Influence Of Developer Sentiment And Stack Overflow Developers On Open Source Project Success: An Empirical Examination

The collaborative effort of software developers around the world produces Open Source Software (OSS) products, and most importantly, the source code of the software product is shared publicly. A recent survey of 1300 IT professionals by Black Duck Software showed that the percentage of companies using open source software grew from 42% to 78% between 2010 and 2015. There has been a significant increase in the formation of self-organizing virtual teams to produce open source software products and services. The current literature does not address the factors affecting the success of open source projects through the lens of self-organizing virtual teams and the sentiment among the developers and the sentiment among software developers. This phenomenon suggests a need to understand how successful project teams are created in a virtual collaborative environment. This research investigates how successful virtual teams are formed through the influence of an online developer community. The focus of this research is to assess how the online developer community, Stack Overflow (SO), influences the success of open source projects. More precisely, the study empirically tests the influence of the SO community on successful Github (GH) projects. The investigation also empirically examines how the ties among the software developers in the SO community initiate the self-creation of OSS project teams. The research also explores the perception of the developers about open source projects. Furthermore, the study probes the impact of OSS artifacts, namely “feature” and “patch” requests, on open source projects. The findings indicate that the perception of the developers in the SO community, prior ties among the developers in the community, and the artifact type of the project are the factors that influence the success of OSS projects. The research discusses the implications of the outcomes concerning self-organizing open source project teams

Opinion Mining for Software Development: A Systematic Literature Review

Opinion mining, sometimes referred to as sentiment analysis, has gained increasing attention in software engineering (SE) studies. SE researchers have applied opinion mining techniques in various contexts, such as identifying developers’ emotions expressed in code comments and extracting users’ critics toward mobile apps. Given the large amount of relevant studies available, it can take considerable time for researchers and developers to figure out which approaches they can adopt in their own studies and what perils these approaches entail. We conducted a systematic literature review involving 185 papers. More specifically, we present 1) well-defined categories of opinion mining-related software development activities, 2) available opinion mining approaches, whether they are evaluated when adopted in other studies, and how their performance is compared, 3) available datasets for performance evaluation and tool customization, and 4) concerns or limitations SE researchers might need to take into account when applying/customizing these opinion mining techniques. The results of our study serve as references to choose suitable opinion mining tools for software development activities, and provide critical insights for the further development of opinion mining techniques in the SE domain