7 citations · 9 across the 9 of their papers we have counts for
9 papers
Predicting LLM Performance from Prompt Linguistic Features: An Empirical Study in Requirements Engineering
Quim Motger, Alessio Miaschi, Xavier Franch +2
Background. LLM outputs are highly sensitive to prompt formulation: small wording changes can substantially affect output quality. This matters in software engineering, where promp…
Large Language Models for Requirements Engineering: A Cross-Task Empirical Evaluation
Jacek Dąbrowski, Manjeshwar Aniruddh Mallya, Alessio Ferrari +2
Requirements-related information is scattered across heterogeneous artefacts such as user feedback, developer discussions, and software repositories, making the extraction of actio…
BT-APE: A Computationally Light Backtracking Approach to Automatic Prompt Engineering for Requirements Classification
Mohammad Amin Zadenoori, Waad Alhoshan, Jacek Dąbrowski +2
Large language models (LLMs) are increasingly applied to requirements engineering (RE) tasks, yet the prompts guiding them are typically designed manually through trial and error,…
CudaMon: An R Package to Monitor NVIDIA GPUs, Showcased by Monitoring a GPU-accelerated Single-cell Analysis Workflow in R
Mohammad Amin Zadenoori, Riccardo Ceccaroni, Gabriele Sales +1
NVIDIA GPUs have recently started to be used in computational biology, yet R users lack integrated GPU monitoring tools, forcing reliance on external utilities like nvidia-smi. We…
RITA: A Tool for Automated Requirements Classification and Specification from Online User Feedback
Manjeshwar Aniruddh Mallya, Alessio Ferrari, Mohammad Amin Zadenoori +1
Context and motivation. Online user feedback is a valuable resource for requirements engineering, but its volume and noise make analysis difficult. Existing tools support individua…
From Online User Feedback to Requirements: Evaluating Large Language Models for Classification and Specification Tasks
Manjeshwar Aniruddh Mallya, Alessio Ferrari, Mohammad Amin Zadenoori +1
[Context and Motivation] Online user feedback provides valuable information to support requirements engineering (RE). However, analyzing online user feedback is challenging due to…
Does Model Size Matter? A Comparison of Small and Large Language Models for Requirements Classification
Mohammad Amin Zadenoori, Vincenzo De Martino, Jacek Dabrowski +2
[Context and motivation] Large language models (LLMs) show notable results in natural language processing (NLP) tasks for requirements engineering (RE). However, their use is compr…
Green Prompt Engineering: Investigating the Energy Impact of Prompt Design in Software Engineering
Vincenzo De Martino, Mohammad Amin Zadenoori, Xavier Franch +1
Language Models are increasingly applied in software engineering, yet their inference raises growing environmental concerns. Prior work has examined hardware choices and prompt len…
Large Language Models (LLMs) for Requirements Engineering (RE): A Systematic Literature Review
Mohammad Amin Zadenoori, Jacek Dąbrowski, Waad Alhoshan +2
Large Language Models (LLMs) are finding applications in numerous domains, and Requirements Engineering (RE) is increasingly benefiting from their capabilities to assist with compl…