Defesa de Dissertação de Mestrado do aluno Rodrigo Galdino Ximenes.

Título da dissertação: Issues that lead to code technical debt in machine learning systems

Resumo: [Context] Technical debt (TD) in machine learning (ML) systems, much like its counterpart in software engineering (SE), holds the potential to lead to future rework, posing risks to productivity, quality, and team morale. However, better understanding code-related issues leading to TD in ML systems is still a green field. [Objective] This paper aims to identify and discuss the relevance of code-related issues leading to TD in ML code throughout the ML life cycle. [Method] The study consisted of first compiling a list of potential issues that can lead to TD in ML code by analyzing the ML life cycle phases and their typical tasks. Thereafter, the list of issues was refined by assessing the prevalence and relevance of the issues leading to ML code TD through feedback collected from industry practitioners in two focus group sessions. [Results] The study compiled a list of 34 potential issues contributing to TD in the source code of ML systems. Through two focus group sessions with nine participants, this list was refined into 30 issues leading to ML code-related TD, with 18 considered highly relevant. The data pre-processing phase was the most critical, with nine issues considered highly relevant in potentially leading to severe ML code TD. Four issues were considered highly relevant in the phases of data collection, model creation and training. The final list of issues is available to the community. [Conclusion] The list can help to raise awareness on issues to be addressed throughout the ML life cycle to minimize accruing TD, helping to improve the maintainability of the ML system.

Orientador: Prof. Dr. Marcos Kalinowski

Banca: Prof. Dr. Tatiana Escovedo | Prof. Dr. Maria Teresa Baldassarre | Prof. Dr. Rodrigo Oliveira Spínola | Prof. Dr. Helio Côrtes Vieira Lopes


Assista a defesa pelo link: https://puc-rio.zoom.us/j/4666190940?pwd=eUdNaDNSbnhEY3VWWU1DMGF0SkRjZz09