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Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis最新文献

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Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis 第一届机器学习与共生软件工程国际研讨会论文集
Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis Pub Date : 2018-09-03 DOI: 10.1145/3243127
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引用次数: 2
Applying graph kernels to model-driven engineering problems 图核在模型驱动工程问题中的应用
Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis Pub Date : 2018-09-03 DOI: 10.1145/3243127.3243128
R. Clarisó, Jordi Cabot
{"title":"Applying graph kernels to model-driven engineering problems","authors":"R. Clarisó, Jordi Cabot","doi":"10.1145/3243127.3243128","DOIUrl":"https://doi.org/10.1145/3243127.3243128","url":null,"abstract":"Machine Learning (ML) can be used to analyze and classify large collections of graph-based information, e.g. images, location information, the structure of molecules and proteins, ... Graph kernels is one of the ML techniques typically used for such tasks. In a software engineering context, models of a system such as structural or architectural diagrams can be viewed as labeled graphs. Thus, in this paper we propose to employ graph kernels for clustering software modeling artifacts. Among other benefits, this would improve the efficiency and usability of a variety of software modeling activities, e.g., design space exploration, testing or verification and validation.","PeriodicalId":244058,"journal":{"name":"Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis","volume":"26 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133105389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 20
Fast deployment and scoring of support vector machine models in CPU and GPU 支持向量机模型在CPU和GPU上的快速部署和评分
Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis Pub Date : 2018-09-03 DOI: 10.1145/3243127.3243133
Oscar Castro-López, Inés Fernando Vega López
{"title":"Fast deployment and scoring of support vector machine models in CPU and GPU","authors":"Oscar Castro-López, Inés Fernando Vega López","doi":"10.1145/3243127.3243133","DOIUrl":"https://doi.org/10.1145/3243127.3243133","url":null,"abstract":"In this paper, we present an approach for the fast deployment and efficient scoring of Support Vector Machine (SVM) models. We developed a compiler for transforming a formal specification of a SVM and generating source code in different versions of the C/C++ language. This effectively automates the deployment of SVM models and its integration into the operational software for its use. The proposed compiler generates efficient code to deploy SVM models in CPUs (single or multi-core) and in Graphics Processing Units (GPUs) through NVIDIA's Computed Unified Device Architecture (CUDA). We also present an empirical evaluation of our compiler's targets scoring a SVM model with a linear kernel. In our experiments we score a real dataset in batch mode at different scales. The results show that our C CUDA implementation performs better as data scale increases and it is approximately 38 times faster than the single-core implementation using single precision floating-point values.","PeriodicalId":244058,"journal":{"name":"Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123302386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A deep learning approach to program similarity 程序相似度的深度学习方法
Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis Pub Date : 2018-09-03 DOI: 10.1145/3243127.3243131
Niccolò Marastoni, R. Giacobazzi, M. Preda
{"title":"A deep learning approach to program similarity","authors":"Niccolò Marastoni, R. Giacobazzi, M. Preda","doi":"10.1145/3243127.3243131","DOIUrl":"https://doi.org/10.1145/3243127.3243131","url":null,"abstract":"In this work we tackle the problem of binary code similarity by using deep learning applied to binary code visualization techniques. Our idea is to represent binaries as images and then to investigate whether it is possible to recognize similar binaries by applying deep learning algorithms for image classification. In particular, we apply the proposed deep learning framework to a dataset of binary code variants obtained through code obfuscation. These binary variants exhibit similar behaviours while being syntactically different. Our results show that the problem of binary code recognition is strictly separated from simple image recognition problems. Moreover, the analysis of the results of the experiments conducted in this work lead us to the identification of interesting research challenges. For example, in order to use image recognition approaches to recognize similar binary code samples it is important to further investigate how to build a suitable mapping from executables to images.","PeriodicalId":244058,"journal":{"name":"Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131903899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 21
