Automated Software Engineering最新文献_第3页

Machine-readable specification and intelligent cloud-based execution of logical test cases for automated driving functions 自动驾驶功能逻辑测试用例的机器可读规范和基于云的智能执行

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-12-16 DOI: 10.1007/s10515-024-00481-6

Max-Arno Meyer, Mehdi Zouari, Sebastian Bannenberg, Markus Deppe, Sébastien Christiaens, Sung-Yong Lee, Jakob Andert

{"title":"Machine-readable specification and intelligent cloud-based execution of logical test cases for automated driving functions","authors":"Max-Arno Meyer, Mehdi Zouari, Sebastian Bannenberg, Markus Deppe, Sébastien Christiaens, Sung-Yong Lee, Jakob Andert","doi":"10.1007/s10515-024-00481-6","DOIUrl":"10.1007/s10515-024-00481-6","url":null,"abstract":"<div><p>The scenario-based verification and validation of highly automated driving functions requires extensive testing using a mix of interconnected test methods with varying degrees of virtualization, ranging from faster-than-real-time scenario exploration in software-in-the-loop simulations to in-vehicle testing. The efficiency of scenario-based test procedures is continuously improving, especially in the simulation domain with the introduction of parallel execution in the cloud, parameter variation algorithms and established standards for driving scenario specification. In contrast, current test case specifications are very tool- and project-specific and often not machine-readable, hindering the exchange, reuse and automation of scenario-based tests across all test platforms. This paper presents a novel machine-readable test specification format for scenario-based testing implemented as an XML schema. Its data structure incorporates logical or concrete scenarios within preconditions, inputs and pass criteria for automated driving functions following established test standards such as ISO/IEC/IEEE 29119. The format enables a tool-agnostic specification, reuse and exchange of scenario-based tests for simulation-based and in-vehicle testing. A cloud-simulation workflow has been developed that exploits the automation potential offered by the format. By means of testing a highway ramp-on function, a logical test case is generated and automatically imported into the simulator. For efficiently exploring the parameter space of the logical test case, a novel parameter variation method is applied. The combination of a dedicated test case format, intelligent scenario exploration methods and a state-of-the-art cloud simulation platform results in a highly efficient scenario-based test procedure.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"32 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10515-024-00481-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826154","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

Parallelization of genetic algorithms for software architecture recovery 软件架构恢复遗传算法的并行化

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-12-14 DOI: 10.1007/s10515-024-00479-0

Taha Varol, Milad Elyasi, T. Huzeyfe Aktaş, O. Örsan Özener, Hasan Sözer

{"title":"Parallelization of genetic algorithms for software architecture recovery","authors":"Taha Varol, Milad Elyasi, T. Huzeyfe Aktaş, O. Örsan Özener, Hasan Sözer","doi":"10.1007/s10515-024-00479-0","DOIUrl":"10.1007/s10515-024-00479-0","url":null,"abstract":"<div><p>Software Architecture Recovery (SAR) techniques analyze dependencies between software modules and automatically cluster them to achieve high modularity. Many of these approaches employ Genetic Algorithms (GAs) for clustering software modules. A major drawback of these algorithms is their lack of scalability. In this paper, we address this drawback by introducing generic software components that can encapsulate subroutines (operators) of a GA to execute them in parallel. We use these components to implement a novel hybrid GA for SAR that exploits parallelism to find better solutions faster. We compare the effectiveness of parallel algorithms with respect to the sequential counterparts that are previously proposed for SAR. We observe that parallelization enables a greater number of iterations to be performed in the search for high-quality solutions. The increased efficiency achieved through parallel processing allows for faster convergence towards optimal solutions by harnessing the power of multiple processing units in a coordinated manner. The amount of improvement in modularity is above 50%, which particularly increases in the context of large-scale systems. Our algorithm can scale to recover the architecture of a large system, Chromium, which has more than 18,500 modules and 750,000 dependencies among these modules.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"32 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821361","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

Efficient feature envy detection and refactoring based on graph neural network 基于图神经网络的高效特征嫉妒检测与重构

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-12-05 DOI: 10.1007/s10515-024-00476-3

