부산대학교 도서관

Software developers are increasingly using cloud-based services that provide machine learning capabilities to implement ‘intelligent’ features. Studies show that incorporating machine learning into an application increases technical debt, creates data dependencies, and introduces uncertainty due to their non-deterministic behaviour. We know very little about the emotional state of software developers who have to deal with such issues; and the impacts on productivity. This paper presents a preliminary effort to better understand the emotions of developers when experiencing issues with these services with the wider goal of discovering potential service improvements. We conducted a landscape analysis of emotions found in 1,425 Stack Overflow questions about a specific and mature subset of these cloud-based services, namely those that provide computer vision techniques. To speed up the emotion identification process, we trialled an automatic approach using a pre-trained emotion classifier that was specifically trained on Stack Overflow content, EmoTxt, and manually verified its classification results. We found that the identified emotions vary for different types of questions, and a discrepancy exists between automatic and manual emotion analysis due to subjectivity.