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An eye-tracking study is presented that investigates how individuals find defects in source code. This work partially replicates a previous eye-tracking study by Uwano et al. [2006]. In the Uwano study, eye movements are used to characterize the performance of individuals in reviewing source code. Their analysis showed that subjects who did not spend enough(More)
Smartphones can now connect to a variety of external sensors over wired and wireless channels. However, ensuring proper device interaction can be burdensome, especially when a single application needs to integrate with a number of sensors using different communication channels and data formats. This paper presents a framework to simplify the interface(More)
Sensing data is important to a variety of data collection and monitoring applications. This paper presents the ODK Sensors framework designed to simplify the process of integrating sensors into mobile data collection tasks for both programmers and data collectors. Current mobile platforms (<i>e.g</i>., smartphones, tablets) can connect to a variety of(More)
Concussions are Mild Traumatic Brain Injuries (mTBI) that are common in contact sports and are often difficult to diagnose due to the delayed appearance of symptoms. This paper explores the feasibility of using speech analysis for detecting mTBI. Recordings are taken on a mobile device from athletes participating in a boxing tournament following each match.(More)
The paper presents iTrace, an Eclipse plugin that implicitly records developers&#039; eye movements while they work on change tasks. iTrace is the first eye tracking environment that makes it possible for researchers to conduct eye tracking studies on large software systems. An overview of the design and architecture is presented along with features and(More)
A study to assess the effect of programming language on student comprehension of source code is presented, comparing the languages of C++ and Python in two task categories: overview and find bug tasks. Eye gazes are tracked while thirty-eight students complete tasks and answer questions. Results indicate no significant difference in accuracy or time,(More)
This paper describes our submission to Se-mEval2014 Task 9: Sentiment Analysis in Twitter. Our model is primarily a lexicon based one, augmented by some pre-processing, including detection of Multi-Word Expressions, negation propagation and hashtag expansion and by the use of pairwise semantic similarity at the tweet level. Feature extraction is repeated(More)
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