CTAL-TTA Certified Tester Advanced Level Technical Test Analyst Questions and Answers

A Technical Test Analyst is primarily involved in the technical aspects of test preparation and execution. One of their typical activities includes the execution of test cases, particularly those that are automated, and the subsequent analysis of any test failures to identify defects and issues. This activity is more technical than defining business processes or training other analysts, and while making decisions based on a business case may be part of their role, it is not an activity directly related to test automation.

The characteristics listed in the question point towards the need to manage synchronization between processes and make efficient use of limited hardware resources, such as microcontrollers and timeslots. Additionally, the complexity and concurrency of tasks highlight the importance of the software's performance over time. Time-behaviour testing is the most appropriate non-functional test type to perform in this scenario as it focuses on evaluating the timing aspects of the system, such as response times, processing times, and throughput rates. It ensures that the system meets its time-related requirements, which is critical for systems reliant on synchronization and limited by hardware performance constraints.

The control flow graph provided illustrates the decision points for an airline's upgrade and boarding process. Decision coverage is a measure of the percentage of decision points executed during testing:

• Test 1 covers the decision points: Gold card? (Yes) and Business full? (No).
• Test 2 covers: Gold card? (No) and Economy full? (No).
• Test 3 covers the decision that leads to being bumped from the flight, which is Economy full? (Yes) and Business full? (Yes).

From the given tests, the decision points for Gold card? (No) and Business full? (No) are not tested, leaving us with 4 out of 6 decision points covered, which is approximately 67% decision coverage.

Maintainability metrics typically used with static analysis include measures that reflect the complexity and understandability of the code, such as Number of Lines of Code (LOC), Number of Function Calls, and Comment Frequency. These metrics help in assessing how easily the software can be understood, modified, and maintained. Mean Time Between Failures (MTBF), on the other hand, is a reliability metric. It measures the time elapsed between inherent failures of a system during operation. MTBF is used to predict the system's reliability and is not directly related to the maintainability of the code. Reliability metrics like MTBF would be used in the testing phase to measure the operational reliability of the system rather than during static analysis for maintainability assessment.

In the context of software testing, different safety integrity levels (SIL) require different levels of rigor in testing. According to the IEC61508 standard, for software components rated at SIL 2, achieving 100% statement coverage and 100% decision coverage is recommended. Statementcoverage ensures that every line of code is executed at least once during testing, while decision coverage ensures that every decision in the code (e.g., every branch of an IF statement) is executed on both the true and false sides. These coverage criteria ensure a thorough testing of the software components to validate that they behave correctly in all circumstances. Multiple condition coverage and MC/DC coverage (Options A and B) are more rigorous and typically required for higher SIL levels, such as SIL 4.

Failure Mode and Effect Analysis (FMEA) is a structured approach to identify and address potential failures in a system, product, process, or service. The major factors involved in calculating risk priorities in FMEA are typically the severity of the potential failure, its likelihood of occurrence, and the ability to detect it. These factors are usually combined to form a Risk Priority Number (RPN) for each potential failure mode identified. However, the specific factors mentioned in the options like functionality, reliability, usability, maintainability, efficiency, and portability are quality characteristics that could be considered in an FMEA analysis but are not directly used for calculating risk priorities. Likewise, financial damage, frequency of use, and external visibility might influence the severity or impact of a failure, but they are not standard factors in calculating risk priorities in the context of FMEA. Therefore, the most relevant factors for calculating risk priorities in an FMEA context would typically be the likelihood of the failure occurring and its potential impact, which aligns with option C: Likelihood and impact.

It's important to note that while these explanations are based on general principles and practices related to fault seeding and FMEA, the specifics might vary slightly in different contexts or with different methodologies.

100% statement coverage means that every line of code is executed at least once during testing. Based on the provided pseudo-code and the test cases given:

• Test 1 executes theMIN = Bstatement whenAandBare equal.
• Test 2 executes theMIN = Astatement and skips the innerIFsinceBis not equal to2*A.

All statements within the code have been executed by these two tests, hence no additional test cases are needed to achieve 100% statement coverage.

The pseudo-code provided for the "Win" program reads in variables A, B, C, and D. However, only variables A, B, and C are used in the conditional statements to determine if the output will be "Win" or "Loose". Variable 'D' is never used after it is read, which is a classic example of a 'defined but not used' data flow anomaly. This means that while there is an instruction to read a value into variable 'D', there is no subsequent use of this variable in the program's logic or output.

Performance efficiency testing is most commonly associated with system testing. This is the level at which the complete, integrated system is evaluated, and it is typically where performance, load, and stress testing are conducted to assess the system's behavior under various conditions and loads. Performance efficiency testing at this level helps to ensure that the system meets the necessary performance criteria as a whole.

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.