The comprehension of source code is a task inherent to many software development activities. Code change, code review and debugging are examples of these activities that depend heavily on developers' understanding of the source code. This ability is threatened when developers' cognitive load approaches the limits of their working memory, which in turn affects their understanding and makes them more prone to errors. Measures capturing humans' behavior and changes in their physiological state have been proposed in a number of studies to investigate developers' cognitive load. However, the majority of the existing approaches operate at a coarse-grained task level estimating the difficulty of the source code as a whole. Hence, they cannot be used to pinpoint the mentally demanding parts of it. We address this limitation in this paper through a non-intrusive approach based on eye-tracking. We collect users' behavioral and physiological features while they are engaging with source code and train a set of machine learning models to estimate the mentally demanding parts of code. The evaluation of our models returns F1, recall, accuracy and precision scores up to 85.65%, 84.25%, 86.24% and 88.61%, respectively, when estimating the mental demanding fragments of code. Our approach enables a fine-grained analysis of cognitive load and allows identifying the parts challenging the comprehension of source code. Such an approach provides the means to test new hypotheses addressing the characteristics of specific parts within the source code and paves the road for novel techniques for code review and adaptive e-learning.

Program comprehension is inherent to all software development activities. This task may require a high mental effort (or so-called “cognitive load”), which in turn can hinder the performance of developers. In the literature, several authors have investigated the ability of biosignals to estimate developers' cognitive load during program comprehension. While the majority of these studies provide estimates at the task level, we aim for a more fine-grained level of analysis allowing to pinpoint the critical parts of code that could be associated with cognitive load. We infer these critical parts solely from eye fixation features and investigate qualitatively their relationship with those perceived as challenging by users. Being able to pinpoint critical parts in the source-code, is a first stride towards a very handy approach providing targeted support to developers to prevent them from committing errors. Furthermore, such a lightweight approach can be adapted in online settings.