(Semi)-Automatic Approach to Support Literature Analysis for Software Engineers

Researchers perform literature reviews to synthesize existing evidence regarding a research topic. While being important means to condense knowledge, conducting a literature analysis, particularly, systematic literature review, requires a large amount of time and effort. Consequently, researchers are considering semi-automatic approaches to facilitate different stages of the review process. Surveys have shown that two of the most time consuming tasks within the literature review process are: to select primary studies and to assess their quality. To assure quality and reliability of the findings from a literature study, the quality of included primary studies must be evaluated. Despite being critical stages, these still lack the support of semi-automatic tools and hence, mostly performed manually. In this PhD thesis, we aim to address this gap in the current state of research and develop techniques that support the selection and assessment of primary studies for literature analyses. For the assessment of studies, we begin with exploring the information available from the digital libraries most commonly used by software engineering researchers, such as, the ACM Digital Library, IEEE Xplore, Science Direct, Springer Link, Web of Science. The information regarding authors, citation counts and publication venues are particularly important as these can provide an initial insight about the studies. Hence, a tool that captures such bibliographic information from the digital libraries and score the studies based on defined quality metrics, would certainly be beneficial to accelerate the process. However, for accurate assessment, the approach could be further extended to an in-depth full text investigation. We believe, developing such a strategy would indeed be useful for researchers conducting literature analyses, particularly software engineers, or any other research domain.