LANGETI '20: "Testing Practices for Infrastructure ..."
Testing Practices for Infrastructure as Code
Mohammed Mehedi Hasan, Farzana Ahamed Bhuiyan, and Akond Rahman
(Independent University, Bangladesh; Tennessee Technological University, USA)
Infrastructure as code (IaC) helps practitioners to rapidly deploy software services to end-users. Despite reported benefits, IaC scripts are susceptible to defects. Defects in IaC scripts can cause serious consequences, for example, creating large-scale outages similar to the Amazon Web Services (AWS) incident in 2017. The prevalence of defects in IaC scripts necessitates practitioners to implement IaC testing and be aware of IaC testing practices. A synthesis of IaC testing practices can enable practitioners in early mitigation of IaC defects and also help researchers to identify potential research avenues. The goal of this paper is to help practitioners improve the quality of infrastructure as code (IaC) scripts by identifying a set of testing practices for IaC scripts. We apply open coding on 50 Internet artifacts, such as blog posts to derive IaC testing practices. We identify six testing practices that include behavior-focused test coverage, the practice of measuring coverage of IaC test cases in terms of expected behavior. We conclude our paper by discussing how practitioners and researchers can leverage our derived list of testing practices for IaC.
@InProceedings{LANGETI20p7,
author = {Mohammed Mehedi Hasan and Farzana Ahamed Bhuiyan and Akond Rahman},
title = {Testing Practices for Infrastructure as Code},
booktitle = {Proc.\ LANGETI},
publisher = {ACM},
pages = {7--12},
doi = {10.1145/3416504.3424334},
year = {2020},
}
Publisher's Version
LANGETI '20: "Testing Multi-tenant Applications ..."
Testing Multi-tenant Applications using Fuzzing and Reinforcement Learning
Ciprian Paduraru, Alin Stefanescu, and Bogdan Ghimis
(University of Bucharest, Romania)
Testing cloud applications has recently gained in importance since many companies migrated their operations in the cloud. To optimise resources, cloud applications may serve several users at once in a so-called multi-tenant setting. We propose a new technique for testing multi-tenant applications using reinforcement learning combined with gray-box fuzzing techniques. A preliminary evaluation using a combination of fuzzing techniques and genetic algorithms is also provided.
@InProceedings{LANGETI20p1,
author = {Ciprian Paduraru and Alin Stefanescu and Bogdan Ghimis},
title = {Testing Multi-tenant Applications using Fuzzing and Reinforcement Learning},
booktitle = {Proc.\ LANGETI},
publisher = {ACM},
pages = {1--6},
doi = {10.1145/3416504.3424333},
year = {2020},
}
Publisher's Version
LANGETI '20: "RIVER 2.0: An Open-Source ..."
RIVER 2.0: An Open-Source Testing Framework using AI Techniques
Bogdan Ghimis, Miruna Paduraru, and Alin Stefanescu
(University of Bucharest, Romania)
This paper presents the latest updates to the RIVER open-source testing platform for x86 programs, focusing on how artificial intelligence (AI) techniques can be used to improve the automated testing processes. It is also important to mention that RIVER is the first open-source platform that offers a concolic execution engine with reinforcement learning capabilities. On the industry side, this can allow security software engineers to test their applications with fewer costs, while for the research community, it can help prototyping new ideas faster. As a secondary contribution, our work makes a summary of the AI techniques that were used for testing processes either in our previous work or other existing work in the field. The presentation describes technical aspects, challenges, and future work.
@InProceedings{LANGETI20p13,
author = {Bogdan Ghimis and Miruna Paduraru and Alin Stefanescu},
title = {RIVER 2.0: An Open-Source Testing Framework using AI Techniques},
booktitle = {Proc.\ LANGETI},
publisher = {ACM},
pages = {13--18},
doi = {10.1145/3416504.3424335},
year = {2020},
}
Publisher's Version
LANGETI '20: "Testing Practices for Infrastructure ..."
Testing Practices for Infrastructure as Code
Mohammed Mehedi Hasan, Farzana Ahamed Bhuiyan, and Akond Rahman
(Independent University, Bangladesh; Tennessee Technological University, USA)
Infrastructure as code (IaC) helps practitioners to rapidly deploy software services to end-users. Despite reported benefits, IaC scripts are susceptible to defects. Defects in IaC scripts can cause serious consequences, for example, creating large-scale outages similar to the Amazon Web Services (AWS) incident in 2017. The prevalence of defects in IaC scripts necessitates practitioners to implement IaC testing and be aware of IaC testing practices. A synthesis of IaC testing practices can enable practitioners in early mitigation of IaC defects and also help researchers to identify potential research avenues. The goal of this paper is to help practitioners improve the quality of infrastructure as code (IaC) scripts by identifying a set of testing practices for IaC scripts. We apply open coding on 50 Internet artifacts, such as blog posts to derive IaC testing practices. We identify six testing practices that include behavior-focused test coverage, the practice of measuring coverage of IaC test cases in terms of expected behavior. We conclude our paper by discussing how practitioners and researchers can leverage our derived list of testing practices for IaC.
@InProceedings{LANGETI20p7,
author = {Mohammed Mehedi Hasan and Farzana Ahamed Bhuiyan and Akond Rahman},
title = {Testing Practices for Infrastructure as Code},
booktitle = {Proc.\ LANGETI},
publisher = {ACM},
pages = {7--12},
doi = {10.1145/3416504.3424334},
year = {2020},
}
Publisher's Version
LANGETI '20: "Testing Multi-tenant Applications ..."
