  The rapid evolution of software development necessitates enhanced safety
measures. Extracting information about software failures from companies is
becoming increasingly more available through news articles.
  This research utilizes the Failure Analysis Investigation with LLMs (FAIL)
model to extract industry-specific information. Although the FAIL model's
database is rich in information, it could benefit from further categorization
and industry-specific insights to further assist software engineers.
  In previous work news articles were collected from reputable sources and
categorized by incidents inside a database. Prompt engineering and Large
Language Models (LLMs) were then applied to extract relevant information
regarding the software failure. This research extends these methods by
categorizing articles into specific domains and types of software failures. The
results are visually represented through graphs.
  The analysis shows that throughout the database some software failures occur
significantly more often in specific industries. This categorization provides a
valuable resource for software engineers and companies to identify and address
common failures.
  This research highlights the synergy between software engineering and Large
Language Models (LLMs) to automate and enhance the analysis of software
failures. By transforming data from the database into an industry specific
model, we provide a valuable resource that can be used to identify common
vulnerabilities, predict potential risks, and implement proactive measures for
preventing software failures. Leveraging the power of the current FAIL database
and data visualization, we aim to provide an avenue for safer and more secure
software in the future.
