Application Programming Interfaces (APIs) are crucial to software development, enabling integration of existing systems with new applications by reusing tried and tested code, saving development time and increasing software safety. In particular, the Java standard library APIs, along with numerous third-party APIs, are extensively utilized in the development of enterprise application software. However, their misuse remains a significant source of bugs and vulnerabilities. Furthermore, due to the limited examples in the official API documentation, developers often rely on online portals and generative AI models to learn unfamiliar APIs, but using such examples may introduce unintentional errors in the software. In this paper, we present AFGNN, a novel Graph Neural Network (GNN)-based framework for efficiently detecting API misuses in Java code. AFGNN uses a novel API Flow Graph (AFG) representation that captures the API execution sequence, data, and control flow information present in the code to model the API usage patterns. AFGNN uses self-supervised pre-training with AFG representation to effectively compute the embeddings for unknown API usage examples and cluster them to identify different usage patterns. Experiments on popular API usage datasets show that AFGNN significantly outperforms state-of-the-art small language models and API misuse detectors.

Computational notebooks have become the go-to way for solving data-science problems. While they are designed to combine code and documentation, prior work shows that documentation is largely ignored by the developers because of the manual effort. Automated documentation generation can help, but existing techniques fail to capture algorithmic details and developers often end up editing the generated text to provide more explanation and sub-steps. This paper proposes a novel machine-learning pipeline, Cell2Doc, for code cell documentation in Python data science notebooks. Our approach works by identifying different logical contexts within a code cell, generating documentation for them separately, and finally combining them to arrive at the documentation for the entire code cell. Cell2Doc takes advantage of the capabilities of existing pre-trained language models and improves their efficiency for code cell documentation. We also provide a new benchmark dataset for this task, along with a data-preprocessing pipeline that can be used to create new datasets. We also investigate an appropriate input representation for this task. Our automated evaluation suggests that our best input representation improves the pre-trained model's performance by 2.5x on average. Further, Cell2Doc achieves 1.33x improvement during human evaluation in terms of correctness, informativeness, and readability against the corresponding standalone pretrained model.