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Under conditions of increasing traffic and limited parking options, mobile apps for real-time monitoring and booking of available spots in public parking lots are becoming an indispensable tool for making our daily lives easier. These apps offer convenience, save time, and reduce the hassles associated with finding a parking spot, while also contributing to a more efficient and organized urban space. Additionally, they help reduce traffic congestion and lower pollution levels by encouraging users to make more rational use of available parking resources, which benefits all city residents. This article describes the development of a mobile application for real-time booking of parking spots. The proposed application integrates useful features from existing mobile apps, adds its own new features, and is adapted to the conditions of life in Bulgaria. Its main advantage is that it is available completely free of charge. It offers the following functionalities: booking or recommending parking spots; checking parking spot availability; and providing feedback on current and past parking spot bookings, including ratings and comments. The application’s interface is intuitive, which makes it easy to use. The following technologies were used in its development: Java, Android Studio, XML, Gradle, Android SDK, and Firebase. The application was compared to similar ones, incorporating their best features and adding new ones that improve upon them. The current work aims to help Bulgarian users save time and money spent looking for parking spots. Full article

Software architecture plays an important role in software development, especially in software quality and maintenance. Understanding the impact of certain architectural patterns on software quality and verification of software requirements has become increasingly difficult with the increasing complexity of codebases in recent years. Researchers over the years have proposed automated approaches based on machine learning. However, there is a lack of benchmark datasets and more accurate machine learning (ML) approaches. This paper presents an ML-based approach for software architecture detection, namely, MVP (Model–View–Presenter) and MVVM (Model–View–ViewModel). Firstly, we present a labeled dataset that consists of 5973 data points retrieved from GitHub. Nine ML methods are applied for detection of software architecture from source code metrics. Using precision, recall, accuracy, and F1 score, the outstanding ML model performance is 83%, 83%, 83%, and 83%, respectively. The ML model’s performance is validated using k-fold validation (k = 5). Our approach outperforms when compared with the state-of-the-art. Full article