Nicolas Anquetil

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.

Houékpétodji Mahugnon Honoré, Nicolas Anquetil, Stéphane Ducasse et al.

One factor of success in software development companies is their ability to deliver good quality products, fast. For this, they need to improve their software development practices. We work with a medium-sized company modernizing its development practices. The company introduced several practices recommended in agile development. If the benefits of these practices are well documented, the impact of such changes on the developers is less well known. We follow this modernization before and during the COVID-19 outbreak. This paper presents an empirical study of the perceived benefit and drawback of these practices as well as the impact of COVID-19 on the company's employees. One of the conclusions, is the additional difficulties created by obsolete technologies to adapt the technology itself and the development practices it encourages to modern standards.

Nicolas Anquetil, Anne Etien, Mahugnon H. Houekpetodji et al.

Advanced reverse engineering tools are required to cope with the complexity of software systems and the specific requirements of numerous different tasks (re-architecturing, migration, evolution). Consequently, reverse engineering tools should adapt to a wide range of situations. Yet, because they require a large infrastructure investment, being able to reuse these tools is key. Moose is a reverse engineering environment answering these requirements. While Moose started as a research project 20 years ago, it is also used in industrial projects, exposing itself to all these difficulties. In this paper we present ModMoose, the new version of Moose. ModMoose revolves around a new meta-model, modular and extensible; a new toolset of generic tools (query module, visualization engine, ...); and an open architecture supporting the synchronization and interaction of tools per task. With ModMoose, tool developers can develop specific meta-models by reusing existing elementary concepts, and dedicated reverse engineering tools that can interact with the existing ones.

Leonardo Humberto Silva, Daniel Hovadick, Marco Tulio Valente et al.

With the increasing usage of JavaScript in web applications, there is a great demand to write JavaScript code that is reliable and maintainable. To achieve these goals, classes can be emulated in the current JavaScript standard version. In this paper, we propose a reengineering tool to identify such class-like structures and to create an object-oriented model based on JavaScript source code. The tool has a parser that loads the AST (Abstract Syntax Tree) of a JavaScript application to model its structure. It is also integrated with the Moose platform to provide powerful visualization, e.g., UML diagram and Distribution Maps, and well-known metric values for software analysis. We also provide some examples with real JavaScript applications to evaluate the tool.

Nicolas Anquetil, Jannik Laval

Software re-modularization is an old preoccupation of reverse engineering research. The advantages of a well structured or modularized system are well known. Yet after so much time and efforts, the field seems unable to come up with solutions that make a clear difference in practice. Recently, some researchers started to question whether some basic assumptions of the field were not overrated. The main one consists in evaluating the high-cohesion/low-coupling dogma with metrics of unknown relevance. In this paper, we study a real structuring case (on the Eclipse platform) to try to better understand if (some) existing metrics would have helped the software engineers in the task. Results show that the cohesion and coupling metrics used in the experiment did not behave as expected and would probably not have helped the maintainers reach there goal. We also measured another possible restructuring which is to decrease the number of cyclic dependencies between modules. Again, the results did not meet expectations.