In this paper, we present our MSR Hackathon 2022 project that replicates an existing Gitter study [2] using GrimoireLab. We compare the previous study's pipeline with our GrimoireLab implementation in terms of speed, data consistency, organization, and the learning curve to get started. We believe our experience with Grimoire-Lab can help future researchers in making the right choice while implementing their data pipelines over Gitter and Github data.

Machine learning, a branch of Artificial Intelligence, is now popular in software engineering community and is successfully used for problems like bug prediction, and software development effort estimation. Developers' understanding of machine learning, however, is not clear, and we require investigation to understand what educators should focus on, and how different online programming discussion communities can be more helpful. We conduct a study on Stack Overflow (SO) machine learning related posts using the SOTorrent dataset. We found that some machine learning topics are significantly more discussed than others, and others need more attention. We also found that topic generation with Latent Dirichlet Allocation (LDA) can suggest more appropriate tags that can make a machine learning post more visible and thus can help in receiving immediate feedback from sites like SO.

With the widespread use of mobile devices relying on limited battery power, the burden of optimizing applications for energy has shifted towards the application developers. In their quest to develop energy efficient applications, developers face the hurdle of measuring the effect of software change on energy consumption. A naive solution to this problem would be to have an exhaustive suite of test cases that are executed upon every change to measure their effect on energy consumption. This method is inefficient and also suffers from environment dependent inconsistencies. A more generalized method would be to relate software structural metrics with its energy consumption behavior. Previous attempts to relate change in objectoriented metrics to their effects on energy consumption have been inconclusive. We observe that structural information is global and executed tests are rarely comprehensive in their coverage, this approach is prone to errors. In this paper, we present a methodology to relate software energy consumption with software structural metrics considering the test case execution traces. Furthermore, we demonstrate that software structural metrics can be reliably related to energy consumption behavior of programs using several versions of three open-source iteratively developed android applications. We discover that by using our approach we are able to identify strong correlations between several software metrics and energy consumption behavior.