Dongjin Yu, Yihang Xu, Lehui Weng, Jie Chen, Xin Chen, Quanxin Yang

{"title":"Efficient feature envy detection and refactoring based on graph neural network","authors":"Dongjin Yu, Yihang Xu, Lehui Weng, Jie Chen, Xin Chen, Quanxin Yang","doi":"10.1007/s10515-024-00476-3","DOIUrl":"10.1007/s10515-024-00476-3","url":null,"abstract":"<div><p>As one type of frequently occurring code smells, feature envy negatively affects class cohesion, increases coupling between classes, and thus hampers software maintainability. While progress has been made in feature envy detection, two challenges still persist. Firstly, existing approaches often underutilize method call relationships, resulting in suboptimal detection efficiency. Secondly, they lack the emphasis on feature envy refactoring, which is however the ultimate goal of feature envy detection. To address these challenges, we propose two approaches: <b>SCG</b> (<b>S</b>MOTE <b>C</b>all <b>G</b>raph) and <b>SFFL</b> (<b>S</b>ymmetric <b>F</b>eature <b>F</b>usion <b>L</b>earning). SCG transforms the feature envy detection problem into a binary classification task on a method call graph. It predicts the weights of edges, termed <i>calling strength</i>, to capture the strength of method invocations. Additionally, it converts the method-method call graph into a method-class call graph and recommends the smelly method to the external class with the highest <i>calling strength</i>. As a holistic approach focusing on refactoring feature envy directly, SFFL leverages four heterogeneous graphs to represent method-class relationships. Through Symmetric Feature Fusion Learning, it obtains representations for methods and classes. Link prediction is then employed to generate the refactored method-class ownership graph, which is regarded as the refactored results. Moreover, to address the limitations of existing metrics in accurately evaluating refactoring performance, we introduce three new metrics: <span>(textit{precision}_2)</span>, <span>(textit{recall}_2)</span> and <span>(textit{F}_1text {-score}_2)</span>. Extensive experiments on five open-source projects demonstrate the superiority of SCG and SFFL. The code and dataset used in our study are available at https://github.com/HduDBSI/SCG-SFFL.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"32 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142778569","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

Correction: Agile meets quantum: a novel genetic algorithm model for predicting the success of quantum software development project 更正：敏捷遇到量子：一种预测量子软件开发项目成功的新型遗传算法模型

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-12-05 DOI: 10.1007/s10515-024-00474-5

Arif Ali Khan, Muhammad Azeem Akbar, Valtteri Lahtinen, Marko Paavola, Mahmood Niazi, Mohammed Naif Alatawi, Shoayee Dlaim Alotaibi

引用次数: 0

Deep imperative mutations have less impact 深层命令式突变的影响较小

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-12-03 DOI: 10.1007/s10515-024-00475-4

W. B. Langdon, David Clark

{"title":"Deep imperative mutations have less impact","authors":"W. B. Langdon, David Clark","doi":"10.1007/s10515-024-00475-4","DOIUrl":"10.1007/s10515-024-00475-4","url":null,"abstract":"<div><p>Information theory and entropy loss predict deeper more hierarchical software will be more robust. Suggesting silent errors and equivalent mutations will be more common in deeper code, highly structured code will be hard to test, so explaining best practise preference for unit testing of small methods rather than system wide analysis. Using the genetic improvement (GI) tool MAGPIE, we measure the impact of source code mutations and how this varies with execution depth in two diverse multi-level nested software. gem5 is a million line single threaded state-of-the-art C++ discrete time VLSI circuit simulator, whilst PARSEC VIPS is a non-deterministic parallel computing multi-threaded image processing benchmark written in C. More than 28–53% of mutants compile and generate identical results to the original program. We observe 12% and 16% Failed Disruption Propagation (FDP). Excluding internal errors, exceptions and asserts, here most faults below about 30 nested function levels which are <b>E</b>xecuted and <b>I</b>nfect data or divert control flow are not <b>P</b>ropagated to the output, i.e. these deep PIE changes have no visible external effect. Suggesting automatic software engineering on highly structured code will be hard.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"32 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10515-024-00475-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761966","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

Technology adoption performance evaluation applied to testing industrial REST APIs 应用于测试工业REST api的技术采用性能评估

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-12-02 DOI: 10.1007/s10515-024-00477-2

Alexander Poth, Olsi Rrjolli, Andrea Arcuri

{"title":"Technology adoption performance evaluation applied to testing industrial REST APIs","authors":"Alexander Poth, Olsi Rrjolli, Andrea Arcuri","doi":"10.1007/s10515-024-00477-2","DOIUrl":"10.1007/s10515-024-00477-2","url":null,"abstract":"<div><p>Testing is an important task within software development. To write test cases and integrate them into an automated test suite requires a significant amount of work. Given a set of requirements and specifications of a software, testing is needed to verify its correctness. When done manually, it is an expensive and error prone task. To facilitate such work, automated test-case generation via tools could be useful. Test-case generation can be facilitated by deterministic algorithm-driven approaches or non-deterministic approaches such as with AI (e.g., evolutionary and LLM). The different approaches come with their strengths and weaknesses, which must be considered when integrating these approaches into a product test procedure in industry. Several novel testing techniques and tools have been developed in academia and industry, but how effective they are and how to integrate them in real-world large industrial scenarios is still unclear. In this paper, a systematic approach is presented to evaluate test-case generation methodologies and integrate them into a scalable enterprise setup. The specific context is black-box testing of REST APIs, based on their OpenAPI schemas. The aim is to facilitate IT product development and service delivery. The proposed Technology Adoption Performance Evaluation (TAPE) approach is evaluated by a case study within the Group IT of Volkswagen AG. We evaluated existing tools such as OpenAPI Generator, EvoMaster and StarCoder which are built on different technologies. Our results show that these tools are of benefit for test engineers to facilitate test-case specification and design within the Group IT of Volkswagen AG.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"32 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10515-024-00477-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757902","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

Tool report: EvoMaster—black and white box search-based fuzzing for REST, GraphQL and RPC APIs 工具报告：evommaster——REST、GraphQL和RPC api的基于黑盒和白盒搜索的模糊测试

