In this paper, we present an experimental study in which an electroencephalogram (EEG) device was used to measure cognitive load in programmers as they attempted to predict the output of C code snippets. Our goal was to see if particular patterns within the snippet induced higher levels of cognitive load, and if the collected EEG data might provide more detailed insights than performance measures. Our results suggest that while cognitive load can be an influence on code comprehension performance, other human factors, such as a tendency to forget certain programming rules or to misread what the code is asking them to do may also play a role, particularly for novice programmers. We conclude that: (1) different types of code patterns can affect programmers' cognitive processes in disparate ways, (2) neither self-reported data nor brainwave activity alone is a reliable indicator of programmers' level of comprehension for all types of code snippets, (3) EEG techniques could be useful to better understand the relationships between program comprehension, code patterns and cognitive processes, and (4) tests like ours could be useful to identify crucial learning gaps in novice programmers, which, in turn can be leveraged to improve programming tools and teaching strategies.

Humans often mistake the meaning of source code, and so misjudge a program's true behavior. These mistakes can be caused by extremely small, isolated patterns in code, which can lead to significant runtime errors. These patterns are used in large, popular software projects and even recommended in style guides. To identify code patterns that may confuse programmers we extracted a preliminary set of `atoms of confusion' from known confusing code. We show empirically in an experiment with 73 participants that these code patterns can lead to a significantly increased rate of misunderstanding versus equivalent code without the patterns. We then go on to take larger confusing programs and measure (in an experiment with 43 participants) the impact, in terms of programmer confusion, of removing these confusing patterns. All of our instruments, analysis code, and data are publicly available online for replication, experimentation, and feedback.