PhD Dissertation Proposal by Abhishek SHARMA | Social Software Development: Insights and Solutions

Over last few decades, the way software is developed has changed drastically. From being an activity performed by developers working individually to develop standalone programs, it has transformed into a highly collaborative and cooperative activity. Today software programs are written by developers who contribute to various projects and teams that may be spread across the globe. Software development today can be considered as a participatory culture, where developers coordinate and engage together to develop software while continuously learning from one another and creating knowledge. In order to support their communication and collaboration needs, software developers often use a variety of social media channels. These channels help software developers to connect with like-minded developers and explore collaborations on software projects of interest. Also, as software development is a field which evolves continuously and at very rapid pace, developers use social media channels in order to keep up-to-date with the latest developments in software communities of their interest. However, developers face a lot of challenges while trying to make use of various social media channels. As the volume of contents produced on social media is huge developers often face the problem of information overload while using various social media channels. Also creating and maintaining a relevant network among a huge number of possible connections is challenging for developers.

The works discussed in this talk focus on addressing the above challenges with respect to Twitter, a social media popular among developers to get the latest technology updates, as well as connect with other developers. The insights found and techniques proposed can also be applicable in addressing similar problems in other social media used by developers.

Abhishek SHARMA is a PhD candidate in School of Information Systems, Singapore Management University. He joined SMU in 2014, and is supervised by Associate Professor David Lo. His research interests are in application of data mining and text analysis techniques for software domain specific information extraction from social media used by software developers.