Towards Evidence-Based Ontology for Supporting Systematic Literature Review

TL;DR

This paper introduces an empirically developed ontology, COSONT, to automate and support systematic literature reviews in software engineering, significantly reducing effort while maintaining accuracy.

Contribution

It presents the design and extension of SLRONT into COSONT, an ontology that captures SLR knowledge to automate review activities effectively.

Findings

COSONT reduces review effort significantly.

Automated SLR using COSONT achieves conclusions comparable to manual reviews.

Ontology-based approach enhances efficiency of SLR processes.

Abstract

[Background]: Systematic Literature Review (SLR) has become an important software engineering research method but costs tremendous efforts. [Aim]: This paper proposes an approach to leverage on empirically evolved ontology to support automating key SLR activities. [Method]: First, we propose an ontology, SLRONT, built on SLR experiences and best practices as a groundwork to capture common terminologies and their relationships during SLR processes; second, we present an extended version of SLRONT, the COSONT and instantiate it with the knowledge and concepts extracted from structured abstracts. Case studies illustrate the details of applying it for supporting SLR steps. [Results]: Results show that through using COSONT, we acquire the same conclusion compared with sheer manual works, but the efforts involved is significantly reduced. [Conclusions]: The approach of using ontology could effectively and efficiently support the conducting of systematic literature review.