Question

Exercises 21-60 are programs or shortanswer questions. Distinguish between test plans based on code coverage and data coverage.

Short answer

Code coverage targets executing all code paths; data coverage tests diverse input scenarios.

Step-by-step solution

Understanding Code Coverage

Code coverage refers to how much of the code is executed when a particular test suite runs. The primary goal is to ensure that every part of the code, such as functions, branches, or loops, is covered by the test cases. This helps identify untested parts of a codebase.

Understanding Data Coverage

Data coverage, on the other hand, focuses on testing different input data scenarios to ensure the application's response is correct across varied inputs. The aim is to validate that the program handles all possible data values and permutations in the input space.

Differences Between Code and Data Coverage

The main distinction between code and data coverage lies in their focus areas. Code coverage is concerned with executing all the code pathways, while data coverage is focused on validating the software’s behavior with diverse input datasets. Each serves a different purpose but is crucial for comprehensive testing.

Application of Test Plans

Developing test plans based on code coverage would prioritize achieving maximum execution of code sections, targeting lines, branches, and paths. Conversely, test plans focusing on data coverage would prioritize various data inputs to comprehensively check the effects and behavioral aspects of the software across input variations.

Key concepts

Code Coverage

Code coverage is a vital aspect of software testing that checks how much of your source code is tested. Imagine code coverage as a map highlighting all the parts of a program that are exercised during a test. The goal is to make sure no code goes untested. This helps in identifying segments, like functions, branches, or loops, that might be skipped. Using code coverage:

• Ensures maximum utilization and validation of the written code.
• Aims at improving the reliability and maintainability of a software product.

Moreover, there are several types of code coverage techniques available: *Line coverage* ensures every line of code runs at least once, while *branch coverage* ensures every possible path or decision is executed. Testing will often include various combinations of these techniques for robustness. An effective testing suite should strive for high code coverage, though 100% coverage is rarely practical. The higher the code coverage, the greater the confidence in the behavior of the program during real-world usage. Code coverage can be quantified into a percentage showing how many lines or branches are tested from the total code. Yet, it is important to remember: high code coverage doesn't guarantee the absence of bugs, it simply shows that more code has been tested.

Data Coverage

Data coverage is equally important and focuses on testing the software with a variety of data inputs. While code coverage tests the structure, data coverage ensures the software behaves correctly under different scenarios. Think of data coverage as a nutritional regime for your code, where diverse data inputs fulfill every condition or requirement. This approach catches issues that only appear with specific data sets. Advantages of data coverage include:

• Helping to confirm that your program can handle expected and unexpected input gracefully.
• Reducing risks related to edge cases or unforeseen input data scenarios.

Data coverage assesses how well your test inputs represent all possible inputs. Factors such as data type, value ranges, format variations, and physiological constraints are considered. Complex systems, with intricate input and output mappings, would benefit from rigorous data coverage tests. This ensures that, regardless of what the user inputs, the software will respond in a predicted and correct manner.

Test Plans

Creating robust test plans is at the heart of effective software testing. Test plans provide a structured approach for verifying that a software product meets its requirements. They detail the scope, approach, resources, and schedule of intended test activities and serve as a blueprint throughout the testing process. When composed of code coverage, test plans prioritize maximizing code execution. Key components of test plans might include:

• Test objectives and responsibilities.
• Features to be tested and not tested.
• Resource requirements.
• Scheduling and deliverables.

By understanding both code and data coverage, test planners determine the right balance to ensure that the tests not only run the code but also exercise it with various data scenarios. This balance is crucial for deriving meaningful insights and confidence in test results.

Input Data Scenarios

Designing input data scenarios is crucial for validating how the software deals with different user inputs. This means testing the software not only with expected data but with unpredictable and extreme data examples as well. This technique belies both code and data coverage concerns, which ensure that the software efficiently handles every conceivable situation. Essential aspects of creating effective input data scenarios:

• Identifying and categorizing possible inputs.
• Creating exhaustive cases for edge scenarios.
• Analyzing problem areas that could arise from input and defining limit conditions.

Thus, crafting well-thought-out input data scenarios ensures broader coverage and the discovery of hidden bugs or performance issues. It means users, once they use the actual software, will find a stable and efficient product that deals with their input in a predictable manner, enhancing the overall user experience.