2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)最新文献_第9页

Rotten Apples Spoil the Bunch: An Anatomy of Google Play Malware 烂苹果坏苹果:剖析Google Play恶意软件

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510161

Michael Cao, Khaled Ahmed, J. Rubin

{"title":"Rotten Apples Spoil the Bunch: An Anatomy of Google Play Malware","authors":"Michael Cao, Khaled Ahmed, J. Rubin","doi":"10.1145/3510003.3510161","DOIUrl":"https://doi.org/10.1145/3510003.3510161","url":null,"abstract":"This paper provides an in-depth analysis of Android malware that bypassed the strictest defenses of the Google Play application store and penetrated the official Android market between January 2016 and July 2021. We systematically identified 1,238 such malicious applications, grouped them into 134 families, and manually analyzed one application from 105 distinct families. During our manual analysis, we identified malicious payloads the applications execute, conditions guarding execution of the payloads, hiding techniques applications employ to evade detection by the user, and other implementation-level properties relevant for automated malware detection. As most applications in our dataset contain multiple payloads, each triggered via its own complex activation logic, we also contribute a graph-based representation showing activation paths for all application payloads in form of a control- and data-flow graph. Furthermore, we discuss the capabilities of existing malware detection tools, put them in context of the properties observed in the analyzed malware, and identify gaps and future research directions. We believe that our detailed analysis of the recent, evasive malware will be of interest to researchers and practitioners and will help further improve malware detection tools.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131721067","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: 4

Nufix: Escape From NuGet Dependency Maze Nufix:逃离NuGet依赖迷宫

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510118

Zhenming Li, Ying Wang, Zeqi Lin, S. Cheung, Jian-Guang Lou

{"title":"Nufix: Escape From NuGet Dependency Maze","authors":"Zhenming Li, Ying Wang, Zeqi Lin, S. Cheung, Jian-Guang Lou","doi":"10.1145/3510003.3510118","DOIUrl":"https://doi.org/10.1145/3510003.3510118","url":null,"abstract":"Developers usually suffer from dependency maze (DM) issues, i.e., package dependency constraints are violated when a project's platform or dependencies are changed. This problem is especially serious in. NET ecosystem due to its fragmented platforms (e.g.,. NET Framework,. NET Core, and. NET Standard). Fixing DM issues is challenging due to the complexity of dependency constraints: multiple DM issues often occur in one project; solving one DM issue usually causes another DM issue cropping up; the exponential search space of possible dependency combinations is also a barrier. In this paper, we aim to help. NET developers tackle the DM issues. First, we empirically studied a set of real DM issues, learning their common fixing strategies and developers' preferences in adopting these strategies. Based on these findings, we propose NuFIX, an automated technique to repair DM issues. NUFIX formulates the repair task as a binary integer linear optimization problem to effectively derive an optimal fix in line with the learnt developers' preferences. The experiment results and expert validation show that NUFIX can generate high-quality fixes for all the DM issues with 262 popular. NET projects. Encouragingly, 20 projects (including affected projects such as Dropbox) have approved and merged our generated fixes, and shown great interests in our technique.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122968997","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: 4

Analyzing User Perspectives on Mobile App Privacy at Scale 大规模分析用户对移动应用隐私的看法

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510079

Preksha Nema, Pauline Anthonysamy, N. Taft, Sai Teia Peddinti

{"title":"Analyzing User Perspectives on Mobile App Privacy at Scale","authors":"Preksha Nema, Pauline Anthonysamy, N. Taft, Sai Teia Peddinti","doi":"10.1145/3510003.3510079","DOIUrl":"https://doi.org/10.1145/3510003.3510079","url":null,"abstract":"In this paper we present a methodology to analyze users‘ con-cerns and perspectives about privacy at scale. We leverage NLP techniques to process millions of mobile app reviews and extract privacy concerns. Our methodology is composed of a binary clas-sifier that distinguishes between privacy and non-privacy related reviews. We use clustering to gather reviews that discuss similar privacy concerns, and employ summarization metrics to extract representative reviews to summarize each cluster. We apply our methods on 287M reviews for about 2M apps across the 29 cate-gories in Google Play to identify top privacy pain points in mobile apps. We identified approximately 440K privacy related reviews. We find that privacy related reviews occur in all 29 categories, with some issues arising across numerous app categories and other issues only surfacing in a small set of app categories. We show empirical evidence that confirms dominant privacy themes - concerns about apps requesting unnecessary permissions, collection of personal information, frustration with privacy controls, tracking and the selling of personal data. As far as we know, this is the first large scale analysis to confirm these findings based on hundreds of thousands of user inputs. We also observe some unexpected findings such as users warning each other not to install an app due to privacy issues, users uninstalling apps due to privacy reasons, as well as positive reviews that reward developers for privacy friendly apps. Finally we discuss the implications of our method and findings for developers and app stores.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121607156","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: 8

