A Eashaan Rao

A Eashaan Rao, Sridhar Chimalakonda

The growing availability of open source projects has facilitated developers to reuse existing software artifacts and leverage them to develop new software. However, it is hard to understand the notion of similarity as it varies from developer to developer. Some developers might search for repositories with similar source code, while some might be in search of repositories with similar requirements or issues. Existing approaches tend to find similar projects by comparing similar artifacts such as source-code to source-code, API usage to API usage, documentation to documentation, and so on. Even though there is a dissimilarity between two similar artifacts, there could be a similarity between two dissimilar artifacts. Hence, in this paper, we aim to answer the question - Can we find similarity of software repositories through dissimilar artifacts?. To this end, we conduct an experiment to find similarities between three repositories, two similar and one different project comparing similar and dissimilar artifacts (documentation, commits, and source-code). We observed similarities between dissimilar artifacts such as Commits, Source Code, and Readme Files in the context of both similar and different repositories.

A Eashaan Rao, Dheeraj Vagavolu, Sridhar Chimalakonda

Open source projects are adopting faster release cycles that reflect various changes. Therefore, comprehending the effects of these changes on software's architecture over the releases becomes necessary. However, it is challenging to keep architecture in-check and add new changes simultaneously for every release. To this end, we propose a visualization tool called AC2, which allows its users to examine the alterations in the architecture at both higher and lower levels of abstraction for the python projects. AC2 uses call graphs and collaboration graphs to show the interaction between different architectural components. The tool provides four different views to see the architectural changes. The user can examine two releases at a time to comprehend the architectural changes between the releases. AC2 can support the maintainers and developers to observe changes in the project and its influence on the architecture, which allow them to see its increasing complexity over the releases at the component level. AC2 can be downloaded at https://github.com/dheerajrox/AC2 and its demo can be seen at the website https://dheerajrox.github.io/AC2doc or on youtube https://www.youtube.com/watch?v=GNrJfZ0RCVI

Sandeep Muvva, A Eashaan Rao, Sridhar Chimalakonda

Bug Localization is the process of locating potential error-prone files or methods from a given bug report and source code. There is extensive research on bug localization in the literature that focuses on applying information retrieval techniques or machine learning/deep learning approaches or both, to detect location of bugs. The common premise for all approaches is the availability of a good dataset, which in this case, is the standard benchmark dataset that comprises of 6 Java projects and in some cases, more than 6 Java projects. The existing dataset do not comprise projects of other programming languages, despite of the need to investigate specific and cross project bug localization. To the best of our knowledge, we are not aware of any dataset that addresses this concern. In this paper, we present BuGL, a large-scale cross-language dataset. BuGL constitutes of more than 10,000 bug reports drawn from open-source projects written in four programming languages, namely C, C++, Java, and Python. The dataset consists of information which includes Bug Reports and Pull-Requests. BuGL aims to unfold new research opportunities in the area of bug localization.