利用大语言模型探索跨行业软件故障相似性的程度
Exploring the extent of similarities in software failures across industries using LLMs
AI总结：
本文利用大语言模型对软件故障新闻文章进行行业和故障类型分类，并可视化分析跨行业故障相似性，为识别常见漏洞和预防风险提供资源。
摘要：
软件开发的快速演进要求加强安全措施。通过新闻文章，从公司中提取软件故障信息正变得越来越可行。本研究利用基于大语言模型的故障分析调查（FAIL）模型来提取行业特定信息。尽管FAIL模型的数据库信息丰富，但仍可通过进一步的分类和行业特定洞察来更好地辅助软件工程师。在先前的工作中，新闻文章从可靠来源收集，并在数据库内按事件进行分类。随后应用提示工程和大语言模型（LLMs）来提取与软件故障相关的信息。本研究通过将文章分类到特定领域和软件故障类型来扩展这些方法，并通过图表对结果进行可视化呈现。分析表明，在整个数据库中，某些软件故障在特定行业中发生的频率显著更高。这种分类为软件工程师和公司识别和解决常见故障提供了宝贵资源。本研究强调了软件工程与大语言模型（LLMs）之间的协同作用，以自动化并增强软件故障分析。通过将数据库中的数据转化为行业特定模型，我们提供了一种宝贵资源，可用于识别常见漏洞、预测潜在风险并实施预防软件故障的主动措施。借助当前FAIL数据库和数据可视化的力量，我们旨在为未来更安全、更可靠的软件提供一条途径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
