iSQI CTFL_Syll2018 - ISTQB Certified Tester Foundation Level (Syllabus 2018)

A decision table is a specification-based test technique that involves creating a table that shows the combinations of inputs and outputs for a system under test based on certain conditions and rules. A decision table can be used to design test cases that cover all possible scenarios and outcomes for a system under test. Some statements about decision tables are:

I. Generally, decision tables are generated for low risk test items: This statement is false because decision tables can be generated for any level of risk test items depending on the complexity and variability of the system under test. Decision tables are especially useful for testing systems that have many inputs, outputs, conditions, and rules that affect their behavior or performance.

II. Test cases derived from decision tables can be used for component tests: This statement is true because decision tables can be used to design test cases for any level or type of testing, including component tests. Component tests are tests that verify individual components or units of software in isolation from other components or systems.

III. Several test cases can be selected for each column of the decision table: This statement is true because each column of the decision table represents a unique combination of inputs and outputs for the system under test based on certain conditions and rules. Therefore, each column can be used as a basis for selecting one or more test cases that cover that combination.

IV. The conditions in the decision table represent negative tests generally: This statement is false because the conditions in the decision table can represent both positive and negative tests depending on the requirements and expectations of the system under test. Positive tests are tests that verify that the system under test behaves as expected under valid or normal inputs or conditions. Negative tests are tests that verify that the system under test handles errors or exceptions gracefully under invalid or abnormal inputs or conditions.

You can find more information about decision tables in [A Study Guide to the ISTQB® Foundation Level 2018 Syllabus], Chapter 4, Section 4.2.

Multiple condition coverage is a structure-based test technique that aims to achieve a high level of coverage by testing all possible combinations of conditions and outcomes in each decision point of the code or design of the system under test. Multiple condition coverage verifies that each condition in a decision point has an effect on the outcome and that there are no hidden defects or logical errors in the code or design. Multiple condition coverage requires some knowledge of the internal structure or implementation of the system under test; it focuses on how the system does what it does rather than what it does. Multiple condition coverage is one of the most rigorous and complex structure-based test techniques because it can generate a large number of test cases for each decision point, especially if there are many conditions involved. You can find more information about multiple condition coverage in [A Study Guide to the ISTQB® Foundation Level 2018 Syllabus], Chapter 4, Section 4.3.

The statement that does not describe how testing contributes to higher quality is “The testing of software demonstrates the absence of defects”. This statement is false because testing cannot prove that a software system is defect-free or has a certain level of quality because there might be some untested inputs or outputs that could reveal defects or failures. Testing can only provide information about the quality and risk level of a software system based on the observed behavior and results under certain conditions and assumptions. The other statements describe how testing contributes to higher quality by:

Performing a review of the requirement specifications before implementing the system can enhance quality: This statement is true because reviewing the requirement specifications can help identify and prevent defects, ambiguities, inconsistencies, or incompleteness in the requirements before they are implemented in the system, which can save time and effort and improve customer satisfaction.

Properly designed tests that pass reduce the level of risk in a system: This statement is true because passing tests can increase confidence that the system meets its requirements and expectations and provides value to the users and customers. Passing tests can also reduce the probability or impact of failures or defects in the system that could cause harm or loss.

Software testing identifies defects, which can be used to improve development activities: This statement is true because identifying defects can help analyze and remove their causes and prevent them from recurring in future development activities. Identifying defects can also help improve development processes, methods, standards, tools, techniques, etc., by providing feedback and lessons learned.

You can find more information about how testing contributes to higher quality in [A Study Guide to the ISTQB® Foundation Level 2018 Syllabus], Chapter 1.

The V-model is a development life cycle model that shows the relationship between each phase of development and its corresponding phase of testing. In this model, each level of testing (unit testing, integration testing, system testing, acceptance testing) has a corresponding level of development (component design, component integration, system design, requirements analysis). The model also shows that testing activities should start as early as possible in the development process and that each level of testing should be planned and designed in parallel with its corresponding level of development. Therefore, one of the roles of a tester in a software company that adopts the V-model is to coordinate the test strategy with the project managers who are responsible for planning and managing each phase of development. This role involves defining the scope, objectives, approach, resources, schedule, risks, and deliverables of each level of testing in alignment with the development plan and the project requirements. You can find more information about the V-model and test planning in Software Testing Foundations: A Study Guide for the Certified Tester Exam, Chapter 22.

Test metrics are quantitative measures that are used to monitor, control, and improve the test process and its outcomes. Test metrics can be collected at different levels of testing (test case, test suite, test project, etc.) and can be used for different purposes (planning, estimation, execution, evaluation, etc.). Some examples of common test metrics are:

Percentage of work done in test environment creation: This metric indicates how much effort has been spent on setting up and maintaining the test environment, which includes hardware, software, network, data, tools, etc., that are required for conducting the test activities.

