Context: In recent years, several studies explored different facets of the developers’ cognitive load while executing tasks related to software engineering. Researchers have proposed and assessed different ways to measure developers’ cognitive load at work and some studies have evaluated the interplay between developers’ cognitive load and other attributes such as productivity and software quality. Problem: However, the body of knowledge about developers’ cognitive load measurement is still dispersed. That hinders the effective use of developers’ cognitive load measurements by industry practitioners and makes it difficult for researchers to build new scientific knowledge upon existing results. Objective: This work aims to pinpoint gaps providing a classification and a thematic analysis of studies on the measurement of cognitive load in the context of software engineering. Method: We carried out a Systematic Mapping Study (SMS) based on well-established guidelines to investigate nine research questions. In total, 33 articles (out of 2,612) were selected from 11 search engines after a careful filtering process. Results: The main findings are that (1) 55% of the studies adopted electroencephalogram (EEG) technology for monitoring the cognitive load; (2) 51% of the studies applied machine- learning classification algorithms for predicting cognitive load; and (3) 48% of the studies measured cognitive load in the context of programming tasks. Moreover, a taxonomy was derived from the answers of research questions. Conclusion: This SMS highlighted that the precision of machine learning techniques is low for realistic scenarios, despite the combination of a set of features related to developers’ cognitive load used on these techniques. Thus, this gap makes the effective integration of the measure of developers’ cognitive load in industry still a relevant challenge.