deep Reinforcement Learning to provide energy-efficient resource allocation decision-making for IoT data generation. Specifically, we deploy a four-layer structured agent to calculate candidate IoT nodes and links that meet data generation requirements. Moreover, the agent is guided by the reward mechanism and gradient back-propagation algorithm for optimization. Finally, the effectiveness of the proposed method is validated through simulation experiments. Compared with other methods, our method improves the long-term revenue, long-term resource utilization, and allocation success rate by 15.78%, 15.56%, and 6.78%, respectively.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"31 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142198468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A survey on robustness attacks for deep code models 深度代码模型鲁棒性攻击调查

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-08-09 DOI: 10.1007/s10515-024-00464-7

Yubin Qu, Song Huang, Yongming Yao

{"title":"A survey on robustness attacks for deep code models","authors":"Yubin Qu, Song Huang, Yongming Yao","doi":"10.1007/s10515-024-00464-7","DOIUrl":"10.1007/s10515-024-00464-7","url":null,"abstract":"<div><p>With the widespread application of deep learning in software engineering, deep code models have played an important role in improving code quality and development efficiency, promoting the intelligence and industrialization of software engineering. In recent years, the fragility of deep code models has been constantly exposed, with various attack methods emerging against deep code models and robustness attacks being a new attack paradigm. Adversarial samples after model deployment are generated to evade the predictions of deep code models, making robustness attacks a hot research direction. Therefore, to provide a comprehensive survey of robustness attacks on deep code models and their implications, this paper comprehensively analyzes the robustness attack methods in deep code models. Firstly, it analyzes the differences between robustness attacks and other attack paradigms, defines basic attack methods and processes, and then summarizes robustness attacks’ threat model, evaluation metrics, attack settings, etc. Furthermore, existing attack methods are classified from multiple dimensions, such as attacker knowledge and attack scenarios. In addition, common tasks, datasets, and deep learning models in robustness attack research are also summarized, introducing beneficial applications of robustness attacks in data augmentation, adversarial training, etc., and finally, looking forward to future key research directions.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"31 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141921312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated system-level testing of unmanned aerial systems 无人驾驶航空系统的自动化系统级测试

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-08-01 DOI: 10.1007/s10515-024-00462-9

Hassan Sartaj, Asmar Muqeet, Muhammad Zohaib Iqbal, Muhammad Uzair Khan

{"title":"Automated system-level testing of unmanned aerial systems","authors":"Hassan Sartaj, Asmar Muqeet, Muhammad Zohaib Iqbal, Muhammad Uzair Khan","doi":"10.1007/s10515-024-00462-9","DOIUrl":"10.1007/s10515-024-00462-9","url":null,"abstract":"<div><p>Unmanned aerial systems (UAS) rely on various avionics systems that are safety-critical and mission-critical. A major requirement of international safety standards is to perform rigorous system-level testing of avionics software systems. The current industrial practice is to manually create test scenarios, manually/automatically execute these scenarios using simulators, and manually evaluate outcomes. The test scenarios typically consist of setting certain flight or environment conditions and testing the system under test in these settings. The state-of-the-art approaches for this purpose also require manual test scenario development and evaluation. In this paper, we propose a novel approach to automate the system-level testing of the UAS. The proposed approach (namely <span>AITester</span>) utilizes model-based testing and artificial intelligence (AI) techniques to automatically generate, execute, and evaluate various test scenarios. The test scenarios are generated on the fly, i.e., during test execution based on the environmental context at runtime. The approach is supported by a toolset. We empirically evaluated the proposed approach on two core components of UAS, an autopilot system of an unmanned aerial vehicle (UAV) and cockpit display systems (CDS) of the ground control station (GCS). The results show that the <span>AITester</span> effectively generates test scenarios causing deviations from the expected behavior of the UAV autopilot and reveals potential flaws in the GCS-CDS.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"31 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141864474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Angels or demons: investigating and detecting decentralized financial traps on ethereum smart contracts 天使还是魔鬼：调查和检测以太坊智能合约上的去中心化金融陷阱

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-07-29 DOI: 10.1007/s10515-024-00459-4

