What is Risk Based Testing?

Risk-based testing is a popular approach to software testing that helps in identifying and assessing risks associated with a project or release. It allows teams to focus their efforts on the areas of the software that are most important and most likely to be affected by defects or errors. By prioritizing testing activities based on business risks, organizations can optimize their testing efforts and improve the overall quality of their software. The article will help in streamlining software testing and will act as a  comprehensive guide to risk based testing implementation in your project.

 

Risk-based testing can be applied throughout the software development lifecycle and can help organizations make better strategic decisions by improving their understanding of the potential return on investment. It also enables them to identify and mitigate risks early in the project lifecycle, reducing the overall cost of testing. The risk-based testing framework can be applied in three vital stages of testing – Test Initiation, Test Planning, and Test Execution. By using this framework, test teams can prioritize user stories based on business risks, which helps in determining the intensity of testing. Let us explore each stage in detail.

Implement risk based testing during the Test Initiation Phase:

One of the first steps in the risk based testing approach is to map the quality attributes of the application like accessibility, scalability, reliability, compliance, portability, etc to test types. This mapping enables the team to categorize the quality attributes and determine which test types are most relevant to each attribute. By doing so, the team can prioritize their testing efforts by focusing on the most critical test types first.

 

Once the quality attributes are mapped to test types, the team can rate the quality attributes using a scoring model. The scoring model enables the team to assign a numerical value to each attribute based on its relative importance to the application. This step is critical in ensuring that the team’s testing efforts align with the project’s goals and objectives.

Implement risk based testing during the Test Planning Phase:

In the Test Planning phase, the testing team needs to analyze the user stories and requirements to identify the risks associated with each of them. The risks could be based on the impact of failure and the probability of occurrence. The testing team must consider the impact of failure in terms of the impact on the business and end-users if the user story or requirement fails. Similarly, the probability of occurrence must be estimated based on various factors such as the complexity of the functionality, the stability of the environment, the experience of the development team, etc.

Once the risks associated with each user story or requirement are identified, the testing team can calculate a risk score for each of them. The risk score is typically a combination of the impact of failure and the probability of occurrence. The risk score is used to prioritize the user stories and requirements based on their importance to the business and end-users.

After prioritizing the user stories and requirements, the testing team must determine the extent of testing required for each of them. This is done by deriving a risk score for each requirement. The risk score can be used to recommend the appropriate test level for each requirement. The test level could be ‘Full’, ‘Partial’, or ‘Basic’ based on the risk score.

For example, if a user story has a high-risk score, it may require ‘Full’ testing, which includes testing all possible scenarios and configurations to ensure that the functionality works as expected. On the other hand, if a user story has a low-risk score, it may require only ‘Basic’ testing, which includes testing only the most critical scenarios and configurations.

Implement Risk Based testing during the Test Execution Phase:

During the execution phase, risk-based testing can help to estimate the number of latent defects in the product. A latent defect is a type of software defect that is not immediately detected or observable but can cause the system to fail or malfunction at a later time. Latent defects can be difficult to identify and correct, as they may not manifest themselves until certain conditions are met or specific actions are taken within the system.

These defects can be challenging to detect and can cause significant problems if left unaddressed. By estimating the number of latent defects, testing teams can prioritize the most critical areas of the application for testing, helping to reduce the risk of these defects causing problems in the future.

In addition to estimating the number of latent defects, risk based testing can also predict defects for untested features or test cases that have yet to be executed. This prediction is based on statistical methods and algorithms that analyze the application’s previous behavior and identify patterns and trends that could indicate potential issues. By predicting these defects, the testing team can prioritize the most critical areas for testing, reducing the risk of defects impacting the end-user experience.

 

Using statistical methods and algorithms to predict residual defects in the product can also help the team decide when to stop testing. Testing can be an expensive and time-consuming process, and testing teams need to balance the need for thorough testing with the need to deliver the product within a reasonable time frame. By using risk-based testing to predict residual defects, the team can make informed decisions about when testing is complete, helping to reduce the overall test execution effort.

 

Conclusion:

Risk-based testing is a powerful approach to software testing that can help organizations optimize their testing efforts and improve the overall quality of their software. By prioritizing testing activities based on business risks, organizations can identify and mitigate risks early in the project lifecycle, reducing the overall cost of testing. The risk-based testing framework can be applied throughout the software development lifecycle and can help organizations make better strategic decisions by improving their understanding of the potential return on investment