Publication: Predicting code comprehension: a novel approach to align human gaze with code using deep neural networks
Predicting code comprehension: a novel approach to align human gaze with code using deep neural networks
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Alakmeh, T., Reich, D., Jäger, L., & Fritz, T. (2024). Predicting code comprehension: a novel approach to align human gaze with code using deep neural networks (Vol. 1, No. FSE, Article 88). Vol. 1, No. FSE, Article 88, 1982–2004. https://doi.org/10.1145/3660795
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The better the code quality and the less complex the code, the easier it is for software developers to comprehend and evolve it. Yet, how do we best detect quality concerns in the code? Existing measures to assess code quality, such as McCabe’s cyclomatic complexity, are decades old and neglect the human aspect. Research has shown that considering how a developer reads and experiences the code can be an indicator of its quality. In our research, we built on these insights and designed, trained, and evaluated the first deep neural netw
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Alakmeh, T., Reich, D., Jäger, L., & Fritz, T. (2024). Predicting code comprehension: a novel approach to align human gaze with code using deep neural networks (Vol. 1, No. FSE, Article 88). Vol. 1, No. FSE, Article 88, 1982–2004. https://doi.org/10.1145/3660795
