Claudio Ferretti

Michele Carissimi, Martina Saletta, Claudio Ferretti

Understanding source code is a topic of great interest in the software engineering community, since it can help programmers in various tasks such as software maintenance and reuse. Recent advances in large language models (LLMs) have demonstrated remarkable program comprehension capabilities, while transformer-based topic modeling techniques offer effective ways to extract semantic information from text. This paper proposes and explores a novel approach that combines these strengths to automatically identify meaningful topics in a corpus of Python programs. Our method consists in applying topic modeling on the descriptions obtained by asking an LLM to summarize the code. To assess the internal consistency of the extracted topics, we compare them against topics inferred from function names alone, and those derived from existing docstrings. Experimental results suggest that leveraging LLM-generated summaries provides interpretable and semantically rich representation of code structure. The promising results suggest that our approach can be fruitfully applied in various software engineering tasks such as automatic documentation and tagging, code search, software reorganization and knowledge discovery in large repositories.

Martina Saletta, Claudio Ferretti

In this paper, we propose a novel approach for mining different program features by analysing the internal behaviour of a deep neural network trained on source code. Using an unlabelled dataset of Java programs and three different embedding strategies for the methods in the dataset, we train an autoencoder for each program embedding and then we test the emerging ability of the internal neurons in autonomously building internal representations for different program features. We defined three binary classification labelling policies inspired by real programming issues, so to test the performance of each neuron in classifying programs accordingly to these classification rules, showing that some neurons can actually detect different program properties. We also analyse how the program representation chosen as input affects the performance on the aforementioned tasks. On the other hand, we are interested in finding the overall most informative neurons in the network regardless of a given task. To this aim, we propose and evaluate two methods for ranking neurons independently of any property. Finally, we discuss how these ideas can be applied in different settings for simplifying the programmers' work, for instance if included in environments such as software repositories or code editors.