GLiSE: A Prompt-Driven and ML-Powered Tool for Automated Grey Literature Extraction in Software Engineering

TL;DR

GLiSE is a prompt-driven, ML-powered tool that automates the collection and relevance filtering of grey literature in software engineering from multiple sources, enhancing large-scale, reproducible research.

Contribution

The paper introduces GLiSE, a novel tool that automates grey literature extraction using prompts, semantic classification, and platform-specific queries, with a curated dataset and usability study.

Findings

Effective retrieval of relevant grey literature across platforms.

High reproducibility due to configuration-based settings.

Positive usability feedback from empirical study.

Abstract

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.