Automated Software Engineering最新文献

{"title":"Survey Paper on Development of ROS for Fault Detection of Underwater Cables","authors":"Vijay U. Rathod, Haritima Kushwaha, Teheseen Shaikh, Vaishnavi Joshi, Shubham Awantkar","doi":"10.11648/j.se.20231001.11","DOIUrl":"https://doi.org/10.11648/j.se.20231001.11","url":null,"abstract":": An introduction to ROS, an open source robot operating system, is given in this paper. In terms of process management and scheduling, ROS is not an operating system. This study explains how Robotic Operating System (ROS) can be used to control items (such as vehicles) remotely and cautiously without human intervention at the location. Instead, it gives heterogeneous computing clusters a structured communication layer on top of the host operating system. This document gives a quick explanation of how ROS fits into the current robot software architecture and how we may utilize it for AUVs (Automated under water vehicle). One of the media that connects the entire world to the internet is optical cable, which is typically installed underground or under water. As a result, it is challenging to inspect them thoroughly because it costs more to do so. To address this issue, we are presenting a solution that involves developing a robotic operating system that would assist in checking the underwater/underground cables. To put this into practice, we have been utilizing VMWare Workstation to virtually install Ubuntu OS, where we will be installing ROS packages, with the ROS-Gazebo toolbox serving as one of the primary tools. We are testing the implemented software with the standard inputs. We are using light radiation as the primary factor to assess the condition of the optical cable.","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"95 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89979180","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}

{"title":"Mining domain-specific edit operations from model repositories with applications to semantic lifting of model differences and change profiling","authors":"Christof Tinnes,&nbsp;Timo Kehrer,&nbsp;Mitchell Joblin,&nbsp;Uwe Hohenstein,&nbsp;Andreas Biesdorf,&nbsp;Sven Apel","doi":"10.1007/s10515-023-00381-1","DOIUrl":"10.1007/s10515-023-00381-1","url":null,"abstract":"<div><p>Model transformations are central to model-driven software development. Applications of model transformations include creating models, handling model co-evolution, model merging, and understanding model evolution. In the past, various (semi-)automatic approaches to derive model transformations from meta-models or from examples have been proposed. These approaches require time-consuming handcrafting or the recording of concrete examples, or they are unable to derive complex transformations. We propose a novel <i>unsupervised</i> approach, called <span>Ockham</span>, which is able to learn edit operations from model histories in model repositories. <span>Ockham</span> is based on the idea that meaningful domain-specific edit operations are the ones that <i>compress</i> the model differences. It employs frequent subgraph mining to discover frequent structures in model difference graphs. We evaluate our approach in two controlled experiments and one real-world case study of a large-scale industrial model-driven architecture project in the railway domain. We found that our approach is able to discover frequent edit operations that have actually been applied before. Furthermore, <span>Ockham</span> is able to extract edit operations that are meaningful—in the sense of explaining model differences through the edit operations they comprise—to practitioners in an industrial setting. We also discuss use cases (i.e., semantic lifting of model differences and change profiles) for the discovered edit operations in this industrial setting. We find that the edit operations discovered by <span>Ockham</span> can be used to better understand and simulate the evolution of models.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"30 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10515-023-00381-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50047644","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}

{"title":"GNet4FL: effective fault localization via graph convolutional neural network","authors":"Jie Qian,&nbsp;Xiaolin Ju,&nbsp;Xiang Chen","doi":"10.1007/s10515-023-00383-z","DOIUrl":"10.1007/s10515-023-00383-z","url":null,"abstract":"<div><p>Fault localization aims to efficiently locate faults when debugging programs, reducing software development and maintenance costs. Spectrum-based fault location (SBFL) is the most commonly used fault location technology, which calculates and ranks the suspicious value of each program entity with a specific formula by counting the coverage information of all the program entities and execution results of test cases. However, previous SBFL techniques suffered from low accuracy due to the sole use of execution coverage. This paper proposed an approach GNet4FL based on the graph convolutional neural network. GNet4FL first collects static features based on code structure and dynamic features based on test results. Then, GNet4FL uses GraphSAGE to obtain node representation of source codes and performs feature fusion on an entity consisting of multiple nodes, which preserves the topological information of the graph. Finally, the representation of each entity is input to the multi-layer perceptron for training and ranking entities. The results of the study showed that GNet4FL successfully located 160 out of 262 faults, outperforming the three state-of-the-art methods by 94, 42, and 14% in Top-1 accuracy, and having close results to Grace with less cost. Furthermore, we investigated the impact of each component (i.e., graph neural network, pruning, and dynamic features) of GNet4FL on the results. We found that all of these components had a positive impact on the proposed approach.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"30 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10515-023-00383-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50045317","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}

