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Main Authors: Cherief, Houcine Abdelkader, Mahmoudi, Brahim, Chenail-Larcher, Zacharie, Moha, Naouel, Sti'evenart, Quentin, Avellaneda, Florent
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2512.23066
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author Cherief, Houcine Abdelkader
Mahmoudi, Brahim
Chenail-Larcher, Zacharie
Moha, Naouel
Sti'evenart, Quentin
Avellaneda, Florent
author_facet Cherief, Houcine Abdelkader
Mahmoudi, Brahim
Chenail-Larcher, Zacharie
Moha, Naouel
Sti'evenart, Quentin
Avellaneda, Florent
contents Grey literature is essential to software engineering research as it captures practices and decisions that rarely appear in academic venues. However, collecting and assessing it at scale remains difficult because of their heterogeneous sources, formats, and APIs that impede reproducible, large-scale synthesis. To address this issue, we present GLiSE, a prompt-driven tool that turns a research topic prompt into platform-specific queries, gathers results from common software-engineering web sources (GitHub, Stack Overflow) and Google Search, and uses embedding-based semantic classifiers to filter and rank results according to their relevance. GLiSE is designed for reproducibility with all settings being configuration-based, and every generated query being accessible. In this paper, (i) we present the GLiSE tool, (ii) provide a curated dataset of software engineering grey-literature search results classified by semantic relevance to their originating search intent, and (iii) conduct an empirical study on the usability of our tool.
format Preprint
id arxiv_https___arxiv_org_abs_2512_23066
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle GLiSE: A Prompt-Driven and ML-Powered Tool for Automated Grey Literature Extraction in Software Engineering
Cherief, Houcine Abdelkader
Mahmoudi, Brahim
Chenail-Larcher, Zacharie
Moha, Naouel
Sti'evenart, Quentin
Avellaneda, Florent
Software Engineering
Digital Libraries
Grey literature is essential to software engineering research as it captures practices and decisions that rarely appear in academic venues. However, collecting and assessing it at scale remains difficult because of their heterogeneous sources, formats, and APIs that impede reproducible, large-scale synthesis. To address this issue, we present GLiSE, a prompt-driven tool that turns a research topic prompt into platform-specific queries, gathers results from common software-engineering web sources (GitHub, Stack Overflow) and Google Search, and uses embedding-based semantic classifiers to filter and rank results according to their relevance. GLiSE is designed for reproducibility with all settings being configuration-based, and every generated query being accessible. In this paper, (i) we present the GLiSE tool, (ii) provide a curated dataset of software engineering grey-literature search results classified by semantic relevance to their originating search intent, and (iii) conduct an empirical study on the usability of our tool.
title GLiSE: A Prompt-Driven and ML-Powered Tool for Automated Grey Literature Extraction in Software Engineering
topic Software Engineering
Digital Libraries
url https://arxiv.org/abs/2512.23066