GLiSE: A Prompt-Driven and ML-Powered Tool for Automated Grey Literature Extraction in Software Engineering
This paper presents GLiSE, a reproducible, prompt-driven tool that automates the extraction, filtering, and ranking of grey literature from diverse software engineering sources using machine learning, accompanied by a curated dataset and an empirical usability study.
Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Imagine you are a software engineer trying to solve a tricky problem. You know the official textbooks (academic papers) have some answers, but the real-world "street smarts"—the quick fixes, the workarounds, and the latest gossip about what's actually breaking in the industry—are hidden in the messy, unorganized corners of the internet. This is called Grey Literature. It's like finding the best recipes not in a cookbook, but in a thousand different blog posts, forum threads, and GitHub comments.
The problem? Finding these gems is like trying to find a specific needle in a haystack that keeps changing shape. The sources are everywhere, the formats are different, and doing it manually takes forever.
Enter GLiSE (pronounced like "glissade," a smooth slide). It's a new digital tool designed to be your personal "Grey Literature Hunter." Here is how it works, broken down into simple steps:
1. The Magic Prompt (The "Order")
Instead of you typing complex search strings for GitHub, Stack Overflow, and Google separately, you just type a simple sentence describing what you need.
- Analogy: Think of this as ordering a custom meal at a restaurant. You don't need to know how to cook the ingredients; you just tell the chef (GLiSE) what you want to eat.
- What it does: GLiSE takes your simple sentence and instantly translates it into the specific "language" or search codes that GitHub, Stack Overflow, and Google understand.
2. The Scavenger Hunt (The "Gathering")
Once GLiSE has your custom search codes, it sends out a team of digital scouts to visit these websites.
- Analogy: Imagine a team of librarians running to different libraries (GitHub, Stack Overflow, Google) at the same time, grabbing every book that matches your description.
- What it does: It pulls in titles, snippets, and descriptions from these sources. It's smart enough to realize if it grabbed the same "book" twice and ignores the duplicate.
3. The Smart Filter (The "Sorter")
This is the most important part. The scouts bring back hundreds of results, but many might be junk or irrelevant. GLiSE uses a "brain" (Machine Learning) to sort the good stuff from the bad.
- Analogy: Imagine a very strict butler who reads every single book the scouts brought back. The butler compares the content of each book against your original order. If a book is about "baking bread" but you asked for "fixing a car," the butler puts it in the trash. If it's a perfect match, it goes to the front of the line.
- How it works: The tool turns words into mathematical "fingerprints" (called embeddings). It compares the fingerprint of your request to the fingerprint of every result. If they look similar, it keeps them; if not, it discards them.
The Results: Does it work?
The creators tested this tool with a small group of software engineers and researchers. They asked them to do the same research task twice: once the old-fashioned way (manually searching) and once using GLiSE.
- Speed: Using GLiSE was like switching from walking to driving. It took participants about 39% less time to find their first useful result.
- Efficiency: To find 10 good results manually, people had to look through about 25 items. With GLiSE, they only had to look through 1 item on average because the tool filtered out the noise so well.
- Happiness: The users rated the tool very highly (81 out of 100), saying it was useful and they would use it again.
The Bottom Line
GLiSE is a tool that automates the boring, messy work of finding real-world software advice. It turns a vague idea into a precise search, gathers the results, and uses a smart filter to show you only the most relevant information, saving researchers and engineers hours of digging through the internet.
What the paper does NOT claim:
- It does not claim to replace human judgment entirely; it just speeds up the search.
- It does not claim to work for medical or clinical research (it is built specifically for software engineering).
- It does not promise to be perfect forever; the creators admit they need more data to make the "butler" even smarter in the future.
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