Md Abdullah Al Alamin

Junaed Younus Khan, Md Abdullah Al Alamin

The World Health Organization (WHO) has recommended wearing face masks as one of the most effective measures to prevent COVID-19 transmission. In many countries, it is now mandatory to wear face masks, specially in public places. Since manual monitoring of face masks is often infeasible in the middle of the crowd, automatic detection can be beneficial. To facilitate that, we explored a number of deep learning models (i.e., VGG1, VGG19, ResNet50) for face-mask detection and evaluated them on two benchmark datasets. We also evaluated transfer learning (i.e., VGG19, ResNet50 pre-trained on ImageNet) in this context. We find that while the performances of all the models are quite good, transfer learning models achieve the best performance. Transfer learning improves the performance by 0.10\%--0.40\% with 30\% less training time. Our experiment also shows these high-performing models are not quite robust for real-world cases where the test dataset comes from a different distribution. Without any fine-tuning, the performance of these models drops by 47\% in cross-domain settings.

Md Abdullah Al Alamin, Gias Uddin

In the past decades, the revolutionary advances of Machine Learning (ML) have shown a rapid adoption of ML models into software systems of diverse types. Such Machine Learning Software Applications (MLSAs) are gaining importance in our daily lives. As such, the Quality Assurance (QA) of MLSAs is of paramount importance. Several research efforts are dedicated to determining the specific challenges we can face while adopting ML models into software systems. However, we are aware of no research that offered a holistic view of the distribution of those ML quality assurance challenges across the various phases of software development life cycles (SDLC). This paper conducts an in-depth literature review of a large volume of research papers that focused on the quality assurance of ML models. We developed a taxonomy of MLSA quality assurance issues by mapping the various ML adoption challenges across different phases of SDLC. We provide recommendations and research opportunities to improve SDLC practices based on the taxonomy. This mapping can help prioritize quality assurance efforts of MLSAs where the adoption of ML models can be considered crucial.

Md Abdullah Al Alamin, Sanjay Malakar, Gias Uddin et al.

Low-code software development (LCSD) is an emerging paradigm that combines minimal source code with interactive graphical interfaces to promote rapid application development. LCSD aims to democratize application development to software practitioners with diverse backgrounds. Given that LCSD is relatively a new paradigm, it is vital to learn about the challenges developers face during their adoption of LCSD platforms. The online developer forum, Stack Overflow (SO), is popular among software developers to ask for solutions to their technical problems. We observe a growing body of posts in SO with discussions of LCSD platforms. In this paper, we present an empirical study of around 5K SO posts (questions + accepted answers) that contain discussions of nine popular LCSD platforms. We apply topic modeling on the posts to determine the types of topics discussed. We find 13 topics related to LCSD in SO. The 13 topics are grouped into four categories: Customization, Platform Adoption, Database Management, and Third-Party Integration. More than 40% of the questions are about customization, i.e., developers frequently face challenges with customizing user interfaces or services offered by LCSD platforms. The topic "Dynamic Event Handling" under the "Customization" category is the most popular (in terms of average view counts per question of the topic) as well as the most difficult. It means that developers frequently search for customization solutions such as how to attach dynamic events to a form in low-code UI, yet most (75.9%) of their questions remain without an accepted answer. We manually label 900 questions from the posts to determine the prevalence of the topics' challenges across LCSD phases. We find that most of the questions are related to the development phase, and low-code developers also face challenges with automated testing.