Tim Nelson, Natasha Danas, Daniel J. Dougherty, S. Krishnamurthi
Scenario-finding tools like the Alloy Analyzer are widely used in numerous concrete domains like security, network analysis, UML analysis, and so on. They can help to verify properties and, more generally, aid in exploring a system's behavior. While scenario finders are valuable for their ability to produce concrete examples, individual scenarios only give insight into what is possible, leaving the user to make their own conclusions about what might be necessary. This paper enriches scenario finding by allowing users to ask ``why?'' and ``why not?'' questions about the examples they are given. We show how to distinguish parts of an example that cannot be consistently removed (or changed) from those that merely reflect underconstraint in the specification. In the former case we show how to determine which elements of the specification and which other components of the example together explain the presence of such facts. This paper formalizes the act of computing provenance in scenario-finding. We present Amalgam, an extension of the popular Alloy scenario-finder, which implements these foundations and provides interactive exploration of examples. We also evaluate Amalgam's algorithmics on a variety of both textbook and real-world examples.
{"title":"The power of \"why\" and \"why not\": enriching scenario exploration with provenance","authors":"Tim Nelson, Natasha Danas, Daniel J. Dougherty, S. Krishnamurthi","doi":"10.1145/3106237.3106272","DOIUrl":"https://doi.org/10.1145/3106237.3106272","url":null,"abstract":"Scenario-finding tools like the Alloy Analyzer are widely used in numerous concrete domains like security, network analysis, UML analysis, and so on. They can help to verify properties and, more generally, aid in exploring a system's behavior. While scenario finders are valuable for their ability to produce concrete examples, individual scenarios only give insight into what is possible, leaving the user to make their own conclusions about what might be necessary. This paper enriches scenario finding by allowing users to ask ``why?'' and ``why not?'' questions about the examples they are given. We show how to distinguish parts of an example that cannot be consistently removed (or changed) from those that merely reflect underconstraint in the specification. In the former case we show how to determine which elements of the specification and which other components of the example together explain the presence of such facts. This paper formalizes the act of computing provenance in scenario-finding. We present Amalgam, an extension of the popular Alloy scenario-finder, which implements these foundations and provides interactive exploration of examples. We also evaluate Amalgam's algorithmics on a variety of both textbook and real-world examples.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127585811","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}
Diego Cedrim, Alessandro F. Garcia, Melina Mongiovi, Rohit Gheyi, L. Sousa, R. Mello, B. Neto, Márcio Ribeiro, Alexander Chávez
Code smells in a program represent indications of structural quality problems, which can be addressed by software refactoring. However, refactoring intends to achieve different goals in practice, and its application may not reduce smelly structures. Developers may neglect or end up creating new code smells through refactoring. Unfortunately, little has been reported about the beneficial and harmful effects of refactoring on code smells. This paper reports a longitudinal study intended to address this gap. We analyze how often commonly-used refactoring types affect the density of 13 types of code smells along the version histories of 23 projects. Our findings are based on the analysis of 16,566 refactorings distributed in 10 different types. Even though 79.4% of the refactorings touched smelly elements, 57% did not reduce their occurrences. Surprisingly, only 9.7% of refactorings removed smells, while 33.3% induced the introduction of new ones. More than 95% of such refactoring-induced smells were not removed in successive commits, which suggest refactorings tend to more frequently introduce long-living smells instead of eliminating existing ones. We also characterized and quantified typical refactoring-smell patterns, and observed that harmful patterns are frequent, including: (i) approximately 30% of the Move Method and Pull Up Method refactorings induced the emergence of God Class, and (ii) the Extract Superclass refactoring creates the smell Speculative Generality in 68% of the cases.