Jiachi Chen, Jiang Hu, Xin Xia, David Lo, John Grundy, Zhipeng Gao, Ting Chen

{"title":"Angels or demons: investigating and detecting decentralized financial traps on ethereum smart contracts","authors":"Jiachi Chen, Jiang Hu, Xin Xia, David Lo, John Grundy, Zhipeng Gao, Ting Chen","doi":"10.1007/s10515-024-00459-4","DOIUrl":"10.1007/s10515-024-00459-4","url":null,"abstract":"<div><p>Decentralized Finance (DeFi) uses blockchain technologies to transform traditional financial activities into decentralized platforms that run without intermediaries and centralized institutions. Smart contracts are programs that run on the blockchain, and by utilizing smart contracts, developers can more easily develop DeFi applications. Some key features of smart contracts—self-executed and immutability—ensure the trustworthiness, transparency and efficiency of DeFi applications and have led to a fast-growing DeFi market. However, misbehaving developers can add traps or backdoor code snippets to a smart contract, which are hard for contract users to discover. We call these code snippets in a DeFi smart contract as “<i>DeFi Contract Traps</i>” (DCTs). In this paper, we identify five DeFi contract traps and introduce their behaviors, describe how attackers use them to make unfair profits and analyze their prevalence in the Ethereum platform. We propose a symbolic execution tool, <span>DeFiDefender</span>, to detect such traps and use a manually labeled small-scale dataset that consists of 700 smart contracts to evaluate it. Our results show that our tool is not only highly effective but also highly efficient.<span>DeFiDefender</span> only needs 0.48 s to analyze one DeFi smart contract and obtains a high average accuracy (98.17%), precision (99.74%)and recall (89.24%). Among the five DeFi contract traps introduced in this paper, four of them can be detected through contract bytecode without the need for source code. We also apply <span>DeFiDefender</span> to a large-scale dataset that consists of 20,679 real DeFi-related Ethereum smart contracts. We found that 52.13% of these DeFi smart contracts contain at least one contract trap. Although a smart contract that contains contract traps is not necessarily malicious, our finding suggests that DeFi-related contracts have many centralized issues in a zero-trust environment and in the absence of a trusted party.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"31 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141864641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revisiting file context for source code summarization 重新审视源代码摘要的文件上下文

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-07-27 DOI: 10.1007/s10515-024-00460-x

Chia-Yi Su, Aakash Bansal, Collin McMillan

引用次数: 0

TM-fuzzer: fuzzing autonomous driving systems through traffic management TM-fuzzer：通过交通管理对自动驾驶系统进行模糊测试

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-07-27 DOI: 10.1007/s10515-024-00461-w

Shenghao Lin, Fansong Chen, Laile Xi, Gaosheng Wang, Rongrong Xi, Yuyan Sun, Hongsong Zhu

引用次数: 0

Rethinking AI code generation: a one-shot correction approach based on user feedback 反思人工智能代码生成：基于用户反馈的一次性修正方法

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-07-12 DOI: 10.1007/s10515-024-00451-y

Kim Tuyen Le, Artur Andrzejak

{"title":"Rethinking AI code generation: a one-shot correction approach based on user feedback","authors":"Kim Tuyen Le, Artur Andrzejak","doi":"10.1007/s10515-024-00451-y","DOIUrl":"10.1007/s10515-024-00451-y","url":null,"abstract":"<div><p>Code generation has become an integral feature of modern IDEs, gathering significant attention. Notable approaches like GitHub Copilot and TabNine have been proposed to tackle this task. However, these tools may shift code writing tasks towards code reviewing, which involves modification from users. Despite the advantages of user feedback, their responses remain transient and lack persistence across interaction sessions. This is attributed to the inherent characteristics of generative AI models, which require explicit re-training for new data integration. Additionally, the non-deterministic and unpredictable nature of AI-powered models limits thorough examination of their unforeseen behaviors. We propose a methodology named <i>One-shot Correction</i> to mitigate these issues in natural language to code translation models with no additional re-training. We utilize decomposition techniques to break down code translation into sub-problems. The final code is constructed using code snippets of each query chunk, extracted from user feedback or selectively generated from a generative model. Our evaluation indicates comparable or improved performance compared to other models. Moreover, the methodology offers straightforward and interpretable approaches, which enable in-depth examination of unexpected results and facilitate insights for potential enhancements. We also illustrate that user feedback can substantially improve code translation models without re-training. Ultimately, we develop a preliminary GUI application to demonstrate the utility of our methodology in simplifying customization and assessment of suggested code for users.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"31 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10515-024-00451-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141609627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interactive search-based Product Line Architecture design 基于搜索的互动式产品线架构设计

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-07-09 DOI: 10.1007/s10515-024-00457-6

Willian Marques Freire, Cláudia Tupan Rosa, Aline Maria Malachini Miotto Amaral, Thelma Elita Colanzi

{"title":"Interactive search-based Product Line Architecture design","authors":"Willian Marques Freire, Cláudia Tupan Rosa, Aline Maria Malachini Miotto Amaral, Thelma Elita Colanzi","doi":"10.1007/s10515-024-00457-6","DOIUrl":"10.1007/s10515-024-00457-6","url":null,"abstract":"<div><p>Software Product Line (SPL) is an approach derived from other engineering fields that use reuse techniques for a family of products in a given domain. An essential artifact of SPL is the Product Line Architecture (PLA), which identifies elements characterized by variation points, variability, and variants. The PLA aims to anticipate design decisions to obtain features such as reusability and modularity. Nevertheless, getting a reusable and modular PLA and following pre-defined standards can be a complex task involving several conflicting objectives. In this sense, PLA can be formulated as a multiobjective optimization problem. This research presents an approach that helps DMs (Decision Makers) to interactively optimize the PLAs through several strategies such as interactive optimization and Machine Learning (ML) algorithms. The interactive multiobjective optimization approach for PLA design (iMOA4PLA) uses specific metrics for the PLA optimization problem, implemented through the OPLA-Tool v2.0. In this approach, the architect assumes the role of DM during the search process, guiding the evolution of PLAs through various strategies proposed in previous works. Two quantitative and one qualitative experiments were performed to evaluate the iMOA4PLA. The results showed that this approach can assist the PLA optimization process by meeting more than 90% of DM preferences. The scientific contribution of this work lies in providing an approach for the PLA design and evaluation that leverages the benefits of machine learning algorithms and can serve as a basis for different SE contexts.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"31 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141567174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0