What CTFL_Syll_4.0 Questions and Answers feature?

Statement coverage, also known as line coverage, is a metric used in white-box testing to measure the percentage of executable statements in the source code that have been executed by the test suite. Achieving a certain percentage of statement coverage means that an equivalent percentage of the executable statements in the code have been executed during testing.

For example, achieving 80% statement coverage indicates that 80% of the lines of code have been run through during the testing process. This metric helps in identifying parts of the code that have not been tested and may contain undetected defects.

It's important to note that achieving high statement coverage does not necessarily guarantee that all possible paths or scenarios within the code are tested, nor does it ensure the detection of all types of defects. Other coverage metrics, such as branch coverage or condition coverage, might also be necessary to achieve a more thorough testing process.

The support offered by a performance testing tool is often leveraged by testers to run load tests, which are tests that simulate a large number of concurrent users or transactions on the system under test, in order to measure its performance, reliability, and scalability. Performance testing tools can help testers to generate realistic workloads, monitor system behavior, collect and analyze performance metrics, and identify performance bottlenecks. The other statements are false, because:

A test data preparation tool is a tool that helps testers to create, manage, and manipulate test data, which are the inputs and outputs of test cases. Test data preparation tools are not directly related to running automated regression test suites, which are test suites that verify that the system still works as expected after changes or modifications. Regression test suites are usually executed by test execution tools, which are tools that can automatically run test cases and compare actual results with expected results.

A bug prediction tool is a tool that uses machine learning or statistical techniques to predict the likelihood of defects in a software system, based on various factors such as code complexity, code churn, code coverage, code smells, etc. Bug prediction tools are not used by testers to track the bugs they found, which are the actual defects that have been detected and reported duringtesting. Bugs are usually tracked by defect management tools, which are tools that help testers to record, monitor, analyze, and resolve defects.

A continuous integration tool is a tool that enables the integration of code changes from multiple developers into a shared repository, and the execution of automated builds and tests, in order to ensure the quality and consistency of the software system. Continuous integration tools are not used by testers to automatically generate test cases from a model, which are test cases that are derived from a representation of the system under test, such as a state diagram, a decision table, a use case, etc. Test cases can be automatically generated by test design tools, which are tools that support the implementation and maintenance of test cases, based on test design specifications or test models. References: ISTQB Certified Tester Foundation Level (CTFL) v4.0 sources and documents:

ISTQB® Certified Tester Foundation Level Syllabus v4.0, Chapter 3.4.1, Types of Test Tools

ISTQB® Glossary of Testing Terms v4.0, Performance Testing Tool, Test Data Preparation Tool, Bug Prediction Tool, Continuous Integration Tool, Test Execution Tool, Defect Management Tool, Test Design Tool

The primary objectives of testing, as outlined in the ISTQB CTFL v4.0 syllabus, include verifying whether specified requirements are met (A), detecting failures and defects (B), and validating that the test object functions as expected (D). However, "finding errors" (C) is not a direct objective. Errors result from human mistakes, but testing primarily identifies defects, which are flaws in the system that cause failures. Testing aims to reveal defects rather than directly identify errors in the code.