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BigQuery ML for Technical Dept predictions

Estimated time to read: 4 minutes

While I can't provide you with specific models, as they would depend on your data and context, I can outline the general steps to create regression or classification models to predict the cost of technical debt. These models can help you estimate the financial impact of technical debt based on various factors like code quality, project size, and team experience.

Preparation

Most of us we do not have all the data required to estimate our technical dept. However, let's assume we have all we need from our various systems that consistently collect the required information.

Data Collection

Gather historical data related to your projects, such as:

  • Code quality metrics (e.g., cyclomatic complexity, code coverage, code churn)
  • Project size (e.g., lines of code, number of modules)
  • Team size and experience
  • Development and maintenance hours
  • Number of defects or system failures
  • Customer satisfaction, churn rate, or lost revenue

Data Preprocessing

Clean and preprocess your data, including handling missing values, outliers, and converting categorical variables to numerical representations.

Feature Engineering:

Create new features or transform existing ones that may have a relationship with the cost of technical debt. For example, you could create a feature representing the ratio of defects per lines of code or average cyclomatic complexity per module.

Model Selection:

Choose the appropriate regression or classification algorithm based on your problem and data. Common choices for regression include linear regression, ridge regression, or LASSO. For classification, you might consider logistic regression, decision trees, or random forests.

Model Training and Validation:

Split your dataset into training and validation sets. Train your model using the training set and validate its performance using the validation set. Evaluate your model's performance using metrics like R-squared, mean squared error (MSE), or accuracy and F1-score for classification models.

Model Tuning:

Fine-tune your model's hyperparameters to optimise its performance. You can use techniques like grid search or random search for this purpose.

Model Deployment and Monitoring:

Deploy your trained model to predict the cost of technical debt for new projects. Continuously monitor the model's performance and update it with new data as necessary.

Keep in mind that the success of these models depends on the quality and representativeness of your data. Make sure to validate your models' performance and assumptions before using them to make decisions about the cost of technical debt. As your projects evolve and you gather more data, you may need to refine and update your models to ensure their accuracy and relevance.

Coding!

BigQuery ML allows you to create and execute machine learning models using SQL-like queries directly within BigQuery. Here's an example of how to create a linear regression model using BigQuery ML to predict the cost of technical debt based on code quality metrics:

  1. Create a dataset in BigQuery (if you don't already have one):
SQL
CREATE SCHEMA your_dataset;

Note: Replace your_dataset with the name of your dataset in BigQuery and adjust the column names and data types as needed to match your data.

  1. Create a table to store your historical data:
SQL
CREATE TABLE your_dataset.project_data (
  project_id INT64,
  cyclomatic_complexity FLOAT64,
  code_coverage FLOAT64,
  code_churn FLOAT64,
  team_size INT64,
  development_hours FLOAT64,
  maintenance_hours FLOAT64,
  defects INT64,
  system_failures INT64,
  lost_revenue FLOAT64,
  technical_debt_cost FLOAT64
);

  1. Load your historical data into the table. You can do this using the BigQuery Console, API, or CLI. Ensure that your data is properly preprocessed and cleaned.

  2. Create a linear regression model to predict the cost of technical debt:

SQL
CREATE OR REPLACE MODEL your_dataset.technical_debt_cost_model
OPTIONS(model_type='linear_reg', input_label_cols=['technical_debt_cost']) AS
SELECT
  cyclomatic_complexity,
  code_coverage,
  code_churn,
  team_size,
  development_hours,
  maintenance_hours,
  defects,
  system_failures,
  lost_revenue,
  technical_debt_cost
FROM
  your_dataset.project_data;
  1. Evaluate the model's performance using metrics like Mean Absolute Error (MAE) and R-squared:
SQL
SELECT
  *
FROM
  ML.EVALUATE(MODEL your_dataset.technical_debt_cost_model);
  1. Use the trained model to predict the cost of technical debt for new projects:
SQL
SELECT
  project_id,
  predicted_technical_debt_cost
FROM
  ML.PREDICT(MODEL your_dataset.technical_debt_cost_model, (
SELECT
  project_id,
  cyclomatic_complexity,
  code_coverage,
  code_churn,
  team_size,
  development_hours,
  maintenance_hours,
  defects,
  system_failures,
  lost_revenue
FROM
  your_dataset.new_project_data));

This is a simple example to illustrate the process. In practice, you might need to preprocess your data, handle missing values, perform feature engineering, and experiment with different models and hyperparameters to achieve better performance. Additionally, consider validating your model using techniques like cross-validation or splitting your data into training, validation, and testing sets to prevent overfitting and ensure generalizability.