Steven Costiou

Marius Mignard, Steven Costiou, Nicolas Anquetil et al.

Machine Learning (ML) code, particularly within notebooks, often exhibits lower quality compared to traditional software. Bad practices arise at three distinct levels: general Python coding conventions, the organizational structure of the notebook itself, and ML-specific aspects such as reproducibility and correct API usage. However, existing analysis tools typically focus on only one of these levels and struggle to capture ML-specific semantics, limiting their ability to detect issues. This paper introduces Vespucci Linter, a static analysis tool with multi-level capabilities, built on Moose and designed to address this challenge. Leveraging a metamodeling approach that unifies the notebook's structural elements with Python code entities, our linter enables a more contextualized analysis to identify issues across all three levels. We implemented 22 linting rules derived from the literature and applied our tool to a corpus of 5,000 notebooks from the Kaggle platform. The results reveal violations at all levels, validating the relevance of our multi-level approach and demonstrating Vespucci Linter's potential to improve the quality and reliability of ML development in notebook environments.

Thomas Dupriez, Steven Costiou, Stéphane Ducasse

As applications get developed, bugs inevitably get introduced. Often, it is unclear why a given code change introduced a given bug. To find this causal relation and more effectively debug, developers can leverage the existence of a previous version of the code, without the bug. But traditional debug-ging tools are not designed for this type of work, making this operation tedious. In this article, we propose as exploratory work the echo-debugger, a tool to debug two different executions in parallel, and the Convergence Divergence Mapping (CDM) algorithm to locate all the control-flow divergences and convergences of these executions. In this exploratory work, we present the architecture of the tool and a scenario to solve a non trivial bug.

Thomas Dupriez, Guillermo Polito, Steven Costiou et al.

Debugging is one of the most important and time consuming activities in software maintenance, yet mainstream debuggers are not well-adapted to several debugging scenarios. This has led to the research of new techniques covering specific families of complex bugs. Notably, recent research proposes to empower developers with scripting DSLs, plugin-based and moldable debuggers. However, these solutions are tailored to specific use-cases, or too costly for one-time-use scenarios. In this paper we argue that exposing a debugging scripting interface in mainstream debuggers helps in solving many challenging debugging scenarios. For this purpose, we present Sindarin, a scripting API that eases the expression and automation of different strategies developers pursue during their debugging sessions. Sindarin provides a GDB-like API, augmented with AST-bytecode-source code mappings and object-centric capabilities. To demonstrate the versatility of Sindarin, we reproduce several advanced breakpoints and non-trivial debugging mechanisms from the literature.