Developers work on a broad variety of tasks during their workdays and constantly switch between them. While these task switches can be beneficial, they can also incur a high cognitive burden on developers, since they have to continuously remember and rebuild the task context–the artifacts and applications relevant to the task. Researchers have therefore proposed to capture task context more explicitly and use it to provide better task support, such as task switch reduction or task resumption support. Yet, these approaches generally require the developer to manually identify task switches. Automatic approaches for predicting task switches have so far been limited in their accuracy, scope, evaluation, and the time discrepancy between predicted and actual task switches. In our work, we examine the use of automatically collected computer interaction data for detecting developers’ task switches as well as task types. In two field studies–a 4h observational study and a multi-day study with experience sampling–we collected data from a total of 25 professional developers. Our study results show that we are able to use temporal and semantic features from developers’ computer interaction data to detect task switches and types in the field with high accuracy of 84 percent and 61 percent respectively, and within a short time window of less than 1.6 minutes on average from the actual task switch. We discuss our findings and their practical value for a wide range of applications in real work settings.

Knowledge workers experience many interruptions during their work day. Especially when they happen at inopportune moments, interruptions can incur high costs, cause time loss and frustration. Knowing a person's interruptibility allows optimizing the timing of interruptions and minimize disruption. Recent advances in technology provide the opportunity to collect a wide variety of data on knowledge workers to predict interruptibility. While prior work predominantly examined interruptibility based on a single data type and in short lab studies, we conducted a two-week field study with 13 professional software developers to investigate a variety of computer interaction, heart-, sleep-, and physical activity-related data. Our analysis shows that computer interaction data is more accurate in predicting interruptibility at the computer than biometric data (74.8% vs. 68.3% accuracy), and that combining both yields the best results (75.7% accuracy). We discuss our findings and their practical applicability also in light of collected qualitative data.

Software developers regularly have to focus in order to successfully perform their work. At the same time, developers experience many disruptions to their focus, especially in today's highly demanding, collaborative and open office work environments. When these disruptions happen during tasks that require a lot of focus, such as comprehending a difficult piece of source code, they can be very costly, causing a decrease in performance and quality. By sensing how focused a developer is, we might be able to reduce the cost of such disruptions. In our previous work, we investigated the use of biometric and computer interaction sensors to sense interruptibility---the availability for interruptions---and developed the FlowLight approach---a traffic light like LED indicator of a person's interruptibility---to reduce the cost of external in-person interruptions, a particularly expensive kind of disruption. Our results demonstrate the potential of accurately sensing interruptibility in the field and of reducing external interruption cost to increase focus and productivity of knowledge workers.

Due to the high number and cost of interruptions at work, several approaches have been suggested to reduce this cost for knowledge workers. These approaches predominantly focus either on a manual and physical indicator, such as headphones or a closed office door, or on the automatic measure of a worker's interruptibilty in combination with a computer-based indicator. Little is known about the combination of a physical indicator with an automatic interruptibility measure and its long-term impact in the workplace. In our research, we developed the FlowLight, that combines a physical traffic-light like LED with an automatic interruptibility measure based on computer interaction data. In a large-scale and long-term field study with 449 participants from 12 countries, we found, amongst other results, that the FlowLight reduced the interruptions of participants by 46%, increased their awareness on the potential disruptiveness of interruptions and most participants never stopped using it.

Interruptions of knowledge workers are common and can cause a high cost if they happen at inopportune moments. With recent advances in psycho-physiological sensors and their link to cognitive and emotional states, we are interested whether such sensors might be used to measure interruptibility of a knowledge worker. In a lab and a field study with a total of twenty software developers, we examined the use of psycho-physiological sensors in a real-world context. The results show that a Naive Bayes classifier based on psychophysiological features can be used to automatically assess states of a knowledge worker's interruptibility with high accuracy in the lab as well as in the field. Our results demonstrate the potential of these sensors to avoid expensive interruptions in a real-world context. Based on brief interviews, we further discuss the usage of such an interruptibility measure and interruption support for software developers.

Software developers make programming mistakes that cause serious bugs for their customers. Existing work to detect problematic software focuses mainly on post hoc identification of correlations between bug fixes and code. We propose a new approach to address this problem --- detect when software developers are experiencing difficulty while they work on their programming tasks, and stop them before they can introduce bugs into the code. In this paper, we investigate a novel approach to classify the difficulty of code comprehension tasks using data from psycho-physiological sensors. We present the results of a study we conducted with 15 professional programmers to see how well an eye-tracker, an electrodermal activity sensor, and an electroencephalography sensor could be used to predict whether developers would find a task to be difficult. We can predict nominal task difficulty (easy/difficult) for a new developer with 64.99% precision and 64.58% recall, and for a new task with 84.38% precision and 69.79% recall. We can improve the Naive Bayes classifier's performance if we trained it on just the eye-tracking data over the entire dataset, or by using a sliding window data collection schema with a 55 second time window. Our work brings the community closer to a viable and reliable measure of task difficulty that could power the next generation of programming support tools.