{"title":"Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis","authors":"","doi":"10.1145/3243127","DOIUrl":"https://doi.org/10.1145/3243127","url":null,"abstract":"","PeriodicalId":244058,"journal":{"name":"Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis","volume":"188 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":"123178065","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}
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.
{"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}
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.
{"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}
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.
{"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}
Ben U. Gelman, B. Hoyle, Jessica Moore, Joshua Saxe, David Slater
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.
{"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}
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.
{"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}
Saahil Ognawala, R. Amato, A. Pretschner, Pooja Kulkarni
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.
测试是在实际软件程序中发现漏洞的最广泛使用的方法。组合分析是一种基于符号执行的自动化测试方法，用于在由许多相互作用的组件组成的中大型程序中发现漏洞。然而，现有的组合分析框架并没有评估所报告的漏洞的严重性。在本文中，我们提出了一个框架来分析现有组合分析工具发现的漏洞，并根据与相关组件的交互、可达性、设计复杂性和接受未经处理的输入的可能性等各种启发式方法，为它们分配CVSS3 (Common Vulnerability Scoring System v3.0)分数。通过分析过去使用CVSS3分数报告的漏洞，我们训练了简单的机器学习模型。通过向流行的开源软件开发人员和其他安全专家展示我们的交互式框架，我们收集了关于我们训练模型的反馈，并进一步改进了功能，以提高我们预测的准确性。通过提供来自21个开源程序的定性(基于社区反馈)和定量(基于预测准确性)证据，我们表明我们的严重性预测框架可以有效地帮助开发人员评估漏洞。
{"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}
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