[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. Despite growing attention to TD in SE, the understanding of ML-specific code-related TD remains underexplored. [Objective] This paper aims to identify and discuss the relevance of code-related issues that lead to TD in ML code throughout the ML workflow. [Method] The study first compiled a list of 34 potential issues contributing to TD in ML code by examining the phases of the ML workflow, their typical associated activities, and problem types. This list was refined through two focus group sessions involving nine experienced ML professionals, where each issue was assessed based on its occurrence contributing to TD in ML code and its relevance. [Results] The list of issues contributing to TD in the source code of ML systems was refined from 34 to 30, with 24 of these issues considered highly relevant. The data pre-processing phase was the most critical, with 14 issues considered highly relevant. Shortcuts in code related to typical pre-processing tasks (e.g., handling missing values, outliers, inconsistencies, scaling, rebalancing, and feature selection) often result in “patch fixes” rather than sustainable solutions, leading to the accumulation of TD and increasing maintenance costs. Relevant issues were also found in the data collection, model creation and training, and model evaluation phases. [Conclusion] We have made the final list of issues available to the community and believe it will help raise awareness about issues that need to be addressed throughout the ML workflow to reduce TD and improve the maintainability of ML code.

Machine learning (ML)-enabled systems are being increasingly adopted by companies aiming to enhance their products and operational processes. This paper aims to deliver a comprehensive overview of the current status quo of engineering ML-enabled systems and lay the foundation to steer practically relevant and problem-driven academic research. We conducted an international survey to collect insights from practitioners on the current practices and problems in engineering ML-enabled systems. We received 188 complete responses from 25 countries. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative analyses on the reported problems using open and axial coding procedures. Our survey results reinforce and extend existing empirical evidence on engineering ML-enabled systems, providing additional insights into typical ML-enabled systems project contexts, the perceived relevance and complexity of ML life cycle phases, and current practices related to problem understanding, model deployment, and model monitoring. Furthermore, the qualitative analysis provides a detailed map of the problems practitioners face within each ML life cycle phase and the problems causing overall project failure. The results contribute to a better understanding of the status quo and problems in practical environments. We advocate for the further adaptation and dissemination of software engineering practices to enhance the engineering of ML-enabled systems. • International survey gathering insights from 188 practitioners across 25 countries. • Overview of current practices and challenges in engineering ML-enabled systems. • Inferential quantitative analysis reporting the status quo with confidence intervals. • Qualitative analysis mapping ML life cycle challenges and causes of project failure.

The use of new information and communication technologies (ICTs) has promoted changes in the way in which information is distributed. A challenge is to explore the integration of new ICTs, such as smartphones and chatbots, to disseminate information within a regional museum. In this paper, we proposed the development of a chatbot to the Regional Museum of São João del-Rei to present his most outstanding artifact, a painting named Retrato de Menina. This paper presents the chatbot design in which was implemented on the IBM Watson platform. An evaluation with users was performed applying the Wizard of Oz method. The results of the evaluation evidenced that the proposed chatbot satisfies users, although it is necessary to improve the communication flow in order to guide users in the chatbot's interaction. The results showed that implementing several chatbots (i.e., one for each artifact of the museum) is better than making a single one, because this way avoids failures in determining dialog contexts and explore more playful interaction styles, such as chatbots that pretend to be the character of some artifact.