Background: With the increasing number of software users using social media forums, providing feedback about the OSS projects, the developer’s need to address this feedback to understand the requirements of an OSS project. As different tools support different structures for the feedback, the need to classify, prioritize and filter them into a fundamental set of categories persists. Some of the feedback includes images from users, along with the text. These images may vary from a screenshot of the bug, encountered by the user to a code snippet modification as required by the user. The significance of how these images help the developers in fixing the bug is not clear. Objectives: This thesis aims to identify the underlying advantages of using images in the feedback or bug report submitted by the user for an OSS project to the developers. The goal is to find the extent to which different image attributes help the developer’s in understanding the issue suggested in the feedback or bug report. The research also aims to classify the view of practitioners regarding which image attributes affect the most and to propose a simple DSS model that can possibly be used by users and developers while attaching images in the feedback or bug reports. Methods: In this research, we have conducted an empirical study using systematic mapping and a survey study. We identified 28 research articles form systematic mapping using a search string and snowballing process to extract different image attributes. To triangulate and verify the results of the systematic map, we have conducted an online questionnaire replied by 32 respondents experienced in contributing to the OSS community. The usability of the image attributes has been evaluated from the responses received. Both quantitative and descriptive statistical analysis techniques were used to analyze the results. Results: From the 28 research articles identified for the systematic mapping study, we have extracted 11 image attributes that influence the developers in interpreting the software requirements from the images attached to feedback or bug reports. Of the identified image attributes, image quality and image resolution are considered to be the most useful attributes by the survey respondents. Moreover, two new image attributes (timestamp and steps to reproduce) are reported from the survey study. Conclusions: The identification and validation of the image attributes suggest the potential use of images in feedback and bug reports. Furthermore, these image attributes provide additional information to the developers in understanding the software requirements from the users perspective clearly. We propose a simple DSS model that can be used by the users and the developers before attaching an image along with the feedback or the bug reports to the developing OSS communities to promote further usage of images in feedback and bug reports for OSS.

The proliferation of open-source software development platforms has given rise to various online social communities where developers can seamlessly collaborate, showcase their projects, and exchange knowledge and ideas. GitHub stands out as a preeminent platform within this ecosystem. It offers developers a space to host and disseminate their code, participate in collaborative ventures, and engage in meaningful dialogues with fellow community members. This dissertation embarks on a comprehensive exploration of various facets of software development communities on GitHub, with a specific focus on innovation diffusion, repository popularity dynamics, code quality enhancement, and user commenting behaviors. This dissertation introduces a popularity-based model that elucidates the diffusion of innovation on GitHub. We scrutinize the influence of a repository's popularity on the transfer of knowledge and the adoption of innovative practices, relying on a dataset encompassing GitHub fork events. Through a meticulous analysis of developers' collaborative coding efforts, this dissertation furnishes valuable insights into the impact of social factors, particularly popularity, on the diffusion of innovation. Furthermore, we introduce a novel approach to computing a weight-based popularity score, denoted as the Weighted Trend Popularity Score (WTPS), derived from the historical trajectory of repository popularity indicators, such as fork and star counts. The accuracy of WTPS as a comprehensive repository popularity indicator is assessed, and the significance of having a singular metric to represent repository popularity is underscored. We delve into the realm of code quality on GitHub by examining it from the perspective of code reviews. Our analysis centers on understanding the code review process and presents an approach rooted in regularity to foster superior code quality by enforcing coding standards. In the concluding phase of our research, we investigate the intricacies of communication within technology-related online communities. Our attention is drawn to the impact of user popularity on communication, as elucidated through an examination of comment timelines and commenting communities. To contextualize our findings, we compare the behavioral patterns of GitHub developers and users on other platforms, such as Reddit and Stack Overflow.