Maximilian Auch, Maximilian Balluff, Peter Mandl, and Christian Wolff, IAMLIS, Munich University of Applied Sciences HM, Lothstraße 34, 80335 Munich, Germany
We propose a novel method to classify GitHub repositories as professionally maintained or exploratory using their README.md files. We compare Large Language Models (LLMs) with classical NLP approaches like term frequency similarity and word embedding-based nearest neighbors, using RoBERTa as a baseline. We created and annotated a new dataset of over 200 repositories. Our evaluation shows LLMs outperform classical NLP models. GPT-4o achieved the best zero-shot classification without multi-step reasoning. Among smaller models, Google’s Gemini 1.5 Flash performed well. Few-shot learning improved performance for some models; Llama 3 (70b) reached 89.5% accuracy with multi-step reasoning, but improvements were inconsistent across models. Filtering based on word probability thresholds had mixed results. We discuss trade-offs between accuracy, time, and cost. Smaller models and prompt-based queries without multi-step reasoning offer faster, cost-effective solutions, useful in time-sensitive scenarios.Approximately 70% of repositories could be accurately classified based on README.md content.
Classification, README.md, Zero-shot, Few-shot, LLM.
Narcísio Mula1 and Claudio Nhancale2, 1Department of Mathematics, Universidade Save, Chongoene, Mozambique, 2Department of Mathematics, Universidade Save, Chongoene, Mozambique
Cyber threats have rapidly evolved, rendering traditional security testing methods insufficient for the effective detection of vulnerabilities in software. This work proposes the development of an automated testing agent based on Machine Learning, aimed at enhancing the detection of vulnerabilities such as Cross-Site Scripting (XSS) and SQL Injection (SQLi). The study encompasses the collection and preparation of vulnerability data, as well as the selection and training of Machine Learning models, utilizing algorithms such as Support Vector Machines and Random Forests. Preliminary results indicate that the proposed approach improves accuracy in identifying vulnerabilities compared to traditional methods. This work contributes to the automation of security testing, providing a more adaptive and efficient solution to address the challenges of contemporary cyber threats.
Vulnerability Detection, Artificial Intelligence, Machine Learning .
Angelina Tzacheva1 and Sanchari Chatterjee2, 1Computer Science and Information Technology College of Computing and Engineering, WestCliff University,Irvine, CA 92614, 2Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC, 28223
Action Rules are rule based systems that extract actionable patterns which are hidden in big volumes of data generated from Education sector, Business field, Medical domain and Social Media, in a single day. In the technological world of big data, massive amounts of data are collected by organizations, including in major domains like financial, medical, social media and Internet of Things(IoT). Mining this data can provide a lot of meaningful insights on how to improve user experience in multiple domain. Users need recommendations on actions they can undertake to increase their profit or accomplish their goals, this recommendations are provided by Actionable patterns. For example: How to improve student learning; how to increase business profitability; how to improve user experience in social media; and how to heal patients and assist hospital administrators. Action Rules provide actionable suggestions on how to change the state of an object from an existing state to a desired state for the benefit of the user. The traditional Action Rules extraction models, which analyze the data in a non distributed fashion, does not perform well when dealing larger datasets. In this work we are concentrating on the vertical data splitting strategy using information granules and creating the data partitioning more logically instead of splitting the data randomly and also generating meta actions after the vertical split. Information granules form basic entities in the world of Granular Computing(GrC), which represents meaningful smaller units derived from a larger complex information system. We introduced Modified Hybrid Action rule method with Partition Threshold Rho. Modified Hybrid Action rule mining approach combines both these frameworks and generates complete set of Action Rules, which further improves the computational performance with large datasets.
Emotion Detection, Meta Action, Information granules. .
