Evidence Based Software Engineering (EBSE) is modeled on collecting and analyzing the existing evidences extracted from the previous software engineering research literature in order to solve various research problems related to various software engineering areas.
We are currently conducting a couple of Systematic Literature Reviews (SLRs) in the areas of software refactoring and software metrics, respectively.
Recently, we conducted a feature analysis based exploration into the inconsistencies in automatic search feature provided by various electronic sources such as IEEEXplore, ACM DL, ScienceDirect, etc. The study cautions systematic reviewers about these inconsistencies and help them in better understanding the existing underlying sub features of automatic search, including search string formation, string execution, result filtering, result export and interface. Although this study was conducted in context of software engineering research, the outcomes reported are quite applicable to any possible domain that involves literature reviews. This is an ongoing work, and we would be happy to initiate all possible external internal or external collaborations to take it forward.
Github Link: https://github.com/pv-singh/DLs-for-SLRs
- Paramvir Singh and Karanpreet Singh (2017): “Exploring Automatic Search in Digital Libraries – A Caution Guide for Systematic Reviewers”, 21st International Conference on Evaluation and Assessment in Software Engineering (EASE), to be held in Karlskrona, Sweden. (Accepted) [Access Article] [Access Article Preprint] [EASE ppt]