{"title":"Correction to: Data-driven prototyping via natural-language-based GUI retrieval","authors":"Kristian Kolthoff,&nbsp;Christian Bartelt,&nbsp;Simone Paolo Ponzetto","doi":"10.1007/s10515-023-00382-0","DOIUrl":"10.1007/s10515-023-00382-0","url":null,"abstract":"","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"30 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10515-023-00382-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50017215","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}

{"title":"qaAskeR(^+): a novel testing method for question answering software via asking recursive questions","authors":"Xiaoyuan Xie,&nbsp;Shuo Jin,&nbsp;Songqiang Chen","doi":"10.1007/s10515-023-00380-2","DOIUrl":"10.1007/s10515-023-00380-2","url":null,"abstract":"<div><p>Question Answering (QA) is an attractive and challenging area in NLP community. With the development of QA technique, plenty of QA software has been applied in daily human life to provide convenient access of information retrieval. To investigate the performance of QA software, many benchmark datasets have been constructed to provide various test cases. However, current QA software is mainly tested in a reference-based paradigm, in which the expected outputs (labels) of test cases are mandatory to be annotated with much human effort before testing. As a result, neither the just-in-time test during usage nor the extensible test on massive unlabeled real-life data is feasible, which keeps the current testing of QA software from being flexible and sufficient. In this work, we propose a novel testing method, <span>qaAskeR</span> <span>(^+)</span>, with five new Metamorphic Relations for QA software. <span>qaAskeR</span> <span>(^+)</span> does not refer to the annotated labels of test cases. Instead, based on the idea that a correct answer should imply a piece of reliable knowledge that always conforms with any other correct answer, <span>qaAskeR</span> <span>(^+)</span> tests QA software by inspecting its behaviors on multiple recursively asked questions that are relevant to the same or some further enriched knowledge. Experimental results show that <span>qaAskeR</span> <span>(^+)</span> can reveal quite a few violations that indicate actual answering issues on various mainstream QA software without using any pre-annotated labels.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"30 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10515-023-00380-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50051303","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}

{"title":"Data-driven prototyping via natural-language-based GUI retrieval","authors":"Kristian Kolthoff,&nbsp;Christian Bartelt,&nbsp;Simone Paolo Ponzetto","doi":"10.1007/s10515-023-00377-x","DOIUrl":"10.1007/s10515-023-00377-x","url":null,"abstract":"<div><p>Rapid GUI prototyping has evolved into a widely applied technique in early stages of software development to facilitate the clarification and refinement of requirements. Especially high-fidelity GUI prototyping has shown to enable productive discussions with customers and mitigate potential misunderstandings, however, the benefits of applying high-fidelity GUI prototypes are accompanied by the disadvantage of being expensive and time-consuming in development and requiring experience to create. In this work, we show <i>RaWi</i>, a data-driven GUI prototyping approach that effectively retrieves GUIs for reuse from a large-scale semi-automatically created GUI repository for mobile apps on the basis of Natural Language (NL) searches to facilitate GUI prototyping and improve its productivity by leveraging the vast GUI prototyping knowledge embodied in the repository. Retrieved GUIs can directly be reused and adapted in the graphical editor of <i>RaWi</i>. Moreover, we present a comprehensive evaluation methodology to enable (i) the systematic evaluation of NL-based GUI ranking methods through a novel high-quality gold standard and conduct an in-depth evaluation of traditional IR and state-of-the-art BERT-based models for GUI ranking, and (ii) the assessment of GUI prototyping productivity accompanied by an extensive user study in a practical GUI prototyping environment.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"30 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10515-023-00377-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50026339","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}

{"title":"bjXnet: an improved bug localization model based on code property graph and attention mechanism","authors":"Jiaxuan Han,&nbsp;Cheng Huang,&nbsp;Siqi Sun,&nbsp;Zhonglin Liu,&nbsp;Jiayong Liu","doi":"10.1007/s10515-023-00379-9","DOIUrl":"10.1007/s10515-023-00379-9","url":null,"abstract":"<div><p>Bug localization technologies and tools are widely used in software engineering. Although state-of-the-art methods have achieved great progress, they only consider the source code information at the text level, which may establish a wrong correlation between the source code and the bug report, affecting the localization accuracy and reliability. In this paper, we propose an improved bug localization model, which uses the semantics of source codes at the graph level to supplement its semantics at the text level, optimizing and adjusting the graph semantics in combination with the attention mechanism to obtain the code semantic feature including the shallow and deep semantics of the source code. Finally, the correlation between code semantic feature and report semantic feature is measured by cosine similarity. We conduct experiments on three open source Java projects to comprehensively evaluate the performance of proposed model. The experimental results show that the model is significantly better than state-of-the-art methods.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"30 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50013451","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}

