Dynamic analysis involves evaluating a system or component based on its behavior during execution. This type of analysis is particularly useful for identifying issues related to memory usage and pointer management. The focus is on runtime behaviors that can't be detected through static analysis alone.

Identifiable Issues via Dynamic Analysis:

System Memory Not Released (Memory Leaks):

System memory not released when it is no longer needed (Option C) can be identified through dynamic analysis. Tools and techniques used in dynamic analysis can track memory allocation and deallocation during program execution, revealing instances where memory is allocated but not freed properly.

Pointers in a Program (Dangling Pointers):

Pointers in a program that are no longer accurate and must not be used (Option D) can also be identified through dynamic analysis. This includes issues like dangling pointers (pointers that refer to memory that has been freed) or wild pointers (uninitialized pointers). Dynamic analysis tools can detect when pointers are used incorrectly or point to invalid memory locations during runtime.

Explanation of Incorrect Options:

A. Objects having too much or too little visibility into parent classes: This is more related to design and structure, typically identified via static analysis or code reviews rather than dynamic analysis.

B. Repetition of code within a single component or module: This is a code quality issue often detected through static code analysis or manual code review.

E. Code that cannot be reached and so will not be executed: This is typically identified through static analysis tools that perform code coverage analysis or by manual code inspection.

References:

Although specific references from the provided documents were not found directly related to dynamic analysis, the general understanding and standard practices of software testing support the answer given. For example, the ISTQB syllabus and various testing literature highlight memory management and pointer issues as typical concerns addressed through dynamic analysis.

Sources:

General knowledge on dynamic analysis in software testing.

ISTQB-CTAL-TTA Syllabus and standard practices in software testing.

The pseudo code provided for the Price program shows a potential for an infinite loop due to the way the 'Del_Charge' variable is being manipulated. The loop is set to continue 'WHILE Del_Charge > 0', and within the loop, 'Del_Charge' is initially set to 5 and then potentially decreased by 2 if 'Sale_Value > 60000'. However, at the end of each loop iteration, 'Del_Charge' is increased by 1. This means that if 'Sale_Value' is not greater than 60000, 'Del_Charge' will not decrease and will instead increment indefinitely, causing an infinite loop. Even if 'Sale_Value' is greater than 60000, the decrement by 2 could be negated by the subsequent increments if the loop runs enough times, potentially leading to an infinite loop situation. There is no guaranteed exit condition once the loop is entered, which is a control flow anomaly.

Tools that support component testing and the build process are designed to provide a controlled environment where individual units or components of the software can be tested in isolation. This is typically done using stubs, which simulate the behavior of missing components, and drivers, which simulate the behavior of a user or calling program. This isolated environment is essential for unit testing because it allows testers to find defects within the boundaries of a single component before integrating it into the larger system.

Analysis:

Given the problems encountered with the first two websites, the types of testing needed for the BestForFood website should address similar issues related to performance under peak load and data volume growth.

Load Testing:

Load testing helps identify the behavior of the website under expected user load conditions. It simulates multiple users accessing the website simultaneously to ensure it can handle peak traffic without performance degradation. This is essential given the underestimation of concurrent user access for the first two websites.

Scalability Testing:

Scalability testing evaluates how well the system can handle increased loads by scaling up its resources, such as adding more servers or increasing database capacity. This is crucial for ensuring that the website can manage rapid data volume growth and increased user traffic over time.

Explanation of Incorrect Options:

B. Stress Testing:

Stress testing pushes the system beyond its normal operational capacity to identify breaking points. While important, it is more extreme than necessary for addressing the issues of underestimated user access and data volume growth.

D. Portability Testing:

Portability testing assesses how well the software can be moved from one environment to another, which is not directly related to the performance issues described.

E. Recoverability Testing:

Recoverability testing ensures the system can recover from failures, which is not the primary concern here.

References:

The ISTQB CTAL-TTA syllabus covers different types of non-functional testing and their relevance. Load and scalability testing are emphasized for addressing performance and capacity-related risks.

Sources:

ISTQB-CTAL-TTA Syllabus

General knowledge on non-functional testing practices.

For Modified Condition/Decision Coverage (MC/DC), each condition within a decision must independently affect the decision's outcome. The provided sets of values need to be analyzed to ensure that each component of the decision ((A > B) or (C > D) and (E = F)) can independently influence the result of the combined expression. Set 3 includes variations for all variables that allow each condition to be tested both true and false independently while keeping the overall expression's outcome affected. This choice provides the most efficient path to achieving MC/DC with minimal tests.

Performance testing is a key part of ensuring that a system can handle the expected load and perform well under high demand. This type of testing is designed to test the robustness, speed, scalability, and stability of the system under a given workload. It is not primarily concerned with security threats such as code insertion (Option A), nor with fault tolerance in terms of unexpected input values (Option B), nor with the speed of addressing user-reported defects (Option C), although these may be tangential benefits. Performance testing is focused on ensuring that the system meets performance criteria and can handle increasing loads without degradation of service, which is essential for providing a good user experience and for the system’s reliability.

Fault seeding and fault injection tools are used intentionally to introduce defects into a system or software to test the robustness of the test suite. This method helps in validating the effectiveness of the test processes and configurations by ensuring that the tests are capable of detecting and isolating inserted faults. It is a technique used to measure the quality of testing rather than debugging or performance compliance, which relates directly to the fundamental purpose of enhancing test coverage and the detection capability of the test suite .