320 observations from a factorial experiment evaluating API latency and payload size effects on React Native application performance. Data includes loading time, rendering time, CPU usage, memory usage, and frame rate metrics. Reyvido Yoga Dwimarsha published the dataset on figshare in May 2026.
Use Cases
- Analyze the impact of API latency on UI rendering time based on the described dependent variables.
- Model the relationship between payload size and CPU usage based on the factorial design.
- Perform ANOVA or regression analysis on the effects of latency and payload size on frame rate.
- Benchmark mobile application performance under controlled network conditions.
- Replicate or extend research on API characteristics in React Native development.
Strengths
- 320 observations from a controlled factorial experiment (4×4 design).
- Independent variables are precisely defined with four latency levels (50 ms, 150 ms, 300 ms, 600 ms) and four payload size levels (<5 KB, ~20 KB, ~50 KB, ~100 KB).
- Data collection was performed in release mode on a physical Android device using a deterministic API simulator.
Limitations
- Row count is unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
- The dataset is very small (15.3 KB), indicating a limited scope.
Provenance
- Source
- figshare
- Collection Method
- Data were collected using a React Native application running in release mode with the Hermes JavaScript engine on a physical Android device. A deterministic API simulator (Node.js + Express) was used to ensure controlled latency injection and payload consistency.
- Freshness
- Last updated 2026-05-06 00:53:49