On the Usage of Psychophysiological Data in Software Engineering: An Extended Systematic Mapping Study

TL;DR

This systematic mapping study reviews how psychophysiological data from wearable devices is used in software engineering, highlighting research gaps, trends, and the diversity of devices and indicators studied.

Contribution

It provides a comprehensive classification and mapping of existing studies, identifying gaps and trends in the use of psychophysiological data in software engineering research.

Findings

No dominant device type used for data collection

Over 50% of studies focus on mental states and neural activity

80% analyze composite data related to cognitive load during debugging

Abstract

In recent years, many studies have applied wearable devices to capture psychophysiological data from software developers. However, the current literature lacks investigations that classify the studies and point out gaps to be explored. This article, therefore, seeks to present a comprehensive overview of the literature by classifying and creating a systematic map of the works. Besides, it seeks to pinpoint research gaps, challenges, and trends. Based on widely known guidelines, a systematic mapping of the literature was designed and run to answer eight research questions. After applying a careful filtering process, we selected 27 representative studies from a sample of 2,084 potentially relevant works retrieved from seven digital libraries. The main results are: a classification scheme of the published studies was produced; there is no predominance of the devices used to capture psychophysiological data; over 50% of the studies have explored indicators related to mental states and neural activity; and 80% have analyzed composite data to understand the cognitive load and in the context of understanding debugging programs and strategies. Our findings can benefit researchers and students by creating a systematic map of the literature, being a starting point for future research.