TY - GEN
T1 - GLiSE
T2 - 23rd International Conference on Mining Software Repositories, MSR 2026
AU - Mahmoudi, Brahim
AU - Chenail-Larcher, Zacharie
AU - Cherief, Houcine Abdelkader
AU - Stiévenart, Quentin
AU - Moha, Naouel
AU - Avellaneda, Florent
N1 - Publisher Copyright:
© 2026 Copyright held by the owner/author(s).
PY - 2026/7/31
Y1 - 2026/7/31
N2 - 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.
AB - 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.
KW - Embedding
KW - GLR
KW - Grey Literature
KW - Grey Literature Review
KW - NLP
UR - https://www.scopus.com/pages/publications/105046971983
U2 - 10.1145/3793302.3793304
DO - 10.1145/3793302.3793304
M3 - Contribution to conference proceedings
AN - SCOPUS:105046971983
T3 - Proceedings - 2026 IEEE/ACM 23rd International Conference on Mining Software Repositories, MSR 2026
SP - 574
EP - 578
BT - Proceedings - 2026 IEEE/ACM 23rd International Conference on Mining Software Repositories, MSR 2026
PB - Association for Computing Machinery, Inc
Y2 - 13 April 2026 through 14 April 2026
ER -