{"title":"Understanding the impact of refactoring on smells: a longitudinal study of 23 software projects","authors":"Diego Cedrim, Alessandro F. Garcia, Melina Mongiovi, Rohit Gheyi, L. Sousa, R. Mello, B. Neto, Márcio Ribeiro, Alexander Chávez","doi":"10.1145/3106237.3106259","DOIUrl":"https://doi.org/10.1145/3106237.3106259","url":null,"abstract":"Code smells in a program represent indications of structural quality problems, which can be addressed by software refactoring. However, refactoring intends to achieve different goals in practice, and its application may not reduce smelly structures. Developers may neglect or end up creating new code smells through refactoring. Unfortunately, little has been reported about the beneficial and harmful effects of refactoring on code smells. This paper reports a longitudinal study intended to address this gap. We analyze how often commonly-used refactoring types affect the density of 13 types of code smells along the version histories of 23 projects. Our findings are based on the analysis of 16,566 refactorings distributed in 10 different types. Even though 79.4% of the refactorings touched smelly elements, 57% did not reduce their occurrences. Surprisingly, only 9.7% of refactorings removed smells, while 33.3% induced the introduction of new ones. More than 95% of such refactoring-induced smells were not removed in successive commits, which suggest refactorings tend to more frequently introduce long-living smells instead of eliminating existing ones. We also characterized and quantified typical refactoring-smell patterns, and observed that harmful patterns are frequent, including: (i) approximately 30% of the Move Method and Pull Up Method refactorings induced the emergence of God Class, and (ii) the Extract Superclass refactoring creates the smell Speculative Generality in 68% of the cases.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131390688","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}
Mobile applications, or apps for short, are gaining popularity. The input sources (e.g., touchscreen, sensors, transmitters) of the smart devices that host these apps enable the apps to offer a rich experience to the users, but these input sources pose testing complications to the developers (e.g., writing tests to accurately utilize multiple input sources together and be able to replay such tests at a later time). To alleviate these complications, researchers and practitioners in recent years have developed a variety of record-and-replay tools to support the testing expressiveness of smart devices. These tools allow developers to easily record and automate the replay of complicated usage scenarios of their app. Due to Android's large share of the smart-device market, numerous record-and-replay tools have been developed using a variety of techniques to test Android apps. To better understand the strengths and weaknesses of these tools, we present a comparison of popular record-and-replay tools from researchers and practitioners, by applying these tools to test three popular industrial apps downloaded from the Google Play store. Our comparison is based on three main metrics: (1) ability to reproduce common usage scenarios, (2) space overhead of traces created by the tools, and (3) robustness of traces created by the tools (when being replayed on devices with different resolutions). The results from our comparison show which record-and-replay tools may be the best for developers and identify future directions for improving these tools to better address testing complications of smart devices.
{"title":"Record and replay for Android: are we there yet in industrial cases?","authors":"Wing Lam, Zhengkai Wu, Dengfeng Li, Wenyu Wang, Haibing Zheng, Hui Luo, Peng Yan, Yuetang Deng, Tao Xie","doi":"10.1145/3106237.3117769","DOIUrl":"https://doi.org/10.1145/3106237.3117769","url":null,"abstract":"Mobile applications, or apps for short, are gaining popularity. The input sources (e.g., touchscreen, sensors, transmitters) of the smart devices that host these apps enable the apps to offer a rich experience to the users, but these input sources pose testing complications to the developers (e.g., writing tests to accurately utilize multiple input sources together and be able to replay such tests at a later time). To alleviate these complications, researchers and practitioners in recent years have developed a variety of record-and-replay tools to support the testing expressiveness of smart devices. These tools allow developers to easily record and automate the replay of complicated usage scenarios of their app. Due to Android's large share of the smart-device market, numerous record-and-replay tools have been developed using a variety of techniques to test Android apps. To better understand the strengths and weaknesses of these tools, we present a comparison of popular record-and-replay tools from researchers and practitioners, by applying these tools to test three popular industrial apps downloaded from the Google Play store. Our comparison is based on three main metrics: (1) ability to reproduce common usage scenarios, (2) space overhead of traces created by the tools, and (3) robustness of traces created by the tools (when being replayed on devices with different resolutions). The results from our comparison show which record-and-replay tools may be the best for developers and identify future directions for improving these tools to better address testing complications of smart devices.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131742017","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}
Ahmet Çelik, Marko Vasic, Aleksandar Milicevic, Miloš Gligorić
Modern software development processes recommend that changes be integrated into the main development line of a project multiple times a day. Before a new revision may be integrated, developers practice regression testing to ensure that the latest changes do not break any previously established functionality. The cost of regression testing is high, due to an increase in the number of revisions that are introduced per day, as well as the number of tests developers write per revision. Regression test selection (RTS) optimizes regression testing by skipping tests that are not affected by recent project changes. Existing dynamic RTS techniques support only projects written in a single programming language, which is unfortunate knowing that an open-source project is on average written in several programming languages. We present the first dynamic RTS technique that does not stop at predefined language boundaries. Our technique dynamically detects, at the operating system level, all file artifacts a test depends on. Our technique is, hence, oblivious to the specific means the test uses to actually access the files: be it through spawning a new process, invoking a system call, invoking a library written in a different language, invoking a library that spawns a process which makes a system call, etc. We also provide a set of extension points which allow for a smooth integration with testing frameworks and build systems. We implemented our technique in a tool called RTSLinux as a loadable Linux kernel module and evaluated it on 21 Java projects that escape JVM by spawning new processes or invoking native code, totaling 2,050,791 lines of code. Our results show that RTSLinux, on average, skips 74.17% of tests and saves 52.83% of test execution time compared to executing all tests.
