The study of bugs can provide important information to understand their nature in the context of complex software systems as well as supporting developers in their detection, fix and prevention. Previous studies focused on analyzing bugs under different perspectives such as changes at code level, frequency, semantics, symptoms, root causes and reproducibility through test cases. Although these studies offer valid methodologies that can be applied in different areas of bugs analysis, literature suggests that very little focus has been aimed towards configuration management systems.

This research has the goal to provide a characterization of bugs in the Moby configuration management system, an open framework used to create container systems. A random sample of 100 collected Moby bugs is manually inspected and categorized by their (1) symptoms, (2) root causes, (3) impact, (4) fixes, (5) system dependency and (6) triggers. Some representative takeaways suggest that: bugs symptoms are mostly linked to a specific root cause, which means that bugs occur in certain areas of the system; the modular nature of Moby drives up the criticality of the bugs as each component needs to work correctly; bugs are overall hard to reproduce as only 24.5\% of the fixes included a test case; developing an automatic tool that provides a historical distribution of bugs can support maintainers in their work by enhancing bugs prevention.

The research provides an analysis and categorization of bugs in the Moby configuration management system. The work adds a new perspective to the literature on the topics of both bugs analysis and configuration management systems and can be used as a starting point for further studies.

Research that focuses on examining software bugs is critical when developing tools for preventing and for fixing software issues. Previous work in this area has explored other types of systems, such as bugs of compilers and security issues stemming from open source systems hosted on public repositories. This paper explores the bugs within the Ansible software provisioning and configuration management system. The main question this paper seeks to answer is "What common patterns can be extracted from the bugs found and what are the root causes, symptoms, triggers, system-dependence factors, fixes, and the impact of the most frequent types of bugs in the Ansible configuration management system". This study defines a data pipeline and custom tools to extract and analyze 100 Ansible bugs. Common classifications are determined, and the bugs are manually classified, revealing common patters within the bugs. Insights are drawn from the aggregated data, and recommendations are made for addressing bug-prone areas of execution and connectivity components, and expanding the test suite with input fuzzing and a genetic algorithms test solution, in order to improve the overall code quality of the Ansible code base.

This research studies the symptoms, root causes, impact, triggers, fixes, and system dependency of bugs in the Puppet configuration management system. Puppet is a widely used open-source configuration management system that performs various administrative tasks on machines based on a central specification. This paper aims to fill the research gap and help improve the understanding of these systems. 2146 registered bugs and fixes are collected and a random sample of 100 is analyzed to answer the research questions. The most common bug types have the symptom of unexpected runtime behavior. The root cause is generally incorrect target machine operations. The impact severity is typically medium and the impact consequence of most bugs is target configuration failure. Bug fixes tend to be small method code changes and usually occur in the execution component. Bugs are generally system-independent and are caused by logic errors that can be triggered by specifically executing an erroneous module. The analysis shows that applying automated bug discovery methods to the most error-prone execution and parser components of the system would lead to the largest improvement in the system’s quality.

Configuration management systems are a class of software used to automate system administrative tasks, one of which is the configuration of software systems. Although the automation is less error-prone than manual configuration done by a human, bugs in the source code can still cause configuration errors. This can result into unwanted consequences for the managed software system, some examples are performance degradation, downtime and security breaches.

Bug studies are conducted on previously reported bugs to find out their characteristics. Such findings help with preventing, detecting and fixing bugs, which ultimately causes software to become more reliable. This paper aims to fill in a research gap in the literature surrounding bug studies conducted on configuration management systems. From Salt, a widely used open-source configuration management system, a data-set of 5,896 bugs with fixes was collected. The main research question answered by this paper is ``What are the common patterns can be extracted from bugs found in Salt?'' To answer this, 100 bugs were randomly sampled from this data-set to analyze their symptoms, root causes, impact, fixes, system-dependence and triggers.