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Chelsea Song and Chen Tang for their helpful comments on an earlier version of this paper.Thank you to the many members of the Well-being and Measurement Lab and the Laboratory for Understanding Careers and Individual Differences who assisted with data collection and rating the interviewees in the study.

Source Code Summarization is an emerging technology for automatically generating brief descriptions of code. Current summarization techniques work by selecting a subset of the statements and keywords from the code, and then including information from those statements and keywords in the summary. The quality of the summary depends heavily on the process of selecting the subset: a high-quality selection would contain the same statements and keywords that a programmer would choose. Unfortunately, little evidence exists about the statements and keywords that programmers view as important when they summarize source code. In this paper, we present an eye-tracking study of 10 professional Java programmers in which the programmers read Java methods and wrote English summaries of those methods. We apply the findings to build a novel summarization tool. Then, we evaluate this tool and provide evidence to support the development of source code summarization systems.

Background: Transdermal biosensors offer a noninvasive, low-cost technology for the assessment of alcohol consumption with broad potential applications in addiction science. Oldergeneration transdermal devices feature bulky designs and sparse sampling intervals, limiting potential applications for transdermal technology. Recently a new-generation of transdermal device has become available, featuring smartphone connectivity, compact designs, and rapid sampling. Here we present initial laboratory research examining the validity of a new-generation transdermal sensor prototype. Methods: Participants were young drinkers administered alcohol (target BAC=.08%) or no-alcohol in the laboratory. Participants wore transdermal sensors while providing repeated breathalyzer (BrAC) readings. We assessed the association between BrAC (measured BrAC for a specific time point) and eBrAC (BrAC estimated based only on transdermal readings collected in the immediately preceding time interval). Extra-Trees machine learning algorithms, incorporating transdermal time series features as predictors, were used to create eBrAC. Results: Failure rates for the new-generation prototype sensor were high (16%-34%). Among participants with useable new-generation sensor data, models demonstrated strong capabilities for separating drinking from non-drinking episodes, and significant (moderate) ability to differentiate BrAC levels within intoxicated participants. Differences between eBrAC and BrAC were 60% higher for models based on data from old-generation vs new-generation devices. Model comparisons indicated that both time series analysis and machine learning contributed significantly to final model accuracy. Conclusions: Results provide favorable preliminary evidence for the accuracy of real-time BAC estimates from a new-generation sensor.Future research featuring variable alcohol doses and real-world contexts will be required to further validate these devices.

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