An empirical study of software ecosystem related tweets by npm maintainers.

The npm ecosystem is crucial for the JavaScript community and its development is significantly influenced by the opinions and feedback of npm maintainers. Many software ecosystem maintainers have utilized social media, such as Twitter, to share community-related information and their views. However, the communication between npm maintainers via Twitter in terms of topics, nature, and sentiment have not been analyzed. This study conducts an empirical analysis of tweets by npm maintainers related to the software ecosystem to understand their perceptions and opinions better. A dataset of tweets was collected and analyzed using qualitative analysis techniques to identify the topic of tweets, nature, and their sentiments. Our study demonstrates that most tweets belong to the package management category, followed by notifications and community-related information. The most frequently discussed topics among npm maintainers in the package management category are usage scenarios. It appears that the nature of tweets mostly shared by npm maintainers is information, followed by question and answer, respectively. Additionally, the sentiment analysis reveals that npm maintainers express more positive sentiments towards notification and community-related discussion while expressing more neutral opinions towards the package management related discussion. This case study provides valuable insights into the perceptions and opinions of the npm maintainers regarding the software ecosystem and can inform future development and decision making.

Dataset of network simulator related-question posts in stack overflow.

Although the use of network simulator (NS) in predicting the behavior of computer networks has increased, the users often face a variety of challenges and share them on Stack Overflow (SO). However, the challenges that users deal with have not been studied. This paper presents an NS discussion dataset extracted from SOTorrent, which consists of 2,322 NS-related question posts spanning 17 features. The process of data collection was conducted in five steps, including filtering initial post dataset using simulator tags, discovering NS-related tags, collecting the tagged posts, extracting the posts title and preprocessing for LDA (Latent Dirichlet Allocation), and finally applying the LDA topic modeling to obtain the NS posts clustered into eight different topic names. We believe that this dataset will help research community in highlighting issues faced by NS users.