How does software re-engineering facilitate the accommodation of new technologies and features?
- By increasing maintenance costs
- By slowing down the development process
- By providing a clean and modular architecture
- By eliminating the need for updates
Software re-engineering typically involves refactoring the existing code to create a clean and modular architecture. This makes it easier to accommodate new technologies and features, as the codebase becomes more adaptable and maintainable.
What is the relevance of creating stubs and drivers during the integration testing process?
- Stubs and drivers are not used in integration testing
- They simulate components not available for testing
- They replace the need for other testing phases
- Stubs and drivers are only used for unit testing
Stubs and drivers are essential in integration testing as they simulate components that are not yet available for testing. By creating these placeholders, developers can validate the interactions between the tested components and the missing or incomplete ones.
Fill in the blank: "_____ refers to the ability of software to be understood, learned, used, and attractive to the user when used under specified conditions."
- Scalability
- Reliability
- Portability
- Usability
Usability is all about the user experience. It refers to how easy it is for users to understand, learn, and use the software effectively under specific conditions. High usability is crucial for user satisfaction and productivity.
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.
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.
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.