Where Does LDA Sit for GitHub A lot of research in Software Engineering (SE) automatically extract topics of the text data and use the results directly or as a feature for a machine learning method. Research has shown that the majority of studies in SE use Latent Dirichlet Allocation (LDA) as the topic modeling approach. Similarly, there is a lot of work that apply LDA on GitHub data. However, there is no study that explores whether LDA is a good choice compared to other algorithms, nor is there any to investigate the effects of specific preprocessing steps on its performance. In this paper, we explore a large dataset of GitHub repositories and apply two main topic modeling algorithms, LDA (3 variants) and NonNegative Matrix Factorization (NMF), in several experiments with different experimental settings. The results show that LDA results in a higher coherence score compared to NMF. However, care should be taken in the choice of LDA algorithm, setting its parameters, and the text preprocessing steps. The results of this paper benefit SE researchers who apply intelligent techniques using LDA.

Big Data Trends in Bioinformatics The amount of biological data available for both the academic community and industry has increased due to the rise of high throughput omics technologies, biotechnology, and health monitoring. This scenario demands efficient storage and analysis of the massive amount of data involved. This work aimed to investigate the scientific literature to map topics related to the use of Big Data in Bioinformatics. Due to the large number of relevant papers to inspect, we employed a threestep datadriven systematic approach. We performed a literature search and selected works related to the theme in three search bases (Scopus, ACM, and Web of Science). Then, we proceeded to a text mining step to analyze terms frequently used in the documents and prepare the documents to be inspected. Afterward, we performed a topic modeling using the LDA (Latent Dirichlet Allocation) algorithm. Twenty groups of topics were obtained and drawn in twenty trend topics, which show a revealing stateoftheart of the Big Data application in Bioinformatics.