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GitHub is a popular data repository for code examples. It is beingcontinuously used to train several AI-based tools to automatically generatecode. However, the effectiveness of such tools in correctly demonstrating theusage of cryptographic APIs has not been thoroughly assessed. In this paper, weinvestigate the extent and severity of misuses, specifically caused byincorrect cryptographic API call sequences in GitHub. We also analyze thesuitability of GitHub data to train a learning-based model to generate correctcryptographic API call sequences. For this, we manually extracted and analyzedthe call sequences from GitHub. Using this data, we augmented an existinglearning-based model called DeepAPI to create two security-specific models thatgenerate cryptographic API call sequences for a given natural language (NL)description. Our results indicate that it is imperative to not neglect themisuses in API call sequences while using data sources like GitHub, to trainmodels that generate code.

GitHub Considered Harmful? Analyzing Open-Source Projects for the Automatic Generation of Cryptographic API Call Sequences