How Do Microservice API Patterns Impact Understandability? A Controlled Experiment
February 21, 2024 Β· Declared Dead Β· π International Conference on Software Architecture
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Justus Bogner, Pawel WΓ³jcik, Olaf Zimmermann
arXiv ID
2402.13696
Category
cs.SE: Software Engineering
Citations
4
Venue
International Conference on Software Architecture
Last Checked
4 months ago
Abstract
Microservices expose their functionality via remote Application Programming Interfaces (APIs), e.g., based on HTTP or asynchronous messaging technology. To solve recurring problems in this design space, Microservice API Patterns (MAPs) have emerged to capture the collective experience of the API design community. At present, there is a lack of empirical evidence for the effectiveness of these patterns, e.g., how they impact understandability and API usability. We therefore conducted a controlled experiment with 6 microservice patterns to evaluate their impact on understandability with 65 diverse participants. Additionally, we wanted to study how demographics like years of professional experience or experience with MAPs influence the effects of the patterns. Per pattern, we constructed two API examples, each in a pattern version "P" and a functionally equivalent non-pattern version "N" (24 in total). Based on a crossover design, participants had to answer comprehension questions, while we measured the time. For five of the six patterns, we identified a significant positive impact on understandability, i.e., participants answered faster and / or more correctly for "P". However, effect sizes were mostly small, with one pattern showing a medium effect. The correlations between performance and demographics seem to suggest that certain patterns may introduce additional complexity; people experienced with MAPs will profit more from their effects. This has important implications for training and education around MAPs and other patterns.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted