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Understanding understanding source code with functional magnetic resonance imaging
Open accessAbstract
Program comprehension is an important cognitive process that inherently eludes direct measurement. Thus, researchers are struggling with providing suitable programming languages, tools, or coding conventions to support developers in their everyday work. In this paper, we explore whether functional magnetic resonance imaging (fMRI), which is well established in cognitive neuroscience, is feasible to soundly measure program comprehension. In a controlled experiment, we observed 17 participants inside an fMRI scanner while they were comprehending short source-code snippets, which we contrasted with locating syntax errors. We found a clear, distinct activation pattern of five brain regions, which are related to working memory, attention, and language processing---all processes that fit well to our understanding of program comprehension. Our results encourage us and, hopefully, other researchers to use fMRI in future studies to measure program comprehension and, in the long run, answer questions, such as: Can we predict whether someone will be an excellent programmer? How effective are new languages and tools for program understanding? How should we train programmers?
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“When comprehension of code was compared to detection of syntactic errors in code, activity in both fronto-parietal and putative language areas was observed (Siegmund et al., 2014, Siegmund, 2017).”
From Computer code comprehension shares neural resources with formal logical inference in the fronto-parietal network (2020) · cites this paper as Siegmund et al., 2014
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Metadata
- Authors
- Siegmund, J., Kästner, C., Apel, S., Parnin, C., Bethmann, A., Leich, T., Saake, G., & Brechmann, A.
- Affiliations
- University of Passau (DE)
- Carnegie Mellon University (US)
- Georgia Institute of Technology (US)
- Leibniz Institute for Neurobiology (DE)
- Metop Research Institute, Germany
- Otto-von-Guericke-Universität Magdeburg (DE)
- Venue
- Proceedings of the 36th International Conference on Software Engineering
- Publisher
- ACM
- Published
- 2014-05-31
- Pages
- 378-389
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W2014232144
- Cited by
- 219
- References
- 87
- Citations by year
- 2026: 19 · 2025: 13 · 2024: 15 · 2023: 8 · 2022: 12 · 2021: 24 · 2020: 38 · 2019: 22 · 2018: 18 · 2017: 10 · 2016: 18 · 2015: 17 · 2014: 4
- FWCI
- 13.93
- Type
- conference paper
- Language
- en
- Open access
- green
- figshare.com
- Locations
- ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (publishedVersion)
- KiltHub Repository (submittedVersion)
- Figshare (submittedVersion, cc-by)
- KiltHub Repository
- Funders
- National Science Foundation
- Deutsche Forschungsgemeinschaft
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience, Education and Cognitive Function
- Keywords
- program comprehension, source code understanding, functional MRI, brain activation patterns, working memory, attention, language processing, programming languages, programmer training
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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