4 papers
On the Robustness of LLMs' Internal Representation of Code Correctness
Francisco Ribeiro, Sohaila Abdulsattar, Renata Gonzalez +2
Code generated by modern language models often reads naturally. Yet, it also often fails to implement what was asked. This should be no surprise, as research shows the models' own…
An Empirical Study of API Misuses of Data-Centric Libraries
Akalanka Galappaththi, Sarah Nadi, Christoph Treude
Developers rely on third-party library Application Programming Interfaces (APIs) when developing software. However, libraries typically come with assumptions and API usage constrai…
On LLMs' Internal Representation of Code Correctness
Francisco Ribeiro, Claudio Spiess, Prem Devanbu +1
Despite the effectiveness of large language models (LLMs) for code generation, they often output incorrect code. One reason is that model output probabilities are often not well-co…
Detecting and Fixing API Misuses of Data Science Libraries Using Large Language Models
Akalanka Galappaththi, Francisco Ribeiro, Sarah Nadi
Data science libraries, such as scikit-learn and pandas, specialize in processing and manipulating data. The data-centric nature of these libraries makes the detection of API misus…