GraphFuzz: Library API Fuzzing with Lifetime-aware Dataflow Graphs GraphFuzz:具有生命周期感知的数据流图的库API模糊测试

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510228

Harrison Green, Thanassis Avgerinos

{"title":"GraphFuzz: Library API Fuzzing with Lifetime-aware Dataflow Graphs","authors":"Harrison Green, Thanassis Avgerinos","doi":"10.1145/3510003.3510228","DOIUrl":"https://doi.org/10.1145/3510003.3510228","url":null,"abstract":"We present the design and implementation of GraphFuzz, a new structure-, coverage- and object lifetime-aware fuzzer capable of automatically testing low-level Library APIs. Unlike other fuzzers, GraphFuzz models sequences of executed functions as a dataflow graph, thus enabling it to perform graph-based mutations both at the data and at the execution trace level. GraphFuzz comes with an automated specification generator to minimize the developer integration effort. We use GraphFuzz to analyze Skia-the rigorously tested Google Chrome graphics library-and benchmark GraphFuzz-generated fuzzing harnesses against hand-optimized, painstakingly written libFuzzer harnesses. We find that GraphFuzz generates test cases that achieve 2-3x more code coverage on average with minimal development effort, and also uncovered previous unknown defects in the process. We demonstrate GraphFuzz's applicability on low-level APIs by analyzing four additional open-source libraries and finding dozens of previously unknown defects. All security relevant findings have already been reported and fixed by the developers. Last, we open-source GraphFuzz under a permissive license and provide code to reproduce all results in this paper.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117270526","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: 5

The Extent of Orphan Vulnerabilities from Code Reuse in Open Source Software 开源软件中代码重用导致的孤立漏洞的范围

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510216

David Reid, Mahmoud Jahanshahi, A. Mockus

{"title":"The Extent of Orphan Vulnerabilities from Code Reuse in Open Source Software","authors":"David Reid, Mahmoud Jahanshahi, A. Mockus","doi":"10.1145/3510003.3510216","DOIUrl":"https://doi.org/10.1145/3510003.3510216","url":null,"abstract":"Motivation: A key premise of open source software is the ability to copy code to other open source projects (white-box reuse). Such copying accelerates development of new projects, but the code flaws in the original projects, such as vulnerabilities, may also spread even if fixed in the projects from where the code was appropriated. The extent of the spread of vulnerabilities through code reuse, the potential impact of such spread, or avenues for mitigating risk of these secondary vulnerabilities has not been studied in the context of a nearly complete collection of open source code. Aim: We aim to find ways to detect the white-box reuse induced vulnerabilities, determine how prevalent they are, and explore how they may be addressed. Method: We rely on World of Code infrastructure that provides a curated and cross-referenced collection of nearly all open source software to conduct a case study of a few known vulnerabilities. To conduct our case study we develop a tool, VDiOS, to help identify and fix white-box-reuse-induced vulnerabilities that have been already patched in the original projects (orphan vulnerabilities). Results: We find numerous instances of orphan vulnerabilities even in currently active and in highly popular projects (over 1K stars). Even apparently inactive projects are still publicly available for others to use and spread the vulnerability further. The often long delay in fixing orphan vulnerabilities even in highly popular projects increases the chances of it spreading to new projects. We provided patches to a number of project maintainers and found that only a small percentage accepted and applied the patch. We hope that VDiOS will lead to further study and mitigation of risks from orphan vulnerabilities and other orphan code flaws.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"34 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116189254","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: 4

Quantifying Permissiveness of Access Control Policies 量化访问控制策略的权限

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510233

William Eiers, G. Sankaran, Albert Li, Emily O'Mahony, Benjamin Prince, T. Bultan