{"title":"Regression test selection across JVM boundaries","authors":"Ahmet Çelik, Marko Vasic, Aleksandar Milicevic, Miloš Gligorić","doi":"10.1145/3106237.3106297","DOIUrl":"https://doi.org/10.1145/3106237.3106297","url":null,"abstract":"Modern software development processes recommend that changes be integrated into the main development line of a project multiple times a day. Before a new revision may be integrated, developers practice regression testing to ensure that the latest changes do not break any previously established functionality. The cost of regression testing is high, due to an increase in the number of revisions that are introduced per day, as well as the number of tests developers write per revision. Regression test selection (RTS) optimizes regression testing by skipping tests that are not affected by recent project changes. Existing dynamic RTS techniques support only projects written in a single programming language, which is unfortunate knowing that an open-source project is on average written in several programming languages. We present the first dynamic RTS technique that does not stop at predefined language boundaries. Our technique dynamically detects, at the operating system level, all file artifacts a test depends on. Our technique is, hence, oblivious to the specific means the test uses to actually access the files: be it through spawning a new process, invoking a system call, invoking a library written in a different language, invoking a library that spawns a process which makes a system call, etc. We also provide a set of extension points which allow for a smooth integration with testing frameworks and build systems. We implemented our technique in a tool called RTSLinux as a loadable Linux kernel module and evaluated it on 21 Java projects that escape JVM by spawning new processes or invoking native code, totaling 2,050,791 lines of code. Our results show that RTSLinux, on average, skips 74.17% of tests and saves 52.83% of test execution time compared to executing all tests.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130271746","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}
Continuous Integration (CI) is a hot topic. Yet, little attention is payed to how CI systems work and what impacts their behavior. In parallel, bug prediction in software is gaining high attention. But this is done mostly in the context of software engineering, and the relation to the realm of CI and CI systems engineering has not been established yet. In this paper we describe how Project Gating systems operate, which are a specific type of CI systems used to keep the mainline of development always clean. We propose and evaluate three heuristics for improving Gating performance and demonstrate their trade-offs. The third heuristic, which leverages state-of-the-art bug prediction achieves the best performance across the entire spectrum of workload conditions.
{"title":"Screening heuristics for project gating systems","authors":"Zahy Volf, Edi Shmueli","doi":"10.1145/3106237.3117766","DOIUrl":"https://doi.org/10.1145/3106237.3117766","url":null,"abstract":"Continuous Integration (CI) is a hot topic. Yet, little attention is payed to how CI systems work and what impacts their behavior. In parallel, bug prediction in software is gaining high attention. But this is done mostly in the context of software engineering, and the relation to the realm of CI and CI systems engineering has not been established yet. In this paper we describe how Project Gating systems operate, which are a specific type of CI systems used to keep the mainline of development always clean. We propose and evaluate three heuristics for improving Gating performance and demonstrate their trade-offs. The third heuristic, which leverages state-of-the-art bug prediction achieves the best performance across the entire spectrum of workload conditions.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"229 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133199026","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}
Decompiled code lacks meaningful variable names. We used statistical machine translation to suggest variable names that are natural given the context. This technique has previously been successfully applied to obfuscated JavaScript code, but decompiled C code poses unique challenges in constructing an aligned corpus and selecting the best translation from among several candidates.