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-11-29 DOI: 10.1007/s10515-024-00478-1

Andrea Arcuri, Man Zhang, Susruthan Seran, Juan Pablo Galeotti, Amid Golmohammadi, Onur Duman, Agustina Aldasoro, Hernan Ghianni

引用次数: 0

Evoattack: suppressive adversarial attacks against object detection models using evolutionary search Evoattack：利用进化搜索对物体检测模型进行压制性对抗攻击

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-11-06 DOI: 10.1007/s10515-024-00470-9

Kenneth H. Chan, Betty H. C. Cheng

{"title":"Evoattack: suppressive adversarial attacks against object detection models using evolutionary search","authors":"Kenneth H. Chan, Betty H. C. Cheng","doi":"10.1007/s10515-024-00470-9","DOIUrl":"10.1007/s10515-024-00470-9","url":null,"abstract":"<div><p>State-of-the-art deep neural networks are increasingly used in image classification, recognition, and detection tasks for a range of real-world applications. Moreover, many of these applications are safety-critical, where the failure of the system may cause serious harm, injuries, or even deaths. Adversarial examples are expected inputs that are maliciously modified, but difficult to detect, such that the machine learning models fail to classify them correctly. While a number of evolutionary search-based approaches have been developed to generate adversarial examples against image classification problems, evolutionary search-based attacks against <i>object detection</i> algorithms remain largely unexplored. This paper describes <span>EvoAttack</span> that demonstrates how evolutionary search-based techniques can be used as a black-box, model- and data-agnostic approach to attack state-of-the-art object detection algorithms (e.g., RetinaNet, Faster R-CNN, and YoloV5). A proof-of-concept implementation is provided to demonstrate how evolutionary search can generate adversarial examples that existing models fail to correctly process, which can be used to assess model robustness against such attacks. In contrast to other adversarial example approaches that cause misclassification or incorrect labeling of objects, <span>EvoAttack</span> applies minor perturbations to generate adversarial examples that <i>suppress</i> the ability of object detection algorithms to detect objects. We applied <span>EvoAttack</span> to popular benchmark datasets for autonomous terrestrial and aerial vehicles.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"32 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595532","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

Multi-objective improvement of Android applications 多目标改进安卓应用程序

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-11-04 DOI: 10.1007/s10515-024-00472-7

James Callan, Justyna Petke

{"title":"Multi-objective improvement of Android applications","authors":"James Callan, Justyna Petke","doi":"10.1007/s10515-024-00472-7","DOIUrl":"10.1007/s10515-024-00472-7","url":null,"abstract":"<div><p>Non-functional properties, such as runtime or memory use, are important to mobile app users and developers, as they affect user experience. We propose a practical approach and the first open-source tool, GIDroid for multi-objective automated improvement of Android apps. In particular, we use Genetic Improvement, a search-based technique that navigates the space of software variants to find improved software. We use a simulation-based testing framework to greatly improve the speed of search. GIDroid contains three state-of-the-art multi-objective algorithms, and two new mutation operators, which cache the results of method calls. Genetic Improvement relies on testing to validate patches. Previous work showed that tests in open-source Android applications are scarce. We thus wrote tests for 21 versions of 7 Android apps, creating a new benchmark for performance improvements. We used GIDroid to improve versions of mobile apps where developers had previously found improvements to runtime, memory, and bandwidth use. Our technique automatically re-discovers 64% of existing improvements. We then applied our approach to current versions of software in which there were no known improvements. We were able to improve execution time by up to 35%, and memory use by up to 33% in these apps.\u0000</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"32 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10515-024-00472-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579515","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

Contractsentry: a static analysis tool for smart contract vulnerability detection Contractsentry：智能合约漏洞检测静态分析工具

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2024-10-23 DOI: 10.1007/s10515-024-00471-8

Shiji Wang, Xiangfu Zhao

{"title":"Contractsentry: a static analysis tool for smart contract vulnerability detection","authors":"Shiji Wang, Xiangfu Zhao","doi":"10.1007/s10515-024-00471-8","DOIUrl":"10.1007/s10515-024-00471-8","url":null,"abstract":"<div><p>Frequent smart contract security incidents pose a threat to the credibility of the Ethereum platform, making smart contract vulnerability detection a focal point of concern. Previous research has proposed vulnerability detection methods in smart contracts. Generally, these tools rely on predefined rules to detect vulnerable smart contracts. However, using out-of-date rules for vulnerability detection may lead to a significant number of false negatives and false positives due to the growing variety of smart contract vulnerability types and the ongoing enhancement of vulnerability defense mechanisms. In this paper, we propose ContractSentry, a tool for static analysis of smart contracts. First, we preprocess Solidity code to build critical contract information and transform it into an intermediate representation. Then, based on the intermediate representations, we propose composite rules for vulnerability detection by analyzing the characteristics of different types of vulnerabilities in smart contracts. Finally, we evaluate ContractSentry with two datasets and compare it with state-of-the-art vulnerability detection tools. Experimental results demonstrate that ContractSentry achieves superior detection effectiveness.\u0000</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"32 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518464","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