{"title":"Quantifying Permissiveness of Access Control Policies","authors":"William Eiers, G. Sankaran, Albert Li, Emily O'Mahony, Benjamin Prince, T. Bultan","doi":"10.1145/3510003.3510233","DOIUrl":"https://doi.org/10.1145/3510003.3510233","url":null,"abstract":"Due to ubiquitous use of software services, protecting the confidentiality of private information stored in compute clouds is becoming an increasingly critical problem. Although access control specification languages and libraries provide mechanisms for protecting confidentiality of information, without verification and validation techniques that can assist developers in writing policies, complex policy specifications are likely to have errors that can lead to unintended and unauthorized access to data, possibly with disastrous consequences. In this paper, we present a quantitative and differential policy analysis framework that not only identifies if one policy is more permissive than another policy, but also quantifies the relative permissiveness of access control policies. We quantify permissiveness of policies using a model counting constraint solver. We present a heuristic that transforms constraints extracted from access control policies and significantly improves the model counting performance. We demonstrate the effectiveness of our approach by applying it to policies written in Amazon's AWS Identity and Access Management (IAM) policy language and Microsoft's Azure policy language.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116655750","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: 5

Towards language-independent Brown Build Detection 走向与语言无关的棕色构建检测

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510122

Doriane Olewicki, Mathieu Nayrolles, Bram Adams

{"title":"Towards language-independent Brown Build Detection","authors":"Doriane Olewicki, Mathieu Nayrolles, Bram Adams","doi":"10.1145/3510003.3510122","DOIUrl":"https://doi.org/10.1145/3510003.3510122","url":null,"abstract":"In principle, continuous integration (CI) practices allow modern software organizations to build and test their products after each code change to detect quality issues as soon as possible. In reality, issues with the build scripts (e.g., missing dependencies) and/or the presence of “flaky tests” lead to build failures that essentially are false positives, not indicative of actual quality problems of the source code. For our industrial partner, which is active in the video game industry, such “brown builds” not only require multidisci-plinary teams to spend more effort interpreting or even re-running the build, leading to substantial redundant build activity, but also slows down the integration pipeline. Hence, this paper aims to prototype and evaluate approaches for early detection of brown build results based on textual similarity to build logs of prior brown builds. The approach is tested on 7 projects (6 closed-source from our industrial collaborators and 1 open-source, Graphviz). We find that our model manages to detect brown builds with a mean F1-score of 53% on the studied projects, which is three times more than the best baseline considered, and at least as good as human experts (but with less effort). Furthermore, we found that cross-project prediction can be used for a project's onboarding phase, that a training set of 30-weeks works best, and that our retraining heuristics keep the F1-score higher than the baseline, while retraining only every 4–5 weeks.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126701826","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: 3

SYMTUNER: Maximizing the Power of Symbolic Execution by Adaptively Tuning External Parameters 通过自适应调整外部参数来最大化符号执行的能力

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510185

Sooyoung Cha, Myungho Lee, Seokhyun Lee, Hakjoo Oh

{"title":"SYMTUNER: Maximizing the Power of Symbolic Execution by Adaptively Tuning External Parameters","authors":"Sooyoung Cha, Myungho Lee, Seokhyun Lee, Hakjoo Oh","doi":"10.1145/3510003.3510185","DOIUrl":"https://doi.org/10.1145/3510003.3510185","url":null,"abstract":"We present SYMTUNER, a novel technique to automatically tune external parameters of symbolic execution. Practical symbolic execution tools have important external parameters (e.g., symbolic arguments, seed input) that critically affect their performance. Due to the huge parameter space, however, manually customizing those parameters is notoriously difficult even for experts. As a consequence, symbolic execution tools have typically been used in a suboptimal manner that, for example, simply relies on the default parameter settings of the tools and loses the opportunity for better performance. In this paper, we aim to change this situation by automatically configuring symbolic execution parameters. With Symtuner that takes parameter spaces to be tuned, symbolic executors are run without manual parameter configurations; instead, appropriate parameter values are learned and adjusted during symbolic execution. To achieve this, we present a learning algorithm that observes the behavior of symbolic execution and accordingly updates the sampling probability of each parameter space. We evaluated Symtuner with KLEE on 12 open-source C programs. The results show that Symtuner increases branch coverage of KLEE by 56% on average and finds 8 more bugs than KLEE with its default parameters over the latest releases of the programs.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114988791","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: 0

