SEDec 12, 2016

On the Pragmatic Design of Literature Studies in Software Engineering: An Experience-based Guideline

arXiv:1612.03583v1164 citations
Originality Synthesis-oriented
AI Analysis

This work addresses the problem for software engineering researchers who need to manage extensive publications, but it is incremental as it builds on existing guidelines by adding practical recommendations.

The authors tackled the challenge of designing systematic literature studies in software engineering by presenting an experience-based guideline that focuses on practical data collection and selection procedures, derived from their own mapping studies and literature reviews.

Systematic literature studies have received much attention in empirical software engineering in recent years. They have become a powerful tool to collect and structure reported knowledge in a systematic and reproducible way. We distinguish systematic literature reviews to systematically analyze reported evidence in depth, and systematic mapping studies to structure a field of interest in a broader, usually quantified manner. Due to the rapidly increasing body of knowledge in software engineering, researchers who want to capture the published work in a domain often face an extensive amount of publications, which need to be screened, rated for relevance, classified, and eventually analyzed. Although there are several guidelines to conduct literature studies, they do not yet help researchers coping with the specific difficulties encountered in the practical application of these guidelines. In this article, we present an experience-based guideline to aid researchers in designing systematic literature studies with special emphasis on the data collection and selection procedures. Our guideline aims at providing a blueprint for a practical and pragmatic path through the plethora of currently available practices and deliverables capturing the dependencies among the single steps. The guideline emerges from various mapping studies and literature reviews conducted by the authors and provides recommendations for the general study design, data collection, and study selection procedures. Finally, we share our experiences and lessons learned in applying the different practices of the proposed guideline.

Foundations

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