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.

Finding and fixing code quality concerns, such as defects or poor understandability of code, decreases software development and evolution costs. A common industrial practice to identify code quality concerns early on are code reviews. While code reviews help to identify problems early on, they also impose costs on development and only take place after a code change is already completed. The goal of our research is to automatically identify code quality concerns while a developer is making a change to the code. By using biometrics, such as heart rate variability, we aim to determine the difficulty a developer experiences working on a part of the code as well as identify and help to fix code quality concerns before they are even committed to the repository.

As a developer works on a change task, he or she might perceive some parts of the task as easy and other parts as being very difficult. Currently, little is known about when a developer experiences different difficulty levels, although being able to assess these difficulty levels would be helpful for many reasons. For instance, a developer's perceived difficulty might be used to determine the likelihood of a bug being introduced into the code or the quality of the code a developer is working with. In psychology, biometric measurements, such as electro-dermal activity or heart rate, have already been extensively used to assess a person's mental state and emotions, but only little research has been conducted to investigate how these sensors can be used in the context of software engineering. In our research we want to take advantage of the insights gained in these psychological studies and investigate whether such biometric sensors can be used to measure developers' perceived difficulty while working on a change task and support them in their work.

Software developers working on change tasks commonly experience a broad range of emotions, ranging from happiness all the way to frustration and anger. Research, primarily in psychology, has shown that for certain kinds of tasks, emotions correlate with progress and that biometric measures, such as electro-dermal activity and electroencephalography data, might be used to distinguish between emotions. In our research, we are building on this work and investigate developers' emotions, progress and the use of biometric measures to classify them in the context of software change tasks. We conducted a lab study with 17 participants working on two change tasks each. Participants were wearing three biometric sensors and had to periodically assess their emotions and progress. The results show that the wide range of emotions experienced by developers is correlated with their perceived progress on the change tasks. Our analysis also shows that we can build a classifier to distinguish between positive and negative emotions in 71.36% and between low and high progress in 67.70% of all cases. These results open up opportunities for improving a developer's productivity. For instance, one could use such a classifier for providing recommendations at opportune moments when a developer is stuck and making no progress.

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.

Continuous Integration (CI) environments cope with the repeated integration of source code changes and provide rapid feedback about the status of a software project. However, as the integration cycles become shorter, the amount of data increases, and the effort to find information in CI environments becomes substantial. In modern CI environments, the selection of measurements (e.g., build status, quality metrics) listed in a dashboard does only change with the intervention of a stakeholder (e.g., a project manager). In this paper, we want to address the shortcoming of static views with so-called Software Quality Assessment (SQA) profiles. SQA-Profiles are defined as rule-sets and enable a dynamic composition of CI dashboards based on stakeholder activities in tools of a CI environment (e.g., version control system). We present a set of SQA-Profiles for project management committee (PMC) members: Bandleader, Integrator, Gatekeeper, and Onlooker. For this, we mined the commit and issue management activities of PMC members from 20 Apache projects. We implemented a framework to evaluate the performance of our rule-based SQA-Profiles in comparison to a machine learning approach. The results showed that project-independent SQA-Profiles can be used to automatically extract the profiles of PMC members with a precision of 0.92 and a recall of 0.78.

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 the evolution of software, stakeholders continuously seek and consult various information artifacts and their interdependencies to successfully complete their daily activities. While a lot of research has focused on supporting stakeholders in satisfying various information needs, there is little empirical evidence on how these information needs manifest themselves in the context of professional software development teams of real world companies. To investigate the information needs of the different stakeholder roles involved in software evolution activities, we conducted an empirical study with 23 participants from two professional development teams of one company. The analysis of the gathered data shows that information needs exhibit a crosscutting nature with respect to stakeholder role, activity, artifacts and even fragments of artifacts. We also found that the dependencies between information artifacts are important for the successful performance of software evolution activities, but often not captured explicitly. The lack of an explicit representation of these interdependencies often result in difficulties identifying dependent artifacts and additional communication effort. Based on our findings, we suggest ways to better support stakeholders with their information needs.