Test Coverage: Understanding Linear Code Sequence and Jump (LCSAJ)

First is first:

What has caught my attention on this subject is the railway standard EN 50716:2023 Railway Applications. Requirements for software development

The standard includes D.50 Structure based testing

a) Statements: b) Branches: c) Compound conditions:

d) LCSAJ (Linear Code Sequence And Jump): a linear code sequence and jump is any linear sequence of code statements including conditional jumps terminated by a jump. Many potential sub-paths will be infeasible due to constraints on the input data imposed by the execution of earlier code.

e) Data flow: f) Call graph: g) Entire path

#Authors_comment: a,b,c,e,f, and g is familiar but what the LCSAJ??

Software testing is a critical aspect of development, but one question always lingers: "How much testing is enough?" The answer often lies in understanding how thoroughly our tests exercise the underlying structure of our code.

That's where a powerful dynamic analysis technique called Linear Code Sequence and Jump (LCSAJ) comes into play. LCSAJ helps us quantify the effectiveness of our test data by analyzing structural units of code, a concept tightly linked to control flow analysis and path testing, which are foundational in software engineering research [1, 2].

Recent research is exploring the nuances of different coverage metrics and their impact on fault detection effectiveness [3].

LCSAJ is a software analysis method used to identify structural units within code. It's particularly valuable in dynamic software analysis, where we examine code behavior during execution. In essence, LCSAJ helps us measure how much of our code has been executed ("touched") by our tests, leading to more effective and reliable software. The idea of using structural coverage to assess testing completeness has been a cornerstone of software engineering research for decades, and it continues to evolve with new techniques and tools [4]. Researchers are also exploring advanced methods for generating test cases that achieve high structural coverage, leveraging techniques like symbolic execution and search-based testing [5].

What is LCSAJ?

Think of LCSAJ as a way to break down your code into meaningful segments for analysis. In its broadest sense, it’s a method for identifying these fundamental building blocks, sometimes referred to as "basic blocks" in program analysis literature [6]. Analyzing code at the basic block level provides a detailed view of control flow and allows for precise test coverage measurement. Research into program slicing techniques offers another way to isolate relevant code segments for analysis and testing [7].

More specifically, LCSAJ often refers to a Jump-to-Jump Path (JJ-Path): a linear sequence of code bounded by jump instructions (e.g., conditional statements, loops, function calls, returns). The concept of path-based testing and its importance in achieving thorough test coverage has been extensively studied [8, 9]. Recent work also emphasizes the importance of considering data dependencies along with control flow to create more effective tests [10]. Furthermore, advancements in AI are beginning to influence how we approach test generation and execution, potentially leading to more efficient and comprehensive path coverage [11].

Why is LCSAJ Important?

• Systematic Testing: By identifying JJ-Paths, we can create tests that systematically cover all possible execution flows within our code. This aligns with structured testing methodologies that emphasize complete coverage of code elements [12].
• Measuring Test Effectiveness: LCSAJ forms the basis for structural coverage metrics. We can measure how many JJ-Paths have been executed by our tests, giving us a concrete number to represent test thoroughness, and aiding in the question of “how much is enough”. The development, comparison, and refinement of coverage criteria and metrics remain an active area of research [13,14].
• Identifying Gaps: If a JJ-Path hasn't been exercised during testing, it's a red flag, indicating a potential bug lurking in that untested code segment. Tools and techniques for identifying untested code segments are crucial for improving software quality and are often integrated into continuous integration/continuous delivery (CI/CD) pipelines [15].
• Regression Prevention: Tracking JJ-Path coverage is crucial during regression testing. We can ensure changes to the code haven’t inadvertently broken previously tested paths. Regression testing strategies often use coverage analysis to ensure that modifications haven’t introduced new defects or uncovered old ones [16]. Smart or intelligent regression testing techniques that leverage code analysis and historical coverage data are gaining popularity to reduce testing time while maintaining effectiveness.

Example in Action:

Code:

int calculate(int input) {
    int result = input;            // Line 1
    if (input > 10) {               // Line 2 - Jump 1
        result = result * 2;       // Line 3
    } else {                             // Line 4 - Jump 2
        result = result / 2;      // Line 5
    }
    return result;                  // Line 6 - Jump 3
}        

Simple flowchart:

[Start] --> int result = input; --> [input > 10?]
                                      /       \
                                    /           \
                                  /               \
                              Yes                No
                             /                        \
                  result = result * 2;      result = result / 2;
                             \                          /
                               \                     /
                                 \                /
                                   \           /
                                     \       /
                             return result; --> [End]        

What are we looking for?

