Sebastian C. Müller

Protter: interactive protein feature visualization and integration with experimental proteomic data

SUMMARY The ability to integrate and visualize experimental proteomic evidence in the context of rich protein feature annotations represents an unmet need of the proteomics community. Here we present Protter, a web-based tool that supports interactive protein data analysis and hypothesis generation by visualizing both annotated sequence features and experimental proteomic data in the context of protein topology. Protter supports numerous proteomic file formats and automatically integrates a variety of reference protein annotation sources, which can be readily extended via modular plug-ins. A built-in export function produces publication-quality customized protein illustrations, also for large datasets. Visualizations of surfaceome datasets show the specific utility of Protter for the integrated visual analysis of membrane proteins and peptide selection for targeted proteomics. AVAILABILITY AND IMPLEMENTATION The Protter web application is available at http://wlab.ethz.ch/protter. Source code and installation instructions are available at http://ulo.github.io/Protter/. CONTACT wbernd@ethz.ch SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Using psycho-physiological measures to assess task difficulty in software development

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 classifiers 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.

Collaborative bug triaging using textual similarities and change set analysis

Bug triaging assigns a bug report, which is also known as a work item, an issue, a task or simply a bug, to the most appropriate software developer for fixing or implementing it. However, this task is tedious, time-consuming and error-prone if not supported by effective means. Current techniques either use information retrieval and machine learning to find the most similar bugs already fixed and recommend expert developers, or they analyze change information stemming from source code to propose expert bug solvers. Neither technique combines textual similarity with change set analysis and thereby exploits the potential of the interlinking between bug reports and change sets. In this paper, we present our approach to identify potential experts by identifying similar bug reports and analyzing the associated change sets. Studies have shown that effective bug triaging is done collaboratively in a meeting, as it requires the coordination of multiple individuals, the understanding of the project context and the understanding of the specific work practices. Therefore, we implemented our approach on a multi-touch table to allow multiple stakeholders to interact simultaneously in the bug triaging and to foster their collaboration. In the current stage of our experiments we have experienced that the expert recommendations are more specific and useful when the rationale behind the expert selection is also presented to the users.

An approach for collaborative code reviews using multi-touch technology

Code reviews are an effective mechanism to improve software quality, but often fall short in the development of software. To improve the desirability and ease of code reviews, we introduce an approach that explores how multi-touch interfaces can support code reviews and can make them more collaborative. Our approach provides users with features to collaboratively find and investigate code smells, annotate source code and generate review reports using gesture recognition and a Microsoft Surface Table. In a preliminary evaluation, subjects generally liked the prototypical implementation of our approach for performing code review tasks.

Using (bio)metrics to predict code quality online

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. In a field study with ten professional developers over a two-week period we investigated the use of biometrics to determine code quality concerns. Our results show that biometrics are indeed able to predict quality concerns of parts of the code while a developer is working on, improving upon a naive classifier by more than 26% and outperforming classifiers based on more traditional metrics. In a second study with five professional developers from a different country and company, we found evidence that some of our findings from our initial study can be replicated. Overall, the results from the presented studies suggest that biometrics have the potential to predict code quality concerns online and thus lower development and evolution costs.

Stakeholders' Information Needs for Artifacts and Their Dependencies in a Real World Context

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.

SQA-Profiles: Rule-based activity profiles for Continuous Integration environments

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.

Leveraging Biometric Data to Boost Software Developer Productivity

Producing great software requires great productive developers. Yet, what does it really mean for an individual developer to be productive, and what can we do to best help developers to be productive? To answer these questions, research has traditionally focused on measuring a developers output and therefore suffered from two drawbacks: the measures can only be calculated after a developer finished her work and these measures do not account for individual differences between developers. The recent advances in biometric sensor technology offer new opportunities to measure a developers cognitive and emotional states in real-time and thus allow us to know more about what an individual developer is currently experiencing and what might foster or impede the developers productivity. Results from recent research studies demonstrate the potential that biometric data has to accurately predict aspects of a developers work, such as perceived task and code difficulty, progress and interruptibility of a developer. This opens up new opportunities for better supporting developers in their work and, for instance, prevent bugs from entering the code, reduce costly interruptions, and foster a better and more productive work day. Our vision is that biometric sensing will be integrated into a developers work and that biometrics can be used to boost the productivity of each individual developer.

Tangible software modeling with multi-touch technology

This paper describes a design study that explores how multi-touch devices can provide support for developers when carrying out modeling tasks in software development. We investigate how well a multi-touch augmented approach performs compared to a traditional approach and if this new approach can be integrated into existing software engineering processes. For that, we have implemented a fully-functional prototype, which is concerned with agreeing on a good object-oriented design through the course of a Class Responsibility Collaboration (CRC) modeling session. We describe how multi-touch technology helps with integrating CRC cards with larger design methodologies, without loosing their unique physical interaction aspect. We observed high-potential in augmenting such informal sessions in software engineering with novel user interfaces, such as those provided by multi-touch devices.

Sensing Interruptibility in the Office: A Field Study on the Use of Biometric and Computer Interaction Sensors

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 persons 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.