Researchers in Mining (open-source) Software Repositories (MSR) often create datasets that should survive the single paper and support long-term investigation of specific phenomena. Although popular, these studies recurrently deal with similar technical limitations. For instance, public collaborative development platforms, such as GitHub, impose hourly rate limits on their API requests. Furthermore, depending on network and API conditions, queries can fail and disrupt the process. These unexpected events can slow down or even invalidate the mining. Nevertheless, there are ways to minimize the undesirable effects in a reusable way while still complying with such limitations. However, best practices are often (re-)implemented on an ad hoc basis. Whatever works.

Adopted by companies such as Netflix, Amazon, and Spotify, the microservices architectural style is now well established. Aimed at facilitating software evolution, it is renowned for modularizing a software system into microservices, implemented in various technologies. Regarding databases, practitioners opt for polyglot persistence: Each microservice is responsible for its own database(s). This influences how the architecture is implemented. The decoupling and heterogeneity of microservices and their databases spread data access points throughout the codebase, complicating program comprehension and code-data co-evolution. Developers' feedback reveals their struggles to obtain a holistic view of data access in such architectures. We present Denim, a tool that enables users to identify data access points in microservices and visualize them in an interactive treemap. Using real microservice applications, we illustrate how this tool can be used for software evolution tasks. https://figshare.com/s/6f1d970b87b7ebce939f?file=54914249

Over the past decade, microservices have gained significant popularity, impacting how applications are designed and deployed. Maintaining a comprehensive high-level view of microservices applications is essential, especially for software evolution tasks, enabling developers to understand, maintain, and optimize the complex interactions across various services. Developers struggle to obtain such an overview, particularly from a data perspective. Currently, when changes occur, they must identify data access code fragments dependent on the modified parts, or manually search through the entire codebase for potentially impacted ones. This process is time-consuming, error-prone, and cumbersome, especially in large codebases residing in multiple repositories and accessing multiple databases. We present a novel approach to support code and data coevolution comprehension. We mine data access fragments using a custom static analyzer and use interactive treemaps to generate a high-level view of the architecture, which can be explored at various levels of detail allowing, among the others, several and quick what-if analyses to assess the impact of changes (e.g., data concept modification, technology switch). As a case study, we use Overleaf, a popular online LATEX collaborative authoring platform, to evaluate our approach. We compared multiple versions and analyzed the evolution of 1.9 k code fragments associated to more than 350 data concepts across 13 microservices, 855 directories, and 3.5 k files mixing different data access technologies. We complement our analysis with insights and reflections on the promising approach.

The popularity of microservices has grown significantly over the past decade. This architectural style is praised for its ability to ease software evolution, particularly due to the modular, heterogeneous, and dynamic communication nature of microservices. This new way of designing applications has also impacted how databases are integrated. Practitioners generally opt for polyglot persistence, meaning that each microservice manages its own database(s). Decoupling, heterogeneity, and distribution introduce implicit dependencies and multiply data access endpoints. This results in added complexity and challenges in understanding change propagation, which can only be addressed through manual browsing of the codebase, a time-consuming, error-prone, and cumbersome process. A holistic view of such architectures is essential, especially for enabling developers to understand, maintain, and optimize the complex interactions across microservices, particularly from a data perspective.We extend a visualization-based approach to support both a high-level view and fine-grained inspection of microservices. Based on static analysis, we generate an interactive treemap for an entire microservices architecture, providing both an overview and the means for more detailed exploration.We evaluated our approach by assessing the scalability and effectiveness of our visualization. First, we generated interactive treemaps for 10 non-trivial microservices architectures. Then, in a qualitative user study, we asked 6 professional developers to perform specific exploration and understanding tasks (e.g., understanding architectural structure, assessing concept spreading, evaluating technology breakdown, comparing versions, identifying anti-patterns). Our results show that interactive treemaps provide the holistic view needed to aid in evolution tasks.

Microservices have become a prevalent architectural style over the past decade, emphasizing the modular and dynamic nature of heterogeneous and distributed units that communicate with each other. Moreover, they promote polyglot persistence, meaning that each microservice is responsible for managing its own database(s), often with heterogeneous technologies. One of the downsides is the increase of the number and diversity of data access endpoints and exchanges. Additionally, the decomposition introduces implicit dependencies that affect code and data understanding and co-evolution. Maintaining a comprehensive high-level view of this kind of architecture is challenging, yet essential for software evolution tasks. Previous works have already proposed holistic representations and visualizations of data access in microservices. However, these are mainly based on structural and fixed snapshots, neglecting the dynamic perspective.We present an approach to enhance static visualizations. First, we record data-access-centered execution traces in microservices architectures through a static analysis-based refinement of dynamic instrumentation. Then, we replay scenarios over an existing static treemap, animating the sequence of data accesses and highlighting hotspots in the codebase through time. Our contribution, the animated heat treemap, helps developers to understand how data management operates inside microservices. We validated our approach on Overleaf, a popular online collaborative LATEX authoring platform, with a real-world scenario. We discuss the results obtained and provide insights and reflections.

The recent advances in deep learning have been beneficial to automatic sign language recognition (SLR). However, free-to-access, usable, and accessible tools are still not widely available to the deaf community. The need for a sign language-to-text dictionary was raised by a bilingual deaf school in Belgium and linguist experts in sign languages (SL) in order to improve the autonomy of students. To meet that need, an efficient SLR system was built based on a specific transformer model. The proposed system is able to recognize 700 different signs, with a top-10 accuracy of 83%. Those results are competitive with other systems in the literature while using 10 times less parameters than existing solutions. The integration of this model into a usable and accessible web application for the dictionary is also introduced. A user-centered human-computer interaction (HCI) methodology was followed to design and implement the user interface. To the best of our knowledge, this is the first publicly released sign language-to-text dictionary using video captured by a standard camera.