Jan Sollmann | Searcharxiv

arXiv2026

Developer Perspectives on REST API Usability: A Study of REST API Guidelines

REST is today's most widely used architectural style for providing web-based services. In the age of service-orientation (a.k.a. Software as a Service (SaaS)) APIs have become core business assets and can easily expose hundreds of operations. While well-designed APIs contribute to the commercial success of a service, poorly designed APIs can threaten entire organizations. Recognizing their relevance and value, many guidelines have been proposed for designing usable APIs, similar to design patterns and coding standards. For example, Zalando and Microsoft provide popular REST API guidelines. However, they are often considered as too large and inapplicable, so many companies create and maintain their own guidelines, which is a challenge in itself. In practice, however, developers still struggle to design effective REST APIs. To improve the situation, we need to improve our empirical understanding of adopting, using, and creating REST API guidelines. We present an interview study with 16 REST API experts from industry. We determine the notion of API usability, guideline effectiveness factors, challenges of adopting and designing guidelines, and best practices. We identified eight factors influencing REST API usability, among which the adherence to conventions is the most important one. While guidelines can in fact be an effective means to improve API usability, there is significant resistance from developers against strict guidelines. Guideline size and how it fits with organizational needs are two important factors to consider. REST guidelines also have to grow with the organization, while all stakeholders need to be involved in their development and maintenance. Automated linting provides an opportunity to not only embed compliance enforcement into processes, but also to justify guideline rules with educational explanations.

cs.SE↗

arXiv2023

Deep learning based Image Compression for Microscopy Images: An Empirical Study

With the fast development of modern microscopes and bioimaging techniques, an unprecedentedly large amount of imaging data are being generated, stored, analyzed, and even shared through networks. The size of the data poses great challenges for current data infrastructure. One common way to reduce the data size is by image compression. This present study analyzes classic and deep learning based image compression methods, and their impact on deep learning based image processing models. Deep learning based label-free prediction models (i.e., predicting fluorescent images from bright field images) are used as an example application for comparison and analysis. Effective image compression methods could help reduce the data size significantly without losing necessary information, and therefore reduce the burden on data management infrastructure and permit fast transmission through the network for data sharing or cloud computing. To compress images in such a wanted way, multiple classical lossy image compression techniques are compared to several AI-based compression models provided by and trained with the CompressAI toolbox using python. These different compression techniques are compared in compression ratio, multiple image similarity measures and, most importantly, the prediction accuracy from label-free models on compressed images. We found that AI-based compression techniques largely outperform the classic ones and will minimally affect the downstream label-free task in 2D cases. In the end, we hope the present study could shed light on the potential of deep learning based image compression and the impact of image compression on downstream deep learning based image analysis models.

eess.IV↗