Exploring the extent of similarities in software failures across industries using LLMs

• URL: http://arxiv.org/abs/2408.03528v2
• Date: Thu, 8 Aug 2024 03:52:06 GMT
• Title: Exploring the extent of similarities in software failures across industries using LLMs
• Authors: Martin Detloff,
• Abstract summary: This research utilizes the Failure Analysis Investigation with LLMs (FAIL) model to extract industry-specific information. In previous work news articles were collected from reputable sources and categorized by incidents inside a database. This research extends these methods by categorizing articles into specific domains and types of software failures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection [9.652886240532741]
  This paper thoroughly analyses large language models' capabilities in detecting vulnerabilities within source code. We evaluate the performance of six open-source models that are specifically trained for vulnerability detection against six general-purpose LLMs.
  arXiv  Detail & Related papers  (2024-08-29T10:00:57Z)
• Agent-Driven Automatic Software Improvement [55.2480439325792]
  This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs) The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation. We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
  arXiv  Detail & Related papers  (2024-06-24T15:45:22Z)
• FAIL: Analyzing Software Failures from the News Using LLMs [2.7325338323814328]
  We propose the Failure Analysis Investigation with LLMs (FAIL) system to fill this gap. FAIL collects, analyzes, and summarizes software failures as reported in the news. FAIL identified and analyzed 2457 distinct failures reported across 4,184 articles.
  arXiv  Detail & Related papers  (2024-06-12T13:51:51Z)
• Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs [49.57641083688934]
  We introduce a novel approach to anomaly detection in financial data using Large Language Models (LLMs) embeddings. Our experiments demonstrate that LLMs contribute valuable information to anomaly detection as our models outperform the baselines.
  arXiv  Detail & Related papers  (2024-06-05T20:19:09Z)
• Harnessing Large Language Models for Software Vulnerability Detection: A Comprehensive Benchmarking Study [1.03590082373586]
  We propose using large language models (LLMs) to assist in finding vulnerabilities in source code. The aim is to test multiple state-of-the-art LLMs and identify the best prompting strategies. We find that LLMs can pinpoint many more issues than traditional static analysis tools, outperforming traditional tools in terms of recall and F1 scores.
  arXiv  Detail & Related papers  (2024-05-24T14:59:19Z)
• The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
  Large Language Models (LLMs) are foundational to AI advancements. LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
  arXiv  Detail & Related papers  (2024-03-23T09:26:15Z)
• Automating SBOM Generation with Zero-Shot Semantic Similarity [2.169562514302842]
  A Software-Bill-of-Materials (SBOM) is a comprehensive inventory detailing a software application's components and dependencies. We propose an automated method for generating SBOMs to prevent disastrous supply-chain attacks. Our test results are compelling, demonstrating the model's strong performance in the zero-shot classification task.
  arXiv  Detail & Related papers  (2024-02-03T18:14:13Z)
• An Empirical Study on Using Large Language Models to Analyze Software Supply Chain Security Failures [2.176373527773389]
  One way to prevent future breaches is by studying past failures. Traditional methods of analyzing these failures require manually reading and summarizing reports about them. Natural Language Processing techniques such as Large Language Models (LLMs) could be leveraged to assist the analysis of failures.
  arXiv  Detail & Related papers  (2023-08-09T15:35:14Z)
• CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models [58.27254444280376]
  Large language models (LLMs) for automatic code generation have achieved breakthroughs in several programming tasks. Training data for these models is usually collected from the Internet (e.g., from open-source repositories) and is likely to contain faults and security vulnerabilities. This unsanitized training data can cause the language models to learn these vulnerabilities and propagate them during the code generation procedure.
  arXiv  Detail & Related papers  (2023-02-08T11:54:07Z)
• VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements [62.93814803258067]
  This paper presents VELVET, a novel ensemble learning approach to locate vulnerable statements in source code. Our model combines graph-based and sequence-based neural networks to successfully capture the local and global context of a program graph. VELVET achieves 99.6% and 43.6% top-1 accuracy over synthetic data and real-world data, respectively.
  arXiv  Detail & Related papers  (2021-12-20T22:45:27Z)
• Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models [59.04636530383049]
  Anomalies or failures in large computer systems, such as the cloud, have an impact on a large number of users. We propose a framework for anomaly detection in log data, as a major troubleshooting source of system information.
  arXiv  Detail & Related papers  (2021-02-23T09:17:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.