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Understanding the relationship between cognition and programming outcomes is important: it can inform interventions that help novices become experts faster. Neuroimaging techniques can measure brain activity, but prior studies of programming report only correlations. We present the first causal neurological investigation of the cognition of programming by using Transcranial Magnetic Stimulation (TMS). TMS permits temporary and noninvasive disruption of specific brain regions. By disrupting brain regions and then measuring programming outcomes, we discover whether a true causal relationship exists. To the best of our knowledge, this is the first use of TMS to study software engineering. Where multiple previous studies reported correlations, we find no direct causal relationships between implicated brain regions and programming. Using a protocol that follows TMS best practices and mitigates for biases, we replicate psychology findings that TMS affects spatial tasks. We then find that neurostimulation can affect programming outcomes. Multi-level regression analysis shows that TMS stimulation of different regions significantly accounts for 2.2% of the variance in task completion time. Our results have implications for interventions in education and training as well as research into causal cognitive relationships. 

This article presents CirFix, a framework for automatically repairing defects in hardware designs implemented in languages like Verilog. We propose a novel fault localization approach based on assignments to wires and registers, and a fitness function tailored to the hardware domain to bridge the gap between software-level automated program repair and hardware descriptions. We also present a benchmark suite of 32 defect scenarios corresponding to a variety of hardware projects. Overall, CirFix produces plausible repairs for 21/32 and correct repairs for 16/32 of the defect scenarios. Additionally, we evaluate CirFix's fault localization independently through a human study (n=41), and find that the approach may be a beneficial debugging aid for complex multi-line hardware defects.