Abstract
Searching for the right code snippet is cumbersome and not a trivial task. Online platforms such as Github.com or searchcode.com provide tools to search, but they are limited to publicly available and internet-hosted code. However, during the development of research prototypes or confidential tools, it is preferable to store source code locally. Consequently, the use of external code search tools becomes impractical. Here, we present Selma (Code and Videos: https://anreu.github.io/selma): a local code search platform that enables term-based and semantic retrieval of source code. Selma searches code and comments, annotates undocumented code to enable term-based search in natural language, and trains neural models for code retrieval.
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Reusch, A., Lopes, G.C., Pertsch, W., Ueck, H., Gonsior, J., Lehner, W. (2024). Selma: A Semantic Local Code Search Platform. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14612. Springer, Cham. https://doi.org/10.1007/978-3-031-56069-9_21
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DOI: https://doi.org/10.1007/978-3-031-56069-9_21
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