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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: General Knowledge | 10% | - Standards, models, and frameworks - Basic statistics and measurement theory - Quality principles and concepts - Ethical, legal, and professional considerations |
| Topic 2: Software Quality Management | 15% | - Audits and assessments - Quality control and improvement - Quality planning and assurance - Quality management systems |
| Topic 3: Software Configuration Management | 15% | - Change control and traceability - Release and deployment management - Configuration audits and status accounting - Configuration planning and identification |
| Topic 4: Software Verification and Validation | 20% | - Test planning, design, and execution - Verification principles and methods - Reviews, inspections, and defect management - Validation and testing concepts |
| Topic 5: Software Metrics and Analysis | 12% | - Process and product metrics - Statistical analysis and interpretation - Measurement and data collection - Reporting and decision support |
| Topic 6: System and Software Engineering Processes | 18% | - Software design, construction, and maintenance - System integration and deployment - Requirements engineering and management - Software development lifecycles and models |
| Topic 7: Project Management | 10% | - Risk management - Supplier and vendor management - Project planning and estimation - Tracking, control, and communication |
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NEW QUESTION # 261
One reason for seeding errors into a program before testing begins is to
Answer: C
Explanation:
Error seeding is a technique used to estimate test effectiveness and the number of remaining defects. Known errors are intentionally inserted into the software before testing. After testing, the percentage of seeded errors found is compared with the number of real errors found. If testers find a high percentage of seeded errors, there is more confidence that the test process is effective. If many seeded errors are missed, the testing approach may be weak. The goal is not to determine whether a specific error is in the program or test case, nor to overload the program with many errors. It also does not directly identify specification gaps. The main purpose is estimating the percentage of total errors testing can detect.
NEW QUESTION # 262
Which of the following measures would a project manager use to assess the team's progress against the project schedule?
Answer: A
Explanation:
Earned Value Analysis (EVA) is a project management technique used to measure project performance and progress. It integrates project scope, time, and cost data to provide accurate forecasts of project performance.
* Planned Value (PV) : The estimated value of work planned to be done.
* Earned Value (EV) : The value of work actually completed.
* Actual Cost (AC) : The actual cost incurred for the completed work.
Using these metrics, EVA can help project managers assess whether they are ahead or behind schedule and within or over budget.
References:
" Project Management: A Systems Approach to Planning, Scheduling, and Controlling " by Harold Kerzner PMBOK Guide by Project Management Institute (PMI)
NEW QUESTION # 263
In specifying a software requirement, it is important to consider how the requirement might be
Answer: B
Explanation:
When specifying a software requirement, it is essential to consider how the requirement might be validated.
Validation ensures that the software meets the needs and requirements of the stakeholders and functions as intended in the real-world environment. Without proper validation criteria, it would be challenging to confirm whether the software fulfills its intended purpose.
References:
* "Software Requirements" by Karl E. Wiegers and Joy Beatty
* IEEE Standard 830-1998 for Software Requirements Specifications
NEW QUESTION # 264
Company Q has contracted with a vendor to convert source data from its customer's proprietary coding structure to XML. Although the XML data parses cleanly at the vendor's site, it generates parsing errors when run with Company Q's parser. The vendor contends that the product meets specification because the contract did not require the use of a specific parser. The best way for Company Q to eliminate this risk in the future is to require in the contract that
Answer: A
Explanation:
The problem occurred because the vendor validated the XML output only in its own environment, not on Company Q's actual parser and platform. In supplier quality management, acceptance criteria should define the required operating environment, validation method, interfaces, data formats, tools, and compatibility expectations. Requiring vendors to validate their products using Company Q's platform directly addresses the failure mode by ensuring the delivered XML is tested under the same conditions in which it will be used. Pre- certification may assess general vendor capability, but it does not guarantee compatibility. Design review involvement may help, but it does not replace validation. Prototype approval may reduce risk, but the strongest contractual requirement is platform-specific validation.
NEW QUESTION # 265
A sprint started with 354 requirements. As the sprint progressed, 14 requirements were introduced, 6 requirements were removed, and 28 requirements were changed. What was the requirements volatility for this sprint?
Answer: C
Explanation:
Requirements volatility is calculated as the percentage of change in the total number of requirements during a sprint. The formula to calculate volatility is:
Volatility=(Introduced+Removed+Changed)Initial×100\text{Volatility} = \frac{(\text{Introduced} + \text
{Removed} + \text{Changed})}{\text{Initial}} \times 100Volatility=Initial(Introduced+Removed+Changed)
×100
Using the given data:
* Initial requirements = 354
* Introduced = 14
* Removed = 6
* Changed = 28
Volatility=(14+6+28)354×100#13.5%\text{Volatility} = \frac{(14 + 6 + 28)}{354} \times 100 \approx 13.5\% Volatility=354(14+6+28)×100#13.5% Reference:
" Agile Estimating and Planning " by Mike Cohn, which explains the concept of requirements volatility in agile projects.
NEW QUESTION # 266
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