:PROPERTIES:
:ID:       f308642d-fcf3-410b-b154-d60582e112a2
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#+title: ise_week_2
#+filetags: :uni:notes:


* DONE Different Configuration Sampling Methods (pages 8-19)
* DONE Configuration Encodings (pages 21-28)
* DONE Single Environment Learning (DaL) (pages 39-46)

* 2.1
** Configuration Sampling
In general machine learning problem, we dont care where the data comes from , but here we do.

Configuration sampling is used to select representative samples for learning performance models.

- Types of options:
  - Binary (e.g., on/off)
  - Numeric (e.g., value ranges)
- Goal: Balance model accuracy with sampling effort.

** Binary Sampling Strategies

*** Option-wise Strategy
- Each binary option is selected at least once in some configuration.
- Minimize other options to reduce unknown interaction effects.
- Size: Linear in the number of binary options.

*** T-wise Strategy
- Covers all T-wise combinations of options (T ≥ 2).
- Example (2-wise): {001}, {010}, {100}, {111}
- Size: Exponential in T.

*** Negative Option-wise Strategy
- For each option: one configuration where it is disabled, all others enabled.
- Adds one all-yes configuration.
- Size: Linear.
- Example (3 options): {110}, {101}, {011}, {111}
- you can see the 4th one is an all-yes configuration

*** Random (Binary)
- Select n configurations randomly.
- Simple but may be less representative.

*** Difference Between Option-wise and Negative Option-wise Strategies

Both strategies are used for sampling configurations in systems with binary options, but they focus on different aspects of option selection.

**** Option-wise Strategy
- **Goal:** Ensure each option is enabled (selected) at least once across configurations.
- For every binary option, create a configuration where it is **on**.
- Other options are minimized to avoid unknown interactions.
- **Focus:** Testing the **presence** of each option.
- **Example (3 options):**
  - {100} → Option 1 enabled, others off
  - {010} → Option 2 enabled, others off
  - {001} → Option 3 enabled, others off

**** Negative Option-wise Strategy
- **Goal:** Ensure each option is disabled (deselected) at least once.
- For each option, create a configuration where it is **off**, and **all others are on**.
- Also includes a configuration where all options are **on**.
- **Focus:** Testing the **absence** of each option.
- **Example (3 options):**
  - {110} → Option 3 disabled
  - {101} → Option 2 disabled
  - {011} → Option 1 disabled
  - {111} → All options enabled

**** Comparison Summary

| Feature                 | Option-wise              | Negative Option-wise          |
|-------------------------+--------------------------+-------------------------------|
| Focus                   | Presence of each option  | Absence of each option        |
| What is varied          | Each option enabled once | Each option disabled once     |
| Other options in config | Typically disabled       | Typically enabled             |
| Additional config?      | Not required             | Yes, includes all-on config   |
| Use case                | Minimal presence testing | Influence of removing options |

** Non-Binary (Numeric) Sampling Strategies

*** One-Factor-At-A-Time (OFAT)
- Assumes no interactions among options.
- Varies one option at a time, others fixed at center values.
- Size: Linear in number of options.
- Example (values = 1,3,5): {333}, {533}, {133}, {353}, {313}, {331}, {335}

*** Box-Behnken Design (BBD)
- Captures quadratic effects and 2-wise interactions.
- Uses subset of 3^k full factorial (min, center, max).
- Size: Exponential in number of options.
- Example: {111}, {113}, {115}, {131}, {151}, etc.

*** Central Composite Design (CCD)
- Combines:
  - 2^k factorial points
  - 2k axial points at α-distance
  - 1 center point
- Captures curvature and interactions.
- Example: 8 full factorial + 6 axial + {333}

*** Plackett-Burman Design (PBD)
- Focus on main effects, assumes negligible interactions.
- Uses predefined seeds, e.g., PBD(9,3)
- First config from seed, rest by right-shifting seed.
- Uses indices only for values.
- Example: If O = {1,100,1000,10000,100000}, index 3 could mean 1, 1000, 100000

*** Random (Non-Binary)
- Random selection of numeric configurations.
- Risk of non-uniformity and clustering.
- Can negatively impact learning performance.

** Mixed Variable Sampling

Some systems include both binary and non-binary (numeric) configuration options.
These are referred to as **mixed systems**.

