Classification of Software Engineering Contributions to Support Validation Techniques and Research Applications

Science and thereby software engineering (SE) research enables progress and innovation. SE research has high economic relevance and serves as an enabler for innovations in other research fields as well. Researchers in this field perform many activities and face corresponding challenges. They have to design and conduct research, while deciding on an appropriate method design for a scientific or practical problem. They have to review research while critically assessing research outcomes in a systematic and comparable way. They have to search for research outcomes to find research gaps to build upon. Likewise, by finding related information, they are able to justify relevance of own research work. These searching processes are done via keyword-based searches while struggling with information overload due to immensely growing number of SE papers published every year. Thus, there is the need for conceptual approaches, automated mechanisms, and a rethinking about scholarly communication to support these research activities and challenges in an effective, efficient, and comprehensible way. There are a variety of available classifications for research methods in literature. Additionally, empirical standards for research methods are continuously developed and adapted from other research fields. However, there is no consistency in the covered research methods, nor in the used terminology as well as no systematic approach to structure this body of research knowledge (e.g., template research questions, research methods, validity threats, replication types). Therefore, this doctoral thesis will provide a unified classification scheme to describe the characteristics of SE research. This further enables classification of SE papers and a corresponding queryable knowledge management system and, thus, advance state of practice.