Md. Tawkat Islam Khondaker

Junaed Younus Khan, Md. Tawkat Islam Khondaker, Gias Uddin et al.

The learning and usage of an API is supported by official documentation. Like source code, API documentation is itself a software product. Several research results show that bad design in API documentation can make the reuse of API features difficult. Indeed, similar to code smells or code antipatterns, poorly designed API documentation can also exhibit 'smells'. Such documentation smells can be described as bad documentation styles that do not necessarily produce an incorrect documentation but nevertheless make the documentation difficult to properly understand and to use. Recent research on API documentation has focused on finding content inaccuracies in API documentation and to complement API documentation with external resources (e.g., crowd-shared code examples). We are aware of no research that focused on the automatic detection of API documentation smells. This paper makes two contributions. First, we produce a catalog of five API documentation smells by consulting literature on API documentation presentation problems. We create a benchmark dataset of 1,000 API documentation units by exhaustively and manually validating the presence of the five smells in Java official API reference and instruction documentation. Second, we conduct a survey of 21 professional software developers to validate the catalog. The developers agreed that they frequently encounter all five smells in API official documentation and 95.2% of them reported that the presence of the documentation smells negatively affects their productivity. The participants wished for tool support to automatically detect and fix the smells in API official documentation. We develop a suite of rule-based, deep and shallow machine learning classifiers to automatically detect the smells. The best performing classifier BERT, a deep learning model, achieves F1-scores of 0.75 - 0.97.

Junaed Younus Khan, Md. Tawkat Islam Khondaker, Iram Tazim Hoque et al.

We are presenting COVID-19Base, a knowledgebase highlighting the biomedical entities related to COVID-19 disease based on literature mining. To develop COVID-19Base, we mine the information from publicly available scientific literature and related public resources. We considered seven topic-specific dictionaries, including human genes, human miRNAs, human lncRNAs, diseases, Protein Databank, drugs, and drug side effects, are integrated to mine all scientific evidence related to COVID-19. We have employed an automated literature mining and labeling system through a novel approach to measure the effectiveness of drugs against diseases based on natural language processing, sentiment analysis, and deep learning. To the best of our knowledge, this is the first knowledgebase dedicated to COVID-19, which integrates such large variety of related biomedical entities through literature mining. Proper investigation of the mined biomedical entities along with the identified interactions among those, reported in COVID-19Base, would help the research community to discover possible ways for the therapeutic treatment of COVID-19.

Junaed Younus Khan, Md. Tawkat Islam Khondaker, Sadia Afroz et al.

The proliferation of fake news and its propagation on social media has become a major concern due to its ability to create devastating impacts. Different machine learning approaches have been suggested to detect fake news. However, most of those focused on a specific type of news (such as political) which leads us to the question of dataset-bias of the models used. In this research, we conducted a benchmark study to assess the performance of different applicable machine learning approaches on three different datasets where we accumulated the largest and most diversified one. We explored a number of advanced pre-trained language models for fake news detection along with the traditional and deep learning ones and compared their performances from different aspects for the first time to the best of our knowledge. We find that BERT and similar pre-trained models perform the best for fake news detection, especially with very small dataset. Hence, these models are significantly better option for languages with limited electronic contents, i.e., training data. We also carried out several analysis based on the models' performance, article's topic, article's length, and discussed different lessons learned from them. We believe that this benchmark study will help the research community to explore further and news sites/blogs to select the most appropriate fake news detection method.