Testing Multi-tenant Applications using Fuzzing and Reinforcement Learning
Ciprian Paduraru, Alin Stefanescu, and Bogdan Ghimis
(University of Bucharest, Romania)
Testing cloud applications has recently gained in importance since many companies migrated their operations in the cloud. To optimise resources, cloud applications may serve several users at once in a so-called multi-tenant setting. We propose a new technique for testing multi-tenant applications using reinforcement learning combined with gray-box fuzzing techniques. A preliminary evaluation using a combination of fuzzing techniques and genetic algorithms is also provided.
@InProceedings{LANGETI20p1,
author = {Ciprian Paduraru and Alin Stefanescu and Bogdan Ghimis},
title = {Testing Multi-tenant Applications using Fuzzing and Reinforcement Learning},
booktitle = {Proc.\ LANGETI},
publisher = {ACM},
pages = {1--6},
doi = {10.1145/3416504.3424333},
year = {2020},
}
Publisher's Version
LANGETI '20: "RIVER 2.0: An Open-Source ..."
RIVER 2.0: An Open-Source Testing Framework using AI Techniques
Bogdan Ghimis, Miruna Paduraru, and Alin Stefanescu
(University of Bucharest, Romania)
This paper presents the latest updates to the RIVER open-source testing platform for x86 programs, focusing on how artificial intelligence (AI) techniques can be used to improve the automated testing processes. It is also important to mention that RIVER is the first open-source platform that offers a concolic execution engine with reinforcement learning capabilities. On the industry side, this can allow security software engineers to test their applications with fewer costs, while for the research community, it can help prototyping new ideas faster. As a secondary contribution, our work makes a summary of the AI techniques that were used for testing processes either in our previous work or other existing work in the field. The presentation describes technical aspects, challenges, and future work.
@InProceedings{LANGETI20p13,
author = {Bogdan Ghimis and Miruna Paduraru and Alin Stefanescu},
title = {RIVER 2.0: An Open-Source Testing Framework using AI Techniques},
booktitle = {Proc.\ LANGETI},
publisher = {ACM},
pages = {13--18},
doi = {10.1145/3416504.3424335},
year = {2020},
}
Publisher's Version
LANGETI '20: "Testing Practices for Infrastructure ..."
Testing Practices for Infrastructure as Code
Mohammed Mehedi Hasan, Farzana Ahamed Bhuiyan, and Akond Rahman
(Independent University, Bangladesh; Tennessee Technological University, USA)
Infrastructure as code (IaC) helps practitioners to rapidly deploy software services to end-users. Despite reported benefits, IaC scripts are susceptible to defects. Defects in IaC scripts can cause serious consequences, for example, creating large-scale outages similar to the Amazon Web Services (AWS) incident in 2017. The prevalence of defects in IaC scripts necessitates practitioners to implement IaC testing and be aware of IaC testing practices. A synthesis of IaC testing practices can enable practitioners in early mitigation of IaC defects and also help researchers to identify potential research avenues. The goal of this paper is to help practitioners improve the quality of infrastructure as code (IaC) scripts by identifying a set of testing practices for IaC scripts. We apply open coding on 50 Internet artifacts, such as blog posts to derive IaC testing practices. We identify six testing practices that include behavior-focused test coverage, the practice of measuring coverage of IaC test cases in terms of expected behavior. We conclude our paper by discussing how practitioners and researchers can leverage our derived list of testing practices for IaC.
@InProceedings{LANGETI20p7,
author = {Mohammed Mehedi Hasan and Farzana Ahamed Bhuiyan and Akond Rahman},
title = {Testing Practices for Infrastructure as Code},
booktitle = {Proc.\ LANGETI},
publisher = {ACM},
pages = {7--12},
doi = {10.1145/3416504.3424334},
year = {2020},
}
Publisher's Version
LANGETI '20: "Testing Multi-tenant Applications ..."
Testing Multi-tenant Applications using Fuzzing and Reinforcement Learning
Ciprian Paduraru, Alin Stefanescu, and Bogdan Ghimis
(University of Bucharest, Romania)
Testing cloud applications has recently gained in importance since many companies migrated their operations in the cloud. To optimise resources, cloud applications may serve several users at once in a so-called multi-tenant setting. We propose a new technique for testing multi-tenant applications using reinforcement learning combined with gray-box fuzzing techniques. A preliminary evaluation using a combination of fuzzing techniques and genetic algorithms is also provided.
@InProceedings{LANGETI20p1,
author = {Ciprian Paduraru and Alin Stefanescu and Bogdan Ghimis},
title = {Testing Multi-tenant Applications using Fuzzing and Reinforcement Learning},
booktitle = {Proc.\ LANGETI},
publisher = {ACM},
pages = {1--6},
doi = {10.1145/3416504.3424333},
year = {2020},
}
Publisher's Version
LANGETI '20: "RIVER 2.0: An Open-Source ..."
RIVER 2.0: An Open-Source Testing Framework using AI Techniques
Bogdan Ghimis, Miruna Paduraru, and Alin Stefanescu
(University of Bucharest, Romania)
This paper presents the latest updates to the RIVER open-source testing platform for x86 programs, focusing on how artificial intelligence (AI) techniques can be used to improve the automated testing processes. It is also important to mention that RIVER is the first open-source platform that offers a concolic execution engine with reinforcement learning capabilities. On the industry side, this can allow security software engineers to test their applications with fewer costs, while for the research community, it can help prototyping new ideas faster. As a secondary contribution, our work makes a summary of the AI techniques that were used for testing processes either in our previous work or other existing work in the field. The presentation describes technical aspects, challenges, and future work.
@InProceedings{LANGETI20p13,
author = {Bogdan Ghimis and Miruna Paduraru and Alin Stefanescu},
title = {RIVER 2.0: An Open-Source Testing Framework using AI Techniques},
booktitle = {Proc.\ LANGETI},
publisher = {ACM},
pages = {13--18},
doi = {10.1145/3416504.3424335},
year = {2020},
}
Publisher's Version