Multi-Intention-Aware Configuration Selection for Performance Tuning 性能调优的多意图感知配置选择

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510094

Haochen He, Zhouyang Jia, Shanshan Li, Yue Yu, Chenglong Zhou, Qing Liao, Ji Wang, Xiangke Liao

{"title":"Multi-Intention-Aware Configuration Selection for Performance Tuning","authors":"Haochen He, Zhouyang Jia, Shanshan Li, Yue Yu, Chenglong Zhou, Qing Liao, Ji Wang, Xiangke Liao","doi":"10.1145/3510003.3510094","DOIUrl":"https://doi.org/10.1145/3510003.3510094","url":null,"abstract":"Automatic configuration tuning helps users who intend to improve software performance. However, the auto-tuners are limited by the huge configuration search space. More importantly, they fo-cus only on performance improvement while being unaware of other important user intentions (e.g., reliability, security). To re-duce the search space, researchers mainly focus on pre-selecting performance-related parameters which requires a heavy stage of dynamically running under different configurations to build per-formance models. Given that other important user intentions are not paid attention to, we focus on guiding users in pre-selecting performance-related parameters in general while warning about side-effects on non-performance intentions. We find that the con-figuration document often, if it does not always, contains rich in-formation about the parameters' relationship with diverse user intentions, but documents might also be long and domain-specific. In this paper, we first conduct a comprehensive study on 13 representative software containing 7,349 configuration parame-ters, and derive six types of ways in which configuration parame-ters may affect non-performance intentions. Guided by this study, we design SAFETUNE, a multi-intention-aware method that pre-selects important performance-related parameters and warns about their side-effects on non-performance intentions. Evaluation on target software shows that SAFETUNE correctly identifies 22–26 performance-related parameters that are missed by state-of-the-art tools but have significant performance impact (up to 14.7x). Furthermore, we illustrate eight representative cases to show that SAFETUNE can effectively prevent real-world and critical side-effects on other user intentions.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122688274","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: 8

Automated Handling of Anaphoric Ambiguity in Requirements: A Multi-solution Study 需求中回指歧义的自动处理:一个多解决方案的研究

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510157

Saad Ezzini, Sallam Abualhaija, Chetan Arora, M. Sabetzadeh

{"title":"Automated Handling of Anaphoric Ambiguity in Requirements: A Multi-solution Study","authors":"Saad Ezzini, Sallam Abualhaija, Chetan Arora, M. Sabetzadeh","doi":"10.1145/3510003.3510157","DOIUrl":"https://doi.org/10.1145/3510003.3510157","url":null,"abstract":"Ambiguity is a pervasive issue in natural-language requirements. A common source of ambiguity in requirements is when a pronoun is anaphoric. In requirements engineering, anaphoric ambiguity occurs when a pronoun can plausibly refer to different entities and thus be interpreted differently by different readers. In this paper, we develop an accurate and practical automated approach for handling anaphoric ambiguity in requirements, addressing both ambiguity detection and anaphora interpretation. In view of the multiple competing natural language processing (NLP) and machine learning (ML) technologies that one can utilize, we simultaneously pursue six alternative solutions, empirically assessing each using a col-lection of ≈1,350 industrial requirements. The alternative solution strategies that we consider are natural choices induced by the existing technologies; these choices frequently arise in other automation tasks involving natural-language requirements. A side-by-side em-pirical examination of these choices helps develop insights about the usefulness of different state-of-the-art NLP and ML technologies for addressing requirements engineering problems. For the ambigu-ity detection task, we observe that supervised ML outperforms both a large-scale language model, SpanBERT (a variant of BERT), as well as a solution assembled from off-the-shelf NLP coreference re-solvers. In contrast, for anaphora interpretation, SpanBERT yields the most accurate solution. In our evaluation, (1) the best solution for anaphoric ambiguity detection has an average precision of ≈60% and a recall of 100%, and (2) the best solution for anaphora interpretation (resolution) has an average success rate of ≈98%.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"19 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114124216","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: 16