Statik: A Decentralized Version Control System Built on IPFS
Statik is a decentralized version control tool that leverages the capabilities of IPFS to provide a secure, efficient, and transparent alternative to traditional centralized version control systems.
Leveraging Large Language Models for Effective Configuration Validation
Large Language Models (LLMs) can be effectively leveraged as configuration validators to detect misconfigurations, outperforming existing techniques.
Automated Generation of User Stories and Test Cases from Software Requirements Using Large Language Models
A tool called "GeneUS" is developed to automatically generate user stories and test cases from software requirements documents using a large language model (LLM) and a novel prompting technique called "Refine and Thought" (RaT).
An LLM-Based Two-Stage Strategy to Accurately Localize Configuration Errors in Configurable Software Systems via Log Analysis
An LLM-based two-stage strategy is proposed to accurately localize the root-cause configuration properties for end-users based on log analysis, overcoming the challenges posed by the vast and complex configuration space.
Improving Pre-Trained Model Naming Practices in Hugging Face to Enhance Reusability and Trustworthiness
Pre-trained model (PTM) naming practices in the Hugging Face ecosystem diverge from traditional software package naming, posing challenges for model reuse and trustworthiness. This study delineates the current PTM naming practices, identifies discrepancies between user preferences and practical implementation, and introduces an automated tool to detect naming anomalies.
Automated Generation of Formal Program Specifications via Large Language Models
SpecGen introduces a novel technique for formal program specification generation based on Large Language Models, outperforming existing methods.
Seeking Enlightenment: Incorporating Evidence-Based Practice Techniques in Research Software Engineering Team
Evidence-based practice techniques can benefit research software engineers by promoting career-long learning, professionalization, and better practices in scientific software development.
Demystifying Faulty Code with LLM: Step-by-Step Explanation
Automated fault localization tools like FuseFL enhance fault localization results by providing step-by-step reasoning for code errors.
Bugs in Large Language Models Generated Code: An Empirical Study Revealing Distinctive Bug Patterns
LLM-generated code exhibits various bug patterns, including Misinterpretation, Missing Corner Cases, and Non-Prompted Consideration, highlighting potential issues with automatic code generation.
Automated Approaches to Detect Self-Admitted Technical Debt: A Systematic Literature Review
The author explores the automated detection of self-admitted technical debt using natural language processing and machine learning algorithms to assist developers efficiently.
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