Domain experts play an important role in data science, as their knowledge can unlock valuable insights from data. As they often lack technical skills required to analyze data, they need collaborations with technical experts. In these joint efforts, productive collaborations are critical not only in the phase of constructing a data science task, but more importantly, during the execution of a task. This need stems from the inherent complexity of data science, which often involves user-defined functions or machine-learning operations. Consequently, collaborators want various interactions during runtime, such as pausing/resuming the execution, inspecting an operator's state, and modifying an operator's logic. To achieve the goal, in the past few years we have been developing an open-source system called Texera to support collaborative data analytics using GUI-based workflows as cloud services. In this paper, we present a holistic view of several important design principles we followed in the design and implementation of the system. We focus on different methods of sending messages to running workers, how these methods are adopted to support various runtime interactions from users, and their trade-offs on both performance and consistency. These principles enable Texera to provide powerful user interactions during a workflow execution to facilitate efficient collaborations in data analytics.

Abstract Technical debt (TD) is a metaphor used to refer to the added software system costs acquired from taking shortcuts. Unfortunately, large amounts of TD can lead to serious consequences, and, thus, the management of TD is essential. Due to TD being a relatively new subject of study, many aspects of TD remain ambiguous. Fortunately, Twitter has been proven to hold a wealth of information on many subjects. As such, this survey study aims to gain a better understanding on how interest in TD has evolved over time and how TD is addressed on Twitter. A total of 128,897 TD‐related tweets were scrapped from Twitter and analyzed using a number of proxy measures and Latent Dirichlet Allocation (LDA). The results revealed that interest in TD on Twitter has been generally increasing since the platform's early stages. Furthermore, TD‐related tweets were found to revolve around 11 distinct categories. The TD in games category was discovered to be the most popular category, followed by TD communication and TD repayment. The results highlight that TD is a diverse and overarching topic that contains many potential avenues for further exploration. Software engineering researchers, practitioners, and educators can utilize this study to help steer their TD‐related future efforts.

Software engineering ethics has been an interest of the software engineering community for over two decades, as evidenced by the creation of the joint ACM/IEEE-CS Soft-ware Engineering Code of Ethics. When reviewing the cur-rent software engineering literature, it was found that Stack Exchange question-and-answer (Q&A) websites are particularly good means of gaining a better understanding on a given subject: what topics are addressed within a subject area, the popularity of these topics, and the difficulty of these topics. As such, this paper utilizes Stack Exchange Q&A websites to review software engineering ethics and presents a case study that aims to provide a better understanding of the presence of software engineering ethics on three popular Stack Exchange Q&A websites, which are Stack Overflow (SO), Software Engineering (SE), and Project Management (PM). The study analyzes over 2,170 posts, using Latent Dirichlet Allocation (LDA), to better understand what ethic-related topics are discussed. Subsequently, the authors analyzed the popularity and difficulty of each identified topic. The study found that users discuss six ethic-related topics, which are as follows: web scraping, software quality, software security, open-source software usage, team and employers, and software billing. Moreover, the results revealed that software security is the most popular and the most difficult topic. This study highlights ethic-related challenges that are faced by Q&A website users. Software practitioners, researchers, and educators can utilize the results presented in this study to pursue new avenues when addressing pressing issues related to software engineering ethics.