Publication Type
PhD Dissertation
Version
publishedVersion
Publication Date
12-2018
Abstract
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. 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. However, developers face a lot of challenges while trying to make use of various social media channels. As the volume of content produced on social media is huge developers often face the problem of information overload while using these channels. Also creating and maintaining a relevant network among a huge number of possible connections is challenging for developers. The works performed in this dissertation 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 first three works performed as a part of this dissertation deal with understanding the software engineering content produced on Twitter and how it can be harnessed for automatic mining of software engineering related knowledge. The last work aims at understanding what kind of accounts software developers follow on Twitter, and then proposes an approach which can help developers to find software experts on Twitter. The following paragraphs briefly describe the works that have been completed as part of this dissertation and how they address the aforementioned challenges.
In the first work performed as part of the dissertation, an exploratory study was conducted to understand what kind of software engineering content is popular among developers in Twitter. The insights found in this work help to understand the content that is preferred by developers on Twitter and can guide future techniques or tools which aim to extract information or knowledge from software engineering content produced on Twitter. In the second work, a technique was developed which can automatically differentiate content related to software development on Twitter from other non-software content. This technique can help in creating a repository of software related content extracted from Twitter, that can be used to create downstream tools which can do tasks such as mining opinions about APIs, best practices, recommending relevant links to read, etc. In the third work, Twitter was leveraged to automatically find URLs related to a particular domain, as Twitter makes it possible to infer the network and popularity information of users who tweet a particular URL. 14 features were proposed to characterize each URL by considering webpage contents pointed by it, popularity and content of tweets mentioning it, and the popularity of users who shared the URL on Twitter.
In the final work of this dissertation, an approach has been proposed to address the challenge developers face in finding relevant developers to follow on Twitter. A survey was done with developers, and based on its analysis, an approach was proposed to identify software experts on Twitter, provided a given software engineering domain. The approach works by extracting 32 features related to Twitter users, with features belonging to the categories such as Content, Network, Profile, and GitHub. These features are then used to build a classifier which can identify a Twitter user as a software expert of a given domain or otherwise. The results show that our approach is able to achieve F-Measure scores of 0.522-0.820 on the task of identifying software experts, achieving an improvement of at-least 7.63% over the baselines.
Degree Awarded
PhD in Information Systems
Discipline
Social Media | Software Engineering
Supervisor(s)
LO, David
Publisher
Singapore Management University
City or Country
Singapore
Citation
SHARMA, Abhishek.
Social software development: Insights and solutions. (2018).
Available at: https://ink.library.smu.edu.sg/etd_coll/166
Copyright Owner and License
Author
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.