Regression testing is crucial when implementing a new feature within an existing system because it helps ensure that the introduction of new features does not lead to the breaking of existing functionality. This is essential for maintaining the software's stability and quality.

Software reverse engineering typically involves analyzing the source code of a program to understand how it works. Source code contains the instructions and logic that govern the program's behavior. By examining the source code, reverse engineers can gain insights into the program's functionality.

The design principle that aims to manage dependencies between modules effectively is "Coupling." It refers to the degree of interconnection between modules in a software system. Reducing coupling helps make the system more maintainable and flexible.

The primary aim of software verification is to identify and fix defects in the software. It involves activities like code reviews and inspections to ensure the software aligns with its specifications and design.

In white box testing, the tester needs to have knowledge of the internal code structure to construct test cases and identify issues. This type of testing involves examining the internal logic, structure, and flow of the software code. Understanding the code is essential for effective white box testing.

The Spiral model is an approach to software evolution that emphasizes incremental development and operational prototyping. This model divides the project into cycles or "spirals," with each spiral incorporating user feedback and refined prototypes. This iterative approach allows for continuous improvements and risk management throughout the software development process.

Java is often integrated into production environments for machine learning model deployment due to its performance and scalability. Java is known for its speed, robustness, and suitability for large-scale applications. It is commonly used to build APIs and services for serving machine learning models in real-time production systems. Python and R are often used in model development, but Java is favored for deployment. Kubernetes is an orchestration tool.

Big Data technologies are specifically designed for data that exceeds the processing capabilities of traditional systems such as mainframes, personal computers, and mobile devices. These traditional systems are not equipped to efficiently process and analyze massive datasets, which is the focus of Big Data technologies.

Data that has some organization but doesn't adhere to a strict tabular structure is known as "Semi-Structured Data." It includes data formats like JSON, XML, and others that have a certain level of structure.

In this case, it might be reasonable to leave missing values as they are. Imputing with the mean height or 0 may introduce bias, and imputing based on 'Age' should be done carefully, as infants may have different height characteristics than adults. Depending on the context and dataset size, leaving the missing values untouched might be the best choice.