README.md

Topology-aware Continuous Experimentation in Microservice-based Applications - Online Appendix

This is the online appendix for our submission at ICSOC'20. It provides the heuristics' source code, a full replication package for the ranking quality evaluation, and screenshots of our interactive tooling.

Table of Contents

1. Source Code
   
2. Screenshots UI
   
3. Ranking Quality Evaluation
   

Source Code

The source code of the implemented heuristics can be found in the folder heuristics/src.

• rankingAlgorithm.ts contains the overall generic algorithm presented in Section 4
• strategies.ts contains the concrete heuristics' implementations as embodiments of the algorithm

Screenshots UI

Screenshots are presented here.

Ranking Quality

The replication package for the ranking quality evaluation can be found in the folder ranking_quality_evaluation. To execute both nDCG computation and data analysis an installation of Python 3 and R (including package tidyverse is required).

The package consists of:

• Relevance ratings for all scenarios in folder relevance
  
• Resulting rankings (heuristic output) for all scenarios in folders running (Scenario 1) and multichange (Scenario 2)
  
• Script ndcg.py to compute nDCG scores based on the relevance ratings and rankings
  
• Data Analysis script script.R to explore results and create plots
  

Relevance ratings can be adjusted to explore how nDCG scores would change. For every sub-scenario there is a respective relevance-rating file in folder relevance. Every line contains a single change (i.e., source and target) plus the rating between 0 and 4.

To compute scores based on the given relevance ratings simply execute python ndcg.py relevance. This creates a results_ndcg.csv file containing all nDCG scores for all scenarios, all heuristics, and multiple combinations of parameters (e.g., penalties, number of rankings to consider).

Use our R script script.R to explore the results and create plots on demand.