Number of test cases run: This metric indicates how many test cases have been executed during a given period or phase of testing.

Deviation from test milestone dates: This metric indicates how much delay or ahead of schedule the test activities are compared to the planned dates.

Defect density: This metric indicates how many defects have been found per unit of size or functionality of the system under test.

Average number of expected defects per requirement is not a common test metric because it is not easy to estimate or measure how many defects are likely to be found for each requirement. Moreover, this metric does not provide useful information for improving the test process or evaluating the test results. You can find more information about test metrics in A Study Guide to the ISTQB® Foundation Level 2018 Syllabus, Chapter 5, Section 5.41.

The term ‘Pesticide paradox’ refers to the phenomenon where the effectiveness of test cases that are repeated and focused on the same scenarios decreases over time because they tend to find the same defects or no defects at all. This is because the system under test becomes more resistant or immune to the existing test cases, just like pests become more resistant or immune to pesticides over time. To overcome the pesticide paradox, test cases should be regularly reviewed and updated to cover new or changed requirements, scenarios, risks, or defects. Test cases should also be designed to cover different aspects and perspectives of the system under test, such as functionality, usability, performance, security, etc. You can find more information about the pesticide paradox in A Study Guide to the ISTQB® Foundation Level 2018 Syllabus, Chapter 4, Section 4.11.

Functional testing is an example of black-box dynamic testing. Functional testing is the process of testing the functionality or behavior of a software system based on its requirements or specifications. Functional testing does not require any knowledge of the internal structure or implementation of the system under test; it only focuses on what the system does rather than how it does it. Functional testing is also an example of dynamic testing because it involves executing the software system with selected inputs and observing its outputs and effects on the environment. Dynamic testing is different from static testing, which involves examining the software system without executing it, such as code inspection, static analysis, etc. You can find more information about functional testing and dynamic testing in A Study Guide to the ISTQB® Foundation Level 2018 Syllabus, Chapter 21.

Pair programming is a software development technique where two programmers work together on the same code at the same workstation. One programmer, called the driver, writes the code while the other programmer, called the navigator, reviews the code and provides feedback and suggestions. Pair programming is not only a coding technique but also an informal review method because it involves constant communication and collaboration between the programmers who check each other’s work and share their knowledge and skills. Pair programming can improve the quality and productivity of software development by reducing defects, increasing code readability, enhancing learning opportunities, and fostering creativity and innovation. Pair programming is usually used in agile methods such as Extreme Programming (XP), Scrum, or Kanban. It is not an alternative term for code inspection, which is a formal review method where a group of reviewers examine a piece of code in a structured and systematic way following predefined rules and roles. It is also not done with developer and tester pairing together because pair programming requires both programmers to have similar levels of expertise and familiarity with the code they are working on. You can find more information about pair programming in A Study Guide to the ISTQB® Foundation Level 2018 Syllabus, Chapter 31.

A requirements management tool may be considered as a test support tool because it can help support or enhance some aspects of testing activities or processes. A requirements management tool is a tool that allows users to manage and trace the requirements of a software system. A requirements management tool can help support or enhance testing activities or processes by:

Providing a clear and consistent definition of what needs to be tested based on user needs and expectations

Enabling traceability between requirements and test cases to ensure adequate and complete test coverage

Supporting change management and impact analysis by identifying and tracking changes in requirements and their effects on testing activities and results

Test estimation is the process of predicting the effort, time, and resources required for testing a software system. Test estimation depends on several factors that influence the scope, complexity, and quality of testing. Some of these factors are:

Characteristics of the product: This factor indicates how difficult or challenging it is to test the software system based on its size, functionality, reliability, usability, performance, security, etc.

Characteristics of the development process: This factor indicates how well or poorly the software system is developed based on the development methodology, standards, tools, techniques, etc.

Characteristics of the test process: This factor indicates how effective or efficient the testing process is based on the test strategy, plan, design, execution, evaluation, etc.

Characteristics of the test team: This factor indicates how skilled or experienced the test team is based on their knowledge, competence, motivation, communication, collaboration, etc.

The outcome of testing of previous test cycle is not a factor on which test estimation is dependent upon because it does not affect the effort, time, or resources required for testing the current test cycle. Rather, it is an outcome or consequence of test estimation that can provide feedback and lessons learned for improving future test estimations. You can find more information about test estimation in Software Testing Foundations: A Study Guide for the Certified Tester Exam, Chapter 22.