The paper presents iTrace, an Eclipse plugin that implicitly records developers' 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 usage scenarios. iTrace is designed to support a variety of eye trackers. The design is flexible enough to record eye movements on various types of software artifacts (Java code, text/html/xml documents, diagrams), as well as IDE user interface elements. The plugin has been successfully used for software traceability tasks and program comprehension tasks. iTrace is also applicable to other tasks such as code summarization and code recommendations based on developer eye movements. A short video demonstration is available at https://youtu.be/3OUnLCX4dXo.

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, however there is a significant difference reported on the rate at which students look at buggy lines of code. These results start to provide some direction as to the effect programming language might have in introductory programming classes.

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. Vowel sounds are isolated from the recordings and acoustic features are extracted and used to train several one-class machine learning algorithms in order to predict whether an athlete is concussed. Prediction results are verified against the diagnoses made by a ringside medical team at the time of recording and performance evaluation shows prediction accuracies of up to 98%.

This paper presents OnionUML, an Eclipse plug-in that reduces the number of visible classes in a UML class diagram while preserving structure and semantics of the UML elements. Compaction of class elements is done using onion graph notation. The goal is that developers will be able to view and understand subsystems of a large software system while being able to visualize how that subsystem fits into the whole system.

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 time initially scanning the code tend to take more time finding defects. The study here follows a similar setup with added eye-tracking measures and analyses on effectiveness and efficiency of finding defects with respect to eye gaze. The subject pool is larger and is comprised of a varied skill level. Results indicate that scanning significantly correlates with defect detection time as well as visual effort on relevant defect lines. Results of the study are compared and contrasted to the Uwano study.

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 between a variety of external sensors and consumer Android devices. The framework simplifies both application and driver development with abstractions that separate responsibilities between the user application, sensor framework, and device driver. These abstractions facilitate a componentized framework that allows developers to focus on writing minimal pieces of sensor-specific code enabling an ecosystem of reusable sensor drivers. The paper explores three alternative architectures for application-level drivers to understand trade-offs in performance, device portability, simplicity, and deployment ease. We explore these tradeoffs in the context of four sensing applications designed to support our work in the developing world. They highlight a range of sensor usage models for our application-level driver framework that vary data types, configuration methods, communication channels, and sampling rates to demonstrate the framework's effectiveness.