See more details in our submitted ICPC tool track paper. Alternatively, watch our short demo video [2m:03s].
Program comprehension is a central cognitive process in programming and has been in the focus of researchers for decades, but is still not thoroughly unraveled. Multi-modal measurement methods are a way to gain a more holistic understanding of program comprehension. However, there is no proper tool support that lets researchers explore synchronized, conjoint multi-modal data, specifically designed for the needs in software engineering. In this paper, we present CodersMUSE, a prototype implementation that aims to satisfy this crucial need.
In essence, CodersMUSE provides a synchronized, integrated presentation of different modalities. The focus is particularly on multi-modal experiments in software engineering.
Currently supported modalities are:
- behavioral data (response time, click time, correctness)
- eye tracking
- psycho-physiological data (heart rate, respiration)
- fMRI data (via nipy)
For more information, see the paper or watch the video below. Keep in mind that this is an early prototype and not a mature tool.
If you want to try out CodersMUSE, clone the repository. Besides the python scripts, it comes with some sample data located in /sample. Currently, the full-brain fMRI data cannot be shared on this public repository due to German privacy regulations. Please contact us if you would like to access a sample fMRI data set.
If you want to explore your own experiment data, make sure to preprocess and prepare your data in a way that is compatible to CodersMUSE's expectation. This first prototype is rather inflexible when it comes to input data.
The data should be in .csv files. One file each for behavioral, eye-tracking, and psycho-physiological data. The .csv files should be:
- Comma-separated
- Be aligned to one common
Timecolumn - Time resolution is in hundreds of a second
- Use the same column naming as the sample files (see
/sample/data) - Already be preprocessed, if necessary (e.g., smoothing)
If you encounter problems, please let us know.
MIT License
Copyright (c) 2019 Norman Peitek
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