Search for: All records

In infrastructure as code (IaC), state reconciliation is the process of querying and comparing the infrastructure state prior to changing the infrastructure. As state reconciliation is pivotal to manage IaC-based computing infrastructure at scale, defects related to state reconciliation can create large-scale consequences. A categorization of state reconciliation defects, i.e., defects related to state reconciliation, can aid in understanding the nature of state reconciliation defects. We conduct an empirical study with 5,110 state reconciliation defects where we apply qualitative analysis to categorize state reconciliation defects. From the identified defect categories, we derive heuristics to design prompts for a large language model (LLM), which in turn are used for validation of state reconciliation. From our empirical study, we identify 8 categories of state reconciliation defects, amongst which 3 have not been reported for previously-studied software systems. The most frequently occurring defect category is inventory, i.e., the category of defects that occur when managing infrastructure inventory. Using an LLM with heuristics-based paragraph style prompts, we identify 9 previously unknown state reconciliation defects of which 7 have been accepted as valid defects, and 4 have already been fixed. Based on our findings, we conclude the paper by providing a set of recommendations for researchers and practitioners.

 

Infrastructure as code (IaC) is the practice of automatically managing computing platforms, such as Internet of Things (IoT) platforms. IaC has gained popularity in recent years, yielding a plethora of software artifacts, such as Ansible playbooks that are available on social coding platforms. Despite the availability of open source software (OSS) Ansible playbooks, there is a lack of empirical research on the quality of these playbooks, which can hinder the progress of IaC-related research. To that end, we conduct an empirical study with 2,952 OSS Ansible playbooks where we evaluate the quality of OSS playbooks from the perspective of executability, i.e., if publicly available OSS Ansible playbooks can be executed without failures. From our empirical study, we observe 71.5\% of the mined 2,952 Ansible playbooks cannot be executed as is because of four categories of failures. 

Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, hands-on, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output. 

This paper presents an innovative approach to DevOps security education, addressing the dynamic landscape of cybersecurity threats. We propose a student-centered learning methodology by developing comprehensive hands-on learning modules. Specifically, we introduce labware modules designed to automate static security analysis, empowering learners to identify known vulnerabilities efficiently. These modules offer a structured learning experience with pre-lab, hands-on, and post-lab sections, guiding students through DevOps concepts and security challenges. In this paper, we introduce hands-on learning modules that familiarize students with recognizing known security flaws through the application of Git Hooks. Through practical exercises with real-world code examples containing security flaws, students gain proficiency in detecting vulnerabilities using relevant tools. Initial evaluations conducted across educational institutions indicate that these hands-on modules foster student interest in software security and cybersecurity and equip them with practical skills to address DevOps security vulnerabilities. 

This paper presents an innovative approach to DevOps security education, addressing the dynamic landscape of cybersecurity threats. We propose a student-centered learning methodology by developing comprehensive hands-on learning modules. Specifically, we introduce labware modules designed to automate static security analysis, empowering learners to identify known vulnerabilities efficiently. These modules offer a structured learning experience with pre-lab, hands-on, and post-lab sections, guiding students through DevOps concepts and security challenges. In this paper, we introduce hands-on learning modules that familiarize students with recognizing known security flaws through the application of Git Hooks. Through practical exercises with real-world code examples containing security flaws, students gain proficiency in detecting vulnerabilities using relevant tools. Initial evaluations conducted across educational institutions indicate that these hands-on modules foster student interest in software security and cybersecurity and equip them with practical skills to address DevOps security vulnerabilities. 

Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, handson, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output. 

Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, handson, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output. 

Context:Compilers are the fundamental tools for software development. Thus, compiler defects can disrupt development productivity and propagate errors into developer-written software source code. Categorizing defects in compilers can inform practitioners and researchers about the existing defects in compilers and techniques that can be used to identify defects systematically.

Objective:The goal of this paper is to help researchers understand the nature of defects in compilers by conducting a review of Internet artifacts and peer-reviewed publications that study defect characteristics of compilers.

Methodology:We conduct a multi-vocal literature review (MLR) with 26 publications and 32 Internet artifacts to characterize compiler defects.

Results:From our MLR, we identify 13 categories of defects, amongst which optimization defects have been the most reported defects in our artifacts publications. We observed 15 defect identification techniques tailored for compilers and no single technique identifying all observed defect categories.

Conclusion:Our MLR lays the groundwork for practitioners and researchers to identify defects in compilers systematically.

 

The pivotal role of white-box testing with respect to software quality assurance, necessitates dissemination of education materials related to white-box testing in the course curriculum. In this poster, we describe our experiences in conducting an authentic learning based exercise related to white-box testing. From a conducted survey with 124 students, we observe the authentic learning-based exercise to be helpful for students to learn about white-box testing.