How does regression testing ensure the consistent functionality of software after changes have been made?
- By retesting all test cases
- By testing only the modified code
- By retesting the entire system
- By comparing the new version with the previous one
Regression testing ensures the consistent functionality of software after changes by retesting the entire system. This practice helps identify any unintended side effects of code changes and ensures that the software continues to work correctly across all its features.
Why is regression testing crucial when implementing a new feature within an existing system?
- It speeds up the development process
- It helps reduce the cost of development
- It ensures that new features do not break existing functionality
- It is a formality without much impact
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 often involves analyzing ______ to understand how a program works.
- Source code
- User interface
- Hardware components
- User documentation
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.
What is the primary aim of software verification in the software development life cycle?
- Identifying and fixing defects in the software
- Ensuring that the software meets the customer's requirements
- Testing the software in a real-world environment
- Validating user interface design
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.
During white box testing, the tester must have knowledge of the ______ to construct test cases and identify issues.
- System's users
- External interfaces
- Internal code structure
- Project timeline
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 _______ model is an approach to software evolution that focuses on incremental development and operational prototyping.
- Waterfall
- Spiral
- Evolutionary
- Agile
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.
For machine learning model deployment in a production environment, which tool or language is often integrated due to its performance and scalability?
- Python
- R
- Java
- Kubernetes
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 primarily designed to handle data that exceeds the processing capability of _______ systems.
- Mainframe
- Personal computer
- Supercomputer
- Mobile device
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 organizational properties, but not as strict as tables in relational databases, is termed as _______ data.
- Unstructured Data
- Semi-Structured Data
- Raw Data
- Big Data
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.
While preparing data for a machine learning model, you realize that the 'Height' column has some missing values. Upon closer inspection, you find that these missing values often correspond to records where the 'Age' column has values less than 1 year. What might be a reasonable way to handle these missing values?
- Impute missing values with the mean height
- Impute missing values with 0
- Leave missing values as they are
- Impute missing values based on 'Age'
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.