Cause Effect Graphing in Software Engineering

Cause–Effect Graphing is a black-box testing technique that identifies test cases by showing the relationship between input conditions (causes) and output results (effects). It helps ensure better test coverage and accurate validation of software requirements.

• Represents logical relationships between inputs and outputs.
• Helps generate effective and systematic test cases.
• Reduces missing and duplicate test cases.

Significance of Cause–Effect Graphing Technique

• Converts software requirements into logical relationships between inputs and outputs.
• Helps understand dependencies among multiple input conditions.
• Supports systematic and structured test case generation.
• Simplifies testing of complex decision-based systems.
• Improves the overall software testing process.

Cause–Effect Graphing Process

• Division of Specification: Divide large and complex software requirements into smaller, manageable sections for easier analysis.
• Identification of Causes and Effects: Identify all input conditions as causes and expected outputs or system actions as effects.
• Constructing the Cause–Effect Graph: Draw the graph by connecting causes and effects using logical operators such as AND, OR, and NOT, while representing their relationships clearly.
• Applying Constraints: Add constraints to eliminate invalid or impossible combinations of causes and effects.
• Conversion to Decision Table: Convert the completed cause–effect graph into a decision table, where each column represents a unique rule.
• Deriving Test Cases: Generate test cases from each rule in the decision table and verify the expected outputs for every valid input combination.

Basic Boolean Notations Used in Cause–Effect Graphs

In cause–effect graphs, c represents the cause (input condition) and e represents the effect (output or system behavior).

Identity Function: if c is 1, then e is 1. Else e is 0.

NOT Function: if c is 1, then e is 0. Else e is 1.

OR Function: if c1 or c2 or c3 is 1, then e is 1. Else e is 0.

AND Function: if both c1 and c2 and c3 is 1, then e is 1. Else e is 0.

Constraints Used in Cause–Effect Graphing

Exclusive constraint or E-constraint: This constraint exists between causes. It states that either c1 or c2 can be 1, i.e., c1 and c2 cannot be 1 simultaneously.

Inclusive constraint or I-constraint: This constraint exists between causes. It states that atleast one of c1, c2 and c3 must always be 1, i.e., c1, c2 and c3 cannot be 0 simultaneously.

One and Only One constraint or O-constraint: This constraint exists between causes. It states that one and only one of c1 and c2 must be 1.

Requires constraint or R-constraint: This constraint exists between causes. It states that for c1 to be 1, c2 must be 1. It is impossible for c1 to be 1 and c2 to be 0.

Mask constraint or M-constraint: This constraint exists between effects. It states that if effect e1 is 1, the effect e2 is forced to be 0.

Example: Online Loan Approval System

Requirement: A loan is approved only if the applicant has sufficient income, a good credit score, and all required documents are verified.

Causes (Input Conditions)

• c1: Income is sufficient
• c2: Credit score is good
• c3: Documents are verified

Effects (Output Conditions)

• e1: Loan approved
• e2: Loan rejected

Logical Relationship

• e1 = c1 AND c2 AND c3
• e2 = NOT (c1 AND c2 AND c3)

Explanation: The cause–effect graph models the relationship between input conditions and expected outputs using Boolean operators. It is then converted into a decision table to generate comprehensive test cases.

Tools Used for Cause–Effect Graphing

• Microsoft Visio: Used to create cause–effect graphs, flowcharts, and logical decision diagrams.
• Lucidchart: A cloud-based tool for collaboratively designing cause–effect graphs and process diagrams.
• draw.io (diagrams.net): A free, open-source diagramming tool for creating cause–effect graphs and decision tables.
• EdrawMax: Provides ready-made templates for creating logical diagrams, flowcharts, and decision models.
• IBM Rational Quality Manager: Supports test design, test case management, and requirement traceability using graphical models.
• TestLink: An open-source test management tool used to organize and manage test cases created from cause–effect graph analysis.

Modern Relevance and Technology

• Widely used in Agile and DevOps projects to validate complex business rules efficiently.
• Supports test automation by helping create logical and comprehensive automated test cases.
• Useful for API testing to verify different input combinations and expected responses.
• Applied in web and mobile application testing for validating workflows and user inputs.
• Helps test AI/ML-based applications by validating decision logic and rule-based outcomes.
• Commonly used in banking, healthcare, insurance, and e-commerce applications where business rules are complex.

Advantages of Cause–Effect Graphing

• Ensures systematic test case design by mapping input conditions to expected outputs.
• Improves test coverage by validating all important cause-effect relationships.
• Helps identify missing, ambiguous, or conflicting requirements early.
• Reduces redundant test cases by focusing on logical combinations.
• Simplifies testing of complex business rules and decision logic.
• Improves defect detection by testing logical conditions effectively.

Limitations of Cause–Effect Graphing

• Becomes complex and difficult to manage when there are many input conditions.
• Creating and maintaining the graph can be time-consuming.
• Requires clear, complete, and accurate requirements to be effective..
• Converting large graphs into Decision Tables can be challenging.
• Does not effectively test performance, usability, or other non-functional requirements.
• Requires testers to understand logical relationships and graph construction.

Symbols Used in Cause–Effect Graph