# A Methodology of Guiding Web Content Mining and Knowledge Discovery in Evidence-based Software Engineering

## TL;DR

This paper proposes a systematic methodology to adapt Evidence-Based Software Engineering practices for web content mining, integrating automated tools and techniques to enhance knowledge discovery from online sources.

## Contribution

It introduces a two-stage adaptation of systematic literature review processes specifically tailored for web content mining in EBSE.

## Key findings

- Developed a framework for web content mining in EBSE
- Integrated automated text mining components into review process
- Enhanced systematic review methodology for online sources

## Abstract

Systematic Literature Review (SLR) is a rigorous methodology applied for Evidence-Based Software Engineering (EBSE) that identify, assess and synthesize the relevant evidence for answering specific research questions. Benefiting from the booming online materials in the era of Web 2.0, the technical Web content starts acting as alternative sources for EBSE. Web knowledge has been investigated and derived from Web content mining and knowledge discovery techniques, however they are still significantly different from reviewing academic literature. Thus the direct adoption of Web knowledge in EBSE lacks of systematic guidelines. In this paper, we propose to make an SLR adaptation to bridge the aforementioned gap along two stages. Firstly, we follow the general logic and procedure of SLR to regulate Web mining activities. Secondly, we substitute and enhance particular SLR processes with Web-mining-friendly methods and approaches. At the second stage, we mainly focus on adapting Conducting Review by integrating a set of automated components ranging from programmatic searching to various text mining techniques.