Authors:

Martin Detloff

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About Paper:

Background/Motivation: The rapid evolution of software development necessitates enhanced safety measures. Extracting information about software failures from companies with opaque privacy policies is challenging. Problem Statement: 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. Methods: 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. Results: 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. Conclusion: This research highlights the synergy between software engineering and neural networks 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 methods such as data visualization, we aim to provide an avenue for safer and more secure software in the future. Keywords: Prompt Engineering; Software Failures

Source:

Purdue University / 2024

Topics:

No topics listed

Co-authors:

Martin Detloff