Paper

Assessing the Feasibility of Web-Request Prediction Models on Mobile Platforms. Yixue Zhao, Siwei Yin, Adriana Sejfia, Marcelo Schmitt Laser, Haoyu Wang, Nenad Medvidovic. Accepted at MOBILESoft 2021.

Link to the paper: https://arxiv.org/abs/2011.04654

Source Code

All of HipHarness’s components are implemented in Python, totaling 1,424 SLOC. [Link]

Test Results

The results of each of the 7.3 million prediction models are stored as HIPHarness-Dataset.RData file, including the following information. [Link]

• the anonymized user ID
• the number of requests sent by the user
• the number and percentage of the repeated requests
• the prediction algorithm used
• the data pruning strategy used
• the statistics of the Test Result (i.e., the output of the Test Engine as shown in Algorithm 1): cache.size, the number of requests in the hit set, the number of requests in the miss set, the number of prefetched requests, the number of hit requests, the number of miss requests
• the accuracy results: static precision, static recall, dynamic recall
• the runtime of training the prediction model and evaluating the model

Data Analysis R Script

All the data analyses are performed through R scripts based on the Test Results stored in HIPHarness-Dataset.RData, totaling 1,036 SLOC. [Link]