Systematic Literature Reviews in Software Engineering -- Enhancement of the Study Selection Process using Cohen's Kappa Statistic

Abstract

Context: Systematic literature reviews (SLRs) rely on a rigorous and auditable methodology for minimizing biases and ensuring reliability. A common kind of bias arises when selecting studies using a set of inclusion/exclusion criteria. This bias can be decreased through dual revision, which makes the selection process more time-consuming and remains prone to generating bias depending on how each researcher interprets the inclusion/exclusion criteria. Objective: To reduce the bias and time spent in the study selection process, this paper presents a process for selecting studies based on the use of Cohen's Kappa statistic. We have defined an iterative process based on the use of this statistic during which the criteria are refined until obtain almost perfect agreement (k>0.8). At this point, the two researchers interpret the selection criteria in the same way, and thus, the bias is reduced. Starting from this agreement, dual review can be eliminated; consequently, the time spent is drastically shortened. Method: The feasibility of this iterative process for selecting studies is demonstrated through a tertiary study in the area of software engineering on works that were published from 2005 to 2018. Results: The time saved in the study selection process was 28% (for 152 studies) and if the number of studies is sufficiently large, the time saved tend asymptotically to 50%. Conclusions: Researchers and students may take advantage of this iterative process for selecting studies when conducting SLRs to reduce bias in the interpretation of inclusion and exclusion criteria. It is especially useful for research with few resources.