{"title":"Suggesting meaningful variable names for decompiled code: a machine translation approach","authors":"A. Jaffe","doi":"10.1145/3106237.3121274","DOIUrl":"https://doi.org/10.1145/3106237.3121274","url":null,"abstract":"Decompiled code lacks meaningful variable names. We used statistical machine translation to suggest variable names that are natural given the context. This technique has previously been successfully applied to obfuscated JavaScript code, but decompiled C code poses unique challenges in constructing an aligned corpus and selecting the best translation from among several candidates.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126519155","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}
It is challenging to verify regular properties of programs. This paper presents symbolic regular verification (SRV), a dynamic symbolic execution based technique for verifying regular properties. The key technique of SRV is a novel synergistic combination of property-oriented path slicing and guiding to mitigate the path explosion problem. Indeed, slicing can prune redundant paths, while guiding can boost the finding of counterexamples. We have implemented SRV for Java and evaluated it on 16 real-world open-source Java programs (totaling 270K lines of code). The experimental results demonstrate the effectiveness and efficiency of SRV.
{"title":"Practical symbolic verification of regular properties","authors":"Hengbiao Yu","doi":"10.1145/3106237.3121275","DOIUrl":"https://doi.org/10.1145/3106237.3121275","url":null,"abstract":"It is challenging to verify regular properties of programs. This paper presents symbolic regular verification (SRV), a dynamic symbolic execution based technique for verifying regular properties. The key technique of SRV is a novel synergistic combination of property-oriented path slicing and guiding to mitigate the path explosion problem. Indeed, slicing can prune redundant paths, while guiding can boost the finding of counterexamples. We have implemented SRV for Java and evaluated it on 16 real-world open-source Java programs (totaling 270K lines of code). The experimental results demonstrate the effectiveness and efficiency of SRV.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134491430","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}
Code reuse is traditionally seen as good practice. Recent trends have pushed the idea of code reuse to an extreme, by using packages that implement simple and trivial tasks, which we call ‘trivial packages’. A recent incident where a trivial package led to the breakdown of some of the most popular web applications such as Facebook and Netflix, put the spotlight on whether using trivial packages should be encouraged. Therefore, in this research, we mine more than 230,000 npm packages and 38,000 JavaScript projects in order to study the prevalence of trivial packages. We found that trivial packages are common, making up 16.8% of the studied npm packages. We performed a survey with 88 Node.js developers who use trivial packages to understand the reasons for and drawbacks of their use. We found that trivial packages are used because they are perceived to be well-implemented and tested pieces of code. However, developers are concerned about maintaining and the risks of breakages due to the extra dependencies trivial packages introduce.
{"title":"Reasons and drawbacks of using trivial npm packages: the developers' perspective","authors":"Rabe Abdalkareem","doi":"10.1145/3106237.3121278","DOIUrl":"https://doi.org/10.1145/3106237.3121278","url":null,"abstract":"Code reuse is traditionally seen as good practice. Recent trends have pushed the idea of code reuse to an extreme, by using packages that implement simple and trivial tasks, which we call ‘trivial packages’. A recent incident where a trivial package led to the breakdown of some of the most popular web applications such as Facebook and Netflix, put the spotlight on whether using trivial packages should be encouraged. Therefore, in this research, we mine more than 230,000 npm packages and 38,000 JavaScript projects in order to study the prevalence of trivial packages. We found that trivial packages are common, making up 16.8% of the studied npm packages. We performed a survey with 88 Node.js developers who use trivial packages to understand the reasons for and drawbacks of their use. We found that trivial packages are used because they are perceived to be well-implemented and tested pieces of code. However, developers are concerned about maintaining and the risks of breakages due to the extra dependencies trivial packages introduce.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132314343","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}
Yamilet R. Serrano Llerena, Guoxin Su, David S. Rosenblum
Probabilistic model checking is a formal verification technique that has been applied successfully in a variety of domains, providing identification of system errors through quantitative verification of stochastic system models. One domain that can benefit from probabilistic model checking is cloud computing, which must provide highly reliable and secure computational and storage services to large numbers of mission-critical software systems. For real-world domains like cloud computing, external system factors and environmental changes must be estimated accurately in the form of probabilities in system models; inaccurate estimates for the model probabilities can lead to invalid verification results. To address the effects of uncertainty in probability estimates, in previous work we have developed a variety of techniques for perturbation analysis of discrete- and continuous-time Markov chains (DTMCs and CTMCs). These techniques determine the consequences of the uncertainty on verification of system properties. In this paper, we present the first approach for perturbation analysis of Markov decision processes (MDPs), a stochastic formalism that is especially popular due to the significant expressive power it provides through the combination of both probabilistic and nondeterministic choice. Our primary contribution is a novel technique for efficiently analyzing the effects of perturbations of model probabilities on verification of reachability properties of MDPs. The technique heuristically explores the space of adversaries of an MDP, which encode the different ways of resolving the MDP's nondeterministic choices. We demonstrate the practical effectiveness of our approach by applying it to two case studies of cloud systems.
