Multidimensional risk-based testing - Richard Seidl

Risk-based testing plays a crucial role in software projects. It is important to not only analyze risks one-dimensionally, but to look at them from different angles. A multidimensional model offers decisive advantages here. By using data from real projects, more precise risk assessments can be achieved. This data-based approach leads to a deeper insight into complex risk scenarios. The approach promotes an optimized test strategy that is not only theoretical, but above all practical.

Podcast Episode: Multidimensional risk-based testing

In this episode, I talk to Richard Hönig about risk-based testing. We look at different perspectives to analyze risks in testing more effectively and optimize our processes. Richard explains his model, which looks at multiple dimensions rather than just one-dimensional risk analysis. An exciting topic that has not only theoretical but also practical relevance. We discuss how to use data from projects to achieve more accurate risk assessments. Richard's approach provides a deeper, data-based view of risk and offers valuable insights into how to really use testing to minimize risk.

"I'm now only saying that the test cases that have a certain risk value should also be included in the test run. We don't care about anything else for now." - Richard Hönig

Richard Hönig studied biochemistry in Leipzig, worked as a scientist and entered the IT industry as a career changer. He has now been an enthusiastic quality engineer for several years. His wealth of experience ranges from manual test methodology to test data generation and test management. The further development of risk-based testing for complex enterprise applications is a project close to Richard's heart and keeps him busy even in the shower.

Highlights der Episode

• Risk-based testing is crucial for effective quality control.
• Multi-dimensional risk analysis provides deeper insights than one-dimensional approaches.
• Project data significantly improves the precision of risk assessments.
• Data-driven methods optimize the test strategy sustainably.
• Practical application of theories is essential for successful testing.

Multidimensional risk-based testing

Introduction

Multidimensional risk-based testing is an important approach in the world of software testing. In this article, we will look at the different aspects of this innovative approach:

1. The design
2. The implementation
3. The future prospects of this test technique

By incorporating different perspectives on risk in testing, a new dimension is added that enables processes and test cases to be optimized. Let's dive into the world of Multidimensional Risk-based Testing and discover how this approach is transforming the way we test software.

Concepts and techniques of multidimensional risk-based testing

Multidimensional risk-based testing goes beyond the traditional, one-dimensional view of risk. It is based on the idea of analyzing risks from different perspectives and using these insights for more precise test case optimization.

Multidimensional risk analysis

The central innovation is to classify risks not only as high, medium or low, but to evaluate them in a differentiated way on several levels. The risk analysis takes into account:

• Technical logic: How complex is a test case? How many steps and dependencies are involved? Complexity increases the probability of an error.
• Test history: How often have errors occurred in this test case in the past? What priority did these errors have? Previous test results provide important information for risk assessment.
• Release relevance: Which requirements and code changes are relevant for the current release? Tests that cover critical changes receive a higher risk assessment.
• Tester assignment: Who is performing the test? The experience and workload of the testers is included in the assessment, as this can have an influence on the risk.
• Code changes: Which parts of the code are affected by a test case? Changes to these areas increase the risk of undetected defects.

Risk Score - Quantitative evaluation

Instead of a rough division into categories, a numerical system based on the Fibonacci sequence is used (numbers such as 1, 2, 3, 5, 8, etc.). This system offers several advantages:

• Allows for a finer nuance of risk.
• High risk scores grow exponentially and influence the overall risk score more than low risk scores.
• Aggregates scores from different dimensions into a meaningful overall value per test case.

Data-driven approach to optimization

The model uses existing data sources in the project environment:

• Linking requirements with test cases
• Historical test results including defect rates
• Error tickets with priorities
• Information about code changes and refactoring

This data is evaluated automatically. Manual assessments remain possible if, for example, experienced project participants want to have different assessments of the risk. The system therefore offers flexibility and avoids additional documentation work.

Algorithms for risk assessment

Various algorithms are used to capture the multidimensional perspective, which take into account the following factors, among others:

• Complexity analyses of individual test cases
• Keyword-based indicators for potential risks
• Distribution of test cases in the project context
• Assignment of tests to specific requirements or releases

This combination of quantitative analysis and empirical values makes the process reliable and practical.

The focus on different dimensions of risk assessment enables you to manage your test cases according to priority and relevance. The database ensures transparency and traceability of your decisions in the testing process. This finally makes risk-based testing measurable and controllable - moving away from gut feeling towards a data-driven approach.

Implementation and use of multidimensional risk-based testing in practice

The implementation and use of multidimensional risk-based testing in practice involves various important aspects, which are explained in more detail below:

1. Using Fibonacci numbers to assess risks

• The use of Fibonacci numbers to assess risks is an innovative method to enable a more differentiated assessment.
• Fibonacci numbers provide a scalable and nuanced assessment of risk that goes beyond traditional categories such as low, medium and high.
• This numerical scale allows risk values to be aggregated and analyzed more precisely.

2. Aggregation of risk values

• A central step in the application of multidimensional risk-based testing is the aggregation of risk scores.
• By looking at the individual risk values at different levels and combining them, an overall picture of the risk for each test case is created.
• Aggregation enables project managers and testers to obtain a clear overview of the distribution of risks in the project.

3. Evaluation of each test case

• Each test case is evaluated using the multidimensional risk analysis and is given a specific risk value.
• This risk score is based on various factors such as technical logic, test history, scope of the release, tester assignment and code changes.
• The assessment of each test case enables the teams to plan and execute their test runs in a targeted manner by focusing on the test cases with the highest risks.

Implementing multidimensional risk-based testing requires careful integration of these concepts into existing test processes. By using Fibonacci numbers for precise assessment, aggregating risk scores at different levels and assessing each test case individually, teams can more effectively identify and minimize risks.

The practical approach of this methodology enables companies to make their testing more efficient and make informed decisions based on detailed risk scoring. With a clear structure and assessment method, projects can be successfully optimized to identify and eliminate potential sources of error at an early stage.

Future of multidimensional risk-based testing

The future of multidimensional risk-based testing offers a lot of development potential in risk-based testing. The Multidimensional Risk-Based Testing approach opens up new possibilities for assessing and optimizing risks in test cases. The further development of this concept could include the following aspects:

• Flexibility in weighting: A customization option for weighting different risk perspectives depending on project requirements and phases.
• Expansion of analysis levels: Integration of further analysis levels such as security, seasonality or individual project requirements for even more accurate risk assessment.
• Improved user-friendliness: Creating more options for users and test managers to define individual risk levels and determine the overall risk more precisely.

Multidimensional risk-based testing has the potential to continuously evolve and provide users with even more effective tools to improve their risk management.