gu-youngfeng / moconfig

This project offers the source code of prototype MoConfig.

MoConfig

This project shows the prototype MoConfig. MoConfigSampling (in Java) and MoConfigComparison (in Python) are two main parts, implementing the process of MoConfig sampling and the comparasion with the rank-based method, respectively.

1. Dataset and environments

The 20 raw datasets we used are saved in the directory MoConfigComparison/raw_data/.

The environments of MoConfig project includes JDK 1.7/1.8, Python 3.X. Moreover, sklearn, pandas, numpy libraries should also be installed.

2. Experiment steps

Step 1: We run the rank-based method to divide each dataset into the training pool, validation pool, and test pool. Above sets are generated and saved in the directory MoConfigComparison/parse_data/.

Step 2: We combine the training pool and validation pool generated by rank-based method into a combiantion dataset (MoConfigComparison/parse_data/combined_train/). We copy this combination dataset to the directory MoConfigSampling/testbed/input/ and take it as the input of MoConfig.

Step 3: We run the MoConfig to sample the optimal sampling sets from the training pool. The optimal sampling sets are saved in the directory MoConfigSampling/testbed/output/.

Step 4: We find the best configuration based on the sampling sets generated by MoConfig. The prediction results are saved in MoConfigComparison/experiments/moconfig/.

Step 5: We compare the results between the rank-based and MoConfig methods. The visualized results of comparision are saved in MoConfigComparison/pics/.

3. Implementation

Here, we list the related code entries to above steps. For instance, we just run the main function in rankbased.py to implement the Step 1 and Step 2.

Step	Running entry	Time cost
1,2	MoConfigComparison/rankbased.py	Around 150 seconds
3	MoConfigSampling/src/main/java/cn/edu/whu/cstar/experiments/Launcher.java	Around 20 minutes
4	MoConfigComparison/moconfig.py	Around 40 minutes
5	MoConfigComparison/experiments.py	Arounds 150 seconds