{"title":"Probabilistic model checking of perturbed MDPs with applications to cloud computing","authors":"Yamilet R. Serrano Llerena, Guoxin Su, David S. Rosenblum","doi":"10.1145/3106237.3106301","DOIUrl":"https://doi.org/10.1145/3106237.3106301","url":null,"abstract":"Probabilistic model checking is a formal verification technique that has been applied successfully in a variety of domains, providing identification of system errors through quantitative verification of stochastic system models. One domain that can benefit from probabilistic model checking is cloud computing, which must provide highly reliable and secure computational and storage services to large numbers of mission-critical software systems. For real-world domains like cloud computing, external system factors and environmental changes must be estimated accurately in the form of probabilities in system models; inaccurate estimates for the model probabilities can lead to invalid verification results. To address the effects of uncertainty in probability estimates, in previous work we have developed a variety of techniques for perturbation analysis of discrete- and continuous-time Markov chains (DTMCs and CTMCs). These techniques determine the consequences of the uncertainty on verification of system properties. In this paper, we present the first approach for perturbation analysis of Markov decision processes (MDPs), a stochastic formalism that is especially popular due to the significant expressive power it provides through the combination of both probabilistic and nondeterministic choice. Our primary contribution is a novel technique for efficiently analyzing the effects of perturbations of model probabilities on verification of reachability properties of MDPs. The technique heuristically explores the space of adversaries of an MDP, which encode the different ways of resolving the MDP's nondeterministic choices. We demonstrate the practical effectiveness of our approach by applying it to two case studies of cloud systems.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114975763","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}
This paper presents the framework based on the emulator QEMU. Our framework provides set of multi-platform analysis tools for the virtual machines and mechanism for creating instrumentation and analysis tools. Our framework is based on a lightweight approach to dynamic analysis of binary code executed in virtual machines. This approach is non-intrusive and provides system-wide analysis capabilities. It does not require loading any guest agents and source code of the OS. Therefore it may be applied to ROM-based guest systems and enables using of record/replay of the system execution. We use application binary interface (ABI) of the platform to be analyzed for creating introspection tools. These tools recover the part of kernel-level information related to the system calls executed on the guest machine.
{"title":"QEMU-based framework for non-intrusive virtual machine instrumentation and introspection","authors":"P. Dovgalyuk, N. Fursova, I. Vasiliev, V. Makarov","doi":"10.1145/3106237.3122817","DOIUrl":"https://doi.org/10.1145/3106237.3122817","url":null,"abstract":"This paper presents the framework based on the emulator QEMU. Our framework provides set of multi-platform analysis tools for the virtual machines and mechanism for creating instrumentation and analysis tools. Our framework is based on a lightweight approach to dynamic analysis of binary code executed in virtual machines. This approach is non-intrusive and provides system-wide analysis capabilities. It does not require loading any guest agents and source code of the OS. Therefore it may be applied to ROM-based guest systems and enables using of record/replay of the system execution. We use application binary interface (ABI) of the platform to be analyzed for creating introspection tools. These tools recover the part of kernel-level information related to the system calls executed on the guest machine.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130476422","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：