---

Data Flow Testing is a type of software testing where the focus is on tracking the flow of data through a program, specifically how variables are initialized, manipulated, and utilized. It’s a white-box testing technique, as it requires knowledge of the internal workings of the code. The primary objective of data flow testing is to find data-related defects, such as uninitialized variables, data flow anomalies, and improper use of data structures.

Key Aspects of Data Flow Testing:

1. Variable Usage: Understanding how each variable is used within the code – whether it’s being declared, read, or modified.
2. Data Flow Graph: A graphical representation that shows how data moves and transforms as it progresses through the program.
3. Def-Use Chains: Representations of where a variable gets a value (definition) and where that value is used (usage).

Data Flow Anomalies:

1. DU Anomaly: A path where a variable is used before it’s defined.
2. UD Anomaly: A path where a variable is defined but not used.
3. DD Anomaly: A path where a variable is defined more than once without being used in between.

Testing Strategies:

1. All Definitions (All-DU-Paths): Ensure that all definitions of every variable are tested.
2. All Uses: For each definition, ensure that all uses are tested.
3. All-Predicate-Uses: For each definition, test all predicate uses, where the value of the variable can affect the outcome of a decision.

Benefits:

1. Targeted Testing: Pinpoints specific data-related issues that might not be caught in general control flow testing.
2. Improves Code Quality: By ensuring that variables are properly initialized and used, it reduces potential runtime errors and inefficiencies.
3. Comprehensive: Can expose issues related to variable scope, data structures, and data transformation.

Challenges:

1. Complexity: In large systems, tracing every variable’s every possible use can be challenging and time-consuming.
2. Requires Expertise: It demands a deep understanding of the code to track data flow effectively.
3. Overlap with Control Flow Testing: Some paths tested might already be covered under control flow testing.

Conclusion:

Data Flow Testing is a valuable technique in the arsenal of a software tester, especially for ensuring the proper and efficient use of data within the application. While it’s particularly technical and might have overlaps with other white-box testing methods, its focus on data can uncover specific defects that might be missed otherwise. When combined with other testing techniques, it contributes significantly to enhancing software robustness and reliability.

---