Open-source repositories have facilitated developers to reuse existing software artifacts to develop and maintain new or similar kinds of software. However, finding similar repositories is a challenging task as the notion of similarity varies depending on multiple contexts, and most of the existing approaches tend to find similar repositories by comparing similar software artifacts. This paper aims to determine "whether dissimilar artifacts can be used as one of the criteria to find similar repositories?" Even though, there could be dissimilarity between two similar artifacts, there could also be similarities between two dissimilar artifacts. We define the notion of similarity by defining two categories of similar repositories. Four text-based artifacts are selected for the experiment, i.e., pull-requests, issues, commits, and readme files. The textual similarity is computed between different artifacts. The results show that similarity does exist in dissimilar artifacts. We observed that 10-20% of dissimilar artifact pairs could be used in searching similar repositories. The preliminary results show promising directions where dissimilar artifacts can also be considered while searching for similar repositories motivating the need for further research.

Open source projects are adopting faster release cycles that reflect various changes in the software. Therefore, comprehending the effects of these changes as software architecture evolves over multiple 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 users to examine the alterations in the architecture at both higher and lower levels of abstraction for 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. Users can examine two releases at a time to comprehend architectural changes between them. AC2 can support the maintainers and developers, observing changes in the project and their influence on the architecture, which allows them to examine its increasing complexity over many releases at component level. AC2 can be downloaded from https://github.com/rishalab/AC2 and the demo can be seen at https://www.youtube.com/watch?v=GNrJfZ0RCVI.

Lambda expressions are anonymous functions in Python. It is one of the alternatives to write a function definition. Syntactically, it is a single expression and defined using the keyword lambda. Lambda expression is a functional programming feature, currently in use, in many mainstream programming languages such as Python, Java8, C++11. There are few studies in C++ and Java to understand the impact of lambda expressions on programmers. These studies are focusing on the developer's adaptability to use a functional style of construct and the benefit they gain from using it. However, we are not aware of any literature on the use of lambda expressions in Python. Thus, there is a need to study lambda expressions in Python projects. In this paper, we examine 15 GitHub repositories out of 760 from our dataset, that are using Python as their primary language. In this study, we are classifying the uses of lambda expressions based on varying scenarios. We identified 13 different usages of lambda expressions from these Python repositories. This catalog is an attempt to support programmers to use lambda expressions more effectively and efficiently.