Internship - Defect Prediction Using Machine Learning for Software Quality Assurance

Key Responsibilities

• Data Collection & Preprocessing: Collect historical defect data from the testing phase, including application metrics such as code complexity, test coverage, and module dependencies.
• Feature Engineering: Develop features from the collected data (code commits, test results, bug reports) that can be used for predictive modeling.
• Machine Learning Model Development: Research, design, and implement machine learning models (e.g., regression, classification, ensemble models) that predict defects or failures in specific modules or features of the application.
• Model Evaluation & Tuning: Continuously evaluate and tune the model's performance using various metrics.
• Integration with CI/CD Pipeline

Candidate Profile Requirements

• Technical Skills: Experience with machine learning algorithms, particularly for classification and regression tasks. Proficiency in Python.
• Data Science Skills: Strong understanding of data preprocessing, feature engineering, and model evaluation techniques.
• Understanding of QA Processes: Familiarity with software testing methodologies, defect lifecycle, and bug tracking tools.
• Knowledge of CI/CD: Experience with Continuous Integration/Continuous Deployment processes.

What We Offer

• Fast-learning environment with opportunities for growth and evolution
• Clear ownership from day one with space to test, fail, and improve fast
• A culture that values sharp thinking, innovation, and autonomy
• Regular collaborations, tie-ups, and activities that expand perspective and skills
• Flexible work setup focused on results