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Since its inception in 2011, Elixir has emerged as a popular programming language. Currently, Elixir is used in a diverse set of domains, such as instant messaging, smart farming, and e-commerce. Usage of Elixir in above-mentioned domains necessitates gaining an understanding of the state of vulnerabilities that are reported for Elixir programs. An empirical analysis of vulnerability-related commits, i.e., commits that indicate action taken to mitigate vulnerabilities, can help us understand how frequently vulnerabilities appear in Elixir programs. Such understanding can also be a starting point to integrate secure software development practices into the Elixir ecosystem. We conduct an empirical study where we mine 4,446 commits from 25 open source Elixir repositories from GitHub. Our findings show that (i) 2.0% of the 4,446 commits are vulnerability-related, (ii) 18.0% of the 1,769 Elixir programs in our dataset are modified in vulnerability-related commits, and (iii) the proportion of vulnerability-related commits is highest in 2020. Despite Elixir being perceived as a 'safe' language, our empirical study shows programs written in Elixir to contain vulnerabilities. Based on our findings, we recommend researchers to investigate the root causes of introducing vulnerabilities in Elixir programs. 

The programming language Julia is designed to solve the 'two language problem', where developers who write scientific software can achieve desired performance, without sacrificing productivity. Since its inception in 2012, developers who have been using other programming languages have transitioned to Julia. A systematic investigation of the questions that developers ask about Julia can help in understanding the challenges that developers face while using Julia. Such understanding can be helpful (i) for toolsmiths who can construct tools so that developers can maximize their experience of using Julia, and (ii) for Julia language maintainers with empirical evidence on areas to improve the language as well as the Julia ecosystem. We conduct an empirical study with 3,093 Stack Overflow posts where we identify 13 categories of questions related to Julia-based software development. We observe developers to ask about a diverse set of topics, such as GC, Julia's garbage collector, JuMP, a domain-specific language constructed using Julia, and symbols, a metaprogramming utility in Julia. Based on our emerging results, we recommend enhancing support for developers with Julia-based tools and techniques for cross language transfer, type-related assistance, and package resolution. 

The ubiquitous usage of robots in modern society necessitates secure development of robotics systems. Practitioners who engage in robot development can benefit from a systematic study that investigates the categories of vulnerabilities that appear in robotics systems. The goal of this paper is to help practitioners mitigate vulnerabilities in robotics systems by conducting an empirical study of vulnerabilities in robotics systems. We conduct an empirical study where we analyze 176 robotics-related vulnerabilities collected from the Robot Vulnerability Database (RVD). Our findings show that: (i) robotics-related vulnerabilities can be classified into nine categories; (ii) memory-related vulnerabilities are the most frequent category, (iii) 92.6% of the reported vulnerabilities are software-related, and (iv) software components in robotics systems include more critical vulnerabilities compared to that of hardware components. Based on our findings, we provide a list of development activities that can be used to mitigate vulnerabilities for robotics systems.