{"title":"AdaComplete: improve DL-based code completion method’s domain adaptability","authors":"Zejun Wang,&nbsp;Fang Liu,&nbsp;Yiyang Hao,&nbsp;Zhi Jin","doi":"10.1007/s10515-023-00376-y","DOIUrl":"10.1007/s10515-023-00376-y","url":null,"abstract":"<div><p>Code completion is an important feature in integrated development environments that can accelerate the coding process. With the development of deep learning technologies and easy-to-acquire open-source codebases, many Deep Learning based code completion models (DL models) are proposed. These models are trained using the generic source code datasets, resulting in poor domain adaptability. That is, these models suffer from performance loss when helping programmers code in a specific domain, e.g., helping to decide which domain-specific API to call. To solve the problem, we propose <i>AdaComplete</i>, a simple and effective framework that utilizes a local code completion model to compensate DL models’ domain adaptability. The local code completion model is trained using the source codes of the target domain. When used in code completion, given the context, AdaComplete can adaptively choose the recommendations from either the DL model or the local code completion model based on our hand-crafted features. Experimental results show that AdaComplete outperforms state-of-the-art DL-based code completion methods on specific domains and can improve the accuracy by 7% on average.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"30 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50011400","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}

{"title":"DroidHook: a novel API-hook based Android malware dynamic analysis sandbox","authors":"Yuning Cui,&nbsp;Yi Sun,&nbsp;Zhaowen Lin","doi":"10.1007/s10515-023-00378-w","DOIUrl":"10.1007/s10515-023-00378-w","url":null,"abstract":"<div><p>With the popularity of Android devices, mobile apps are prevalent in our daily life, making them a target for attackers to steal private data and push advertisements. Dynamic analysis is an effective approach to detect runtime behavior of Android malware and can reduce the impact of code obfuscation. However, some dynamic sandboxes commonly used by researchers are usually based on emulators with older versions of Android, for example, the state-of-the-art sandbox, DroidBox. These sandboxes are vulnerable to evasion attacks and may not work with the latest apps. In this paper, we propose a prototype framework, DroidHook, as a novel automated sandbox for Android malware dynamic analysis. Unlike most existing tools, DroidHook has two obvious advantages. Firstly, the set of APIs to be monitored by DroidHook can be easily modified, so that DroidHook is ideally suitable for diverse situations, including the detection of a specific family of malware and unknown malware. Secondly, DroidHook does not depend on a specific Android OS but only on Xposed, so it can work with multiple Android versions and can perform normally on both emulators and real devices. Experiments show that DroidHook can provide more fine-grained and precise results than DroidBox. Moreover, with the support for real devices and new versions of Android, DroidHook can run most samples properly and acquire stronger detection results, compared to emulator-based tools.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"30 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50044869","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}

{"title":"Freeze-and-mutate: abnormal sample identification for DL applications through model core analysis","authors":"Huiyan Wang,&nbsp;Ziqi Chen,&nbsp;Chang Xu","doi":"10.1007/s10515-022-00373-7","DOIUrl":"10.1007/s10515-022-00373-7","url":null,"abstract":"<div><p>Deep learning (DL) applications, representing an emerging form of new software, are gaining increasing popularity by their intelligent and adaptive services. However, their service reliability depends highly on the prediction accuracy of their internally-integrated DL models. In practice, DL models are often observed to suffer from ill predictions upon abnormal inputs (e.g., adversarial attacking samples, out-of-distribution (OOD) samples, and etc.), and this could easily lead to unexpected behaviors or even catastrophic consequences (e.g., system crash). One promising way to guard the application reliability is to reveal such abnormal inputs in time before they are fed to the DL models integrated in the concerned applications. Then remedy actions (e.g., discarding or fixing these inputs) can be done to protect applications from acting abnormally. Existing work addressed this revealing problem by either making sample distance-comparison based analysis or generating sufficient model mutants for comparative analysis. However, such treatments caused a restricted focus on samples only, while overlooking the DL models themselves, or had to analyze massive mutants, incurring non-negligible overheads to applications. In this article, we propose a novel approach, <span>NetChopper</span>, to conducting a core analysis on the target DL model, and then partitioning it into two parts, one associating closely with the training knowledge being the model core (expected to be important and thus stable), and the other being the remaining part (expected to be immaterial and thus changeable). Based on such partitioning, <span>NetChopper</span> proceeds to preserve (or freeze) the model core, but mutate the remaining part to produce only a small number of model mutants. Later, <span>NetChopper</span> becomes able to reveal abnormal inputs from normal ones by exploiting these model-relevant and light-weight mutants only. We experimentally evaluated <span>NetChopper</span> by widely-used DL subjects (e.g., MNIST+LeNet4, and CIFAR10+VGG16) and typical abnormal inputs (e.g., adversarial and OOD samples). The results reported <span>NetChopper</span> ’s promising AUROC scores in revealing the abnormal degrees of inputs, generally and stably outperforming, or comparably effective as, state-of-the-art techniques (e.g., mMutant, Surprise, and Mahalanobis), and also confirmed its high effectiveness and efficiency (with only marginal online overhead).</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"30 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50035542","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}