- Requires hybrid or combined strategies to ensure representative coverage.
- One approach: **Permute over the mixed space** by combining possible binary and numeric value combinations.
- This can grow combinatorially, so sampling techniques may be needed to reduce the total number of permutations.

*** Example
- Non-binary configs: {0.1, 0.4, 5}, {0.2, 0.4, 7}, {0.2, 0.7, 5}
- Binary configs: {1,0}, {1,1}
- Full mixed permutations:
  - {0.1, 0.4, 5, 1, 0}
  - {0.1, 0.4, 5, 1, 1}
  - {0.2, 0.4, 7, 1, 0}
  - {0.2, 0.4, 7, 1, 1}
  - {0.2, 0.7, 5, 1, 0}
  - {0.2, 0.7, 5, 1, 1}

* 2.2
** Single Environment Learning: DeepPerf
  Source: Ha & Zhang, ICSE 2019

  DeepPerf is an early approach using deep neural networks (>3 layers) to predict software performance in configurable systems.

  - Designed to address:
    - Small data size: Limited measurements available.
    - Feature sparsity: Only a few configuration options significantly impact performance.
    - Network instability: Tackled with tailored hyperparameter tuning.

** Limitation of DeepPerf
  - Does not handle sample sparsity, a major issue in configuration performance prediction.

** Improved Approach: Divide-and-Learn (DaL)
  Source: Gong & Chen, ESEC/FSE 2023

*** Key Problem: Sample Sparsity
   - Caused by:
     - Inherited feature sparsity.
     - Small configuration changes leading to drastic performance shifts.
     - Not all configurations being valid.
   - Training data is sparse due to expensive measurements.

*** Key Properties of Configuration Landscape
   1. Intra-division smoothness: Configurations in the same division show smooth performance variations.
   2. Inter-division sharpness: Cross-division configurations differ significantly, possibly on key options.

   Risk: Limited data might lead to overfitting within divisions.

*** Architecture of DaL

   Three Goals:
   1. Divide the configuration data into meaningful divisions → function ϕ
   2. Learn a local model for each division → function μ
   3. Assign new configurations to the correct local model → using ϕ and μ

   - Implementation:
     - CART (Decision Tree) is used for dividing.
     - DeepPerf models are trained within each division.
     - Random Forest is used for classifying unseen configurations into divisions.

*** Trade-off: Number of Divisions
   - More divisions → better at tackling sparsity, but less data per model → risks underfitting.
   - Need to balance:
     - Generalizability vs.
     - Specialization

*** Results
   - DaL outperforms or matches state-of-the-art in 33 out of 40 cases.
   - Achieves up to 1.94× improvement.
   - Needs fewer training samples for same accuracy.
   - Especially beneficial in complex systems or with more training data.
* 2.3
** Single Environment Learning: Encoding
  Source: Gong & Chen, MSR 2022

  A study conducted by the lab investigates how different encoding schemes impact the software performance learning pipeline.

*** Three Common Encoding Schemes
   - Label encoding
   - Scaled label encoding (e.g., max-min normalization)
   - One-hot encoding

** Encoding Schemes Explained

*** Label Encoding
   - Converts configuration options into numeric values.
   - Example:
     - Configuration: (cache_size, interval, ssl, data_strategy)
     - Values: cache_size = (1, 10, 10000), interval = (1–4), ssl = (0, 1), data_strategy = (strategy_1, strategy_2, strategy_3)
     - Encoded: (10000, 2, 1, 1) → (2, 1, 1, 1) → data_strategy: (0, 1, 2)

*** Scaled Label Encoding
   - Similar to label encoding but normalizes all values to the range [0, 1].
   - Example (10000, 2, 1, 1) becomes (1, 1/3, 1, 0.5)

*** One-Hot Encoding
   - Transforms each categorical value into a binary vector.
   - Example: (10000, 2, 1, 1) becomes (0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0)

** Community Debate and Justifications

   - Categorical features (e.g., cache_mode = memory, disk, mixed):
     - Label encoding implies false ordering (1, 2, 3)
     - One-hot encoding avoids this but may introduce multicollinearity.

   - Numeric options (e.g., cache_size = 1, 10, 10000):
     - Label encoding maintains order but struggles with large scale differences.
     - Scaled label encoding improves numeric stability but weakens interaction with binary features.

** Study Protocol
   - Evaluated using 7 learning algorithms across 5 software systems.

     