A language-agnostic model for semantic source code labeling 语义源代码标注的语言无关模型
Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis Pub Date : 2018-09-03 DOI: 10.1145/3243127.3243132
Ben U. Gelman, B. Hoyle, Jessica Moore, Joshua Saxe, David Slater
{"title":"A language-agnostic model for semantic source code labeling","authors":"Ben U. Gelman, B. Hoyle, Jessica Moore, Joshua Saxe, David Slater","doi":"10.1145/3243127.3243132","DOIUrl":"https://doi.org/10.1145/3243127.3243132","url":null,"abstract":"Code search and comprehension have become more difficult in recent years due to the rapid expansion of available source code. Current tools lack a way to label arbitrary code at scale while maintaining up-to-date representations of new programming languages, libraries, and functionalities. Comprehensive labeling of source code enables users to search for documents of interest and obtain a high-level understanding of their contents. We use Stack Overflow code snippets and their tags to train a language-agnostic, deep convolutional neural network to automatically predict semantic labels for source code documents. On Stack Overflow code snippets, we demonstrate a mean area under ROC of 0.957 over a long-tailed list of 4,508 tags. We also manually validate the model outputs on a diverse set of unlabeled source code documents retrieved from Github, and obtain a top-1 accuracy of 86.6%. This strongly indicates that the model successfully transfers its knowledge from Stack Overflow snippets to arbitrary source code documents.","PeriodicalId":244058,"journal":{"name":"Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126617126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Learning-based testing for autonomous systems using spatial and temporal requirements 基于学习的基于空间和时间需求的自主系统测试
Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis Pub Date : 2018-09-03 DOI: 10.1145/3243127.3243129
Hojat Khosrowjerdi, K. Meinke
{"title":"Learning-based testing for autonomous systems using spatial and temporal requirements","authors":"Hojat Khosrowjerdi, K. Meinke","doi":"10.1145/3243127.3243129","DOIUrl":"https://doi.org/10.1145/3243127.3243129","url":null,"abstract":"Cooperating cyber-physical systems-of-systems (CO-CPS) such as vehicle platoons, robot teams or drone swarms usually have strict safety requirements on both spatial and temporal behavior. Learning-based testing is a combination of machine learning and model checking that has been successfully used for black-box requirements testing of cyber-physical systems-of-systems. We present an overview of research in progress to apply learning-based testing to evaluate spatio-temporal requirements on autonomous systems-of-systems through modeling and simulation.","PeriodicalId":244058,"journal":{"name":"Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129171645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 24
Automatically assessing vulnerabilities discovered by compositional analysis 自动评估由组合分析发现的漏洞
Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis Pub Date : 2018-07-24 DOI: 10.1145/3243127.3243130
Saahil Ognawala, R. Amato, A. Pretschner, Pooja Kulkarni
{"title":"Automatically assessing vulnerabilities discovered by compositional analysis","authors":"Saahil Ognawala, R. Amato, A. Pretschner, Pooja Kulkarni","doi":"10.1145/3243127.3243130","DOIUrl":"https://doi.org/10.1145/3243127.3243130","url":null,"abstract":"Testing is the most widely employed method to find vulnerabilities in real-world software programs. Compositional analysis, based on symbolic execution, is an automated testing method to find vulnerabilities in medium- to large-scale programs consisting of many interacting components. However, existing compositional analysis frameworks do not assess the severity of reported vulnerabilities. In this paper, we present a framework to analyze vulnerabilities discovered by an existing compositional analysis tool and assign CVSS3 (Common Vulnerability Scoring System v3.0) scores to them, based on various heuristics such as interaction with related components, ease of reachability, complexity of design and likelihood of accepting unsanitized input. By analyzing vulnerabilities reported with CVSS3 scores in the past, we train simple machine learning models. By presenting our interactive framework to developers of popular open-source software and other security experts, we gather feedback on our trained models and further improve the features to increase the accuracy of our predictions. By providing qualitative (based on community feedback) and quantitative (based on prediction accuracy) evidence from 21 open-source programs, we show that our severity prediction framework can effectively assist developers with assessing vulnerabilities.","PeriodicalId":244058,"journal":{"name":"Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134023329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 12
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