We are excited that our paper “Interruptibility of Software Developers and its Prediction Using Psycho-Physiological Sensors” by Manuela Züger and Thomas Fritz was accepted for CHI 2015 and like to share a preprint with you.
Interruptions of knowledge workers are common and can cause a high cost if they happen at inopportune moments. Our paper presents a lab and a field study with a total of 20 software developers, where we examined the use of psycho-physiological sensors to measure interruptibility of a knowledge worker in a real-world context.
The results show that a Naïve Bayes classifier 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. This demonstrates the potential of psycho-physiological sensors to avoid expensive interruptions. For instance, such a classifier could be used to automatically turn of notifications while a knowledge worker’s interruptibility is low.
The preprint of the paper can be downloaded here.
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We are looking for a master student (40%) to help us with programming tasks in the context of the FP7 research project CloudWave. Please find details here.
We are excited to announce that out paper “Discovering Loners and Phantoms in Commit and Issue Data” has been accepted for the 23rd IEEE International Conference on Program Comprehension (ICPC 2015) in Florence, Italy.
The interlinking of commit and issue data has become a de-facto standard in software development. Modern issue tracking systems, such as JIRA, automatically interlink commits and issues by the extraction of identifiers (e.g., issue key) from commit messages. However, the conventions for the use of interlinking methodologies vary between software projects. For example, some projects enforce the use of identifiers for every commit while others have less restrictive conventions. In this work, we introduce a model called PaLiMod (Partial Linking Model) to enable the analysis of interlinking characteristics in commit and issue data. We surveyed 15 Apache projects to investigate differences and commonalities between linked and non-linked commits and issues (RQ1). Based on the gathered information, we created a set of heuristics to interlink the residual of non-linked commits and issues (RQ2).
We observed that in the majority of the analyzed projects, the number of commits linked to issues is higher than the number of commits without link. On average, 74% of commits are linked to issues and 50% of the issues have associated commits. Based on the survey data, we identified two interlinking characteristics which we call Loners (one commit, one issue) and Phantoms (multiple commits, one issue). For these two characteristics, we proposed heuristics to automatically interlink non-linked commit and issue data. The evaluation results showed that our approach can achieve an overall precision of 96% with a recall of 92% in case of the Loner heuristic and an overall precision of 73% with a recall of 53% in case of the Phantom heuristic.
The results of our evaluation indicate that the proposed PaLiMod model and heuristics enable an automatic interlinking and can indeed reduce the residual of non-linked commits and issues in software projects.