Here, we can identify several JJ-Paths:

Remember, a JJ-Path is a straight sequence of code that:

• Starts at a "jump" point (like an if, else, return, or the beginning of a function).
• Ends at another "jump" point.
• Has no other jumps in between.

Let's identify the JJ-Paths step by step:

Path 1:

Starts: At the beginning of the function calculate (Line 1 is the first statement). Think of the function call as an initial jump into the code.

Ends: At the if statement (Line 2), because the if is a conditional jump. It will either go to Line 3 or Line 4.

The path: int result = input; to if (input > 10)

#Authors_comment: This is the setup part. We take the input and get ready to check if it's greater than 10.

Path 2:

Starts: After the if condition is true (we've jumped into the if block - Line 3)

Ends: At the end of the if block (implied jump to Line 6). This is because after the if code executes we move to the return statement.

The path: result = result * 2;

#Authors_comment: If the input was big, we double it. This is a single action, so it's a short path.

Path 3:

Starts: After the if condition is false (we've jumped into the else block - Line 5).

Ends: At the end of the else block (implied jump to Line 6). Similar to the if block after the else code executes we move to the return statement

The path: result = result / 2;

#Authors_comment: If the input was small, we cut it in half. Again, a single action, a short path.

Path 4:

Starts: After either the if block (Line 3) or the else block (Line 5) has finished executing, because we are going to execute the return statement.

Ends: At the return statement (Line 6), because return is a jump out of the function.

The path: Implied jump from Line 3 or Line 5 to return result;

#Authors_comment: No matter if we doubled or halved the number, we now send the final result back.

Summary of JJ-Paths:

• Path 1: Setup before the if (Line 1 to Line 2).
• Path 2: Action inside the if (Line 3).
• Path 3: Action inside the else (Line 5).
• Path 4: Return result after the condition has been checked and logic executed, (from Line 3/5 to Line 6).

Why this is useful:

By identifying these paths, we know we need to write tests that:

Test for each equivalence partition:

Test 1: Input less than 10 (e.g., 5) - Covers Path 1, Path 3, Path 4 (after path 3)

Test 2: Input greater than 10 (e.g., 15) - Covers Path 1, Path 2, Path 4 (after path 2)

Test boundary values:

Test 3: Input equal to the boundary (e.g., 10) - Checks if the boundary is handled correctly on Path 1, Path 3, Path 4 (after path 3)

Test 4: Input just below the boundary (e.g., 9) - Double checks the boundary on Path 1, Path 3, Path 4 (after path 3)

Test 5: Input just above the boundary (e.g., 11)- Double checks the boundary on Path 1, Path 2, Path 4 (after path 2)

Consider other potentially problematic inputs:

Test 6: Zero (0) - A common special case. - Checks if zero is handled correctly, this will follow Path 1, Path 3, Path 4 (after Path 3)

Test 7: Large positive number (e.g., 1000000) - Tests for potential overflow issues on Path 1, Path 2, Path 4 (after Path 2)

Test 8: Large negative number (e.g., -1000000) - Tests for potential issues with negative numbers, this will follow Path 1, Path 3, Path 4 (after Path 3).

LCSAJ isn't a panacea, but it’s a valuable tool in a tester’s arsenal. It helps us move beyond simply checking if code works to ensuring it works reliably under all intended conditions.

By understanding the structure of our code and systematically covering its key execution paths, we can build higher-quality, more robust software.

As software systems become increasingly complex, and with the rise of AI-generated code, the need for sophisticated testing techniques like LCSAJ, often in combination with other advanced methodologies, will continue to grow.

References:

[1] Ammann, P., & Offutt, J. (2016). Introduction to Software Testing. Cambridge University Press.

[2] Sommerville, I. (2015). Software Engineering (10th ed.). Pearson Education.

[3] Mao, K., Harman, M., & Jia, Y. (2021). A Study of the Relationship Between Structural Coverage and Fault Detection Effectiveness. IEEE Transactions on Software Engineering, 47(10), 2171-2191.

[4] Briand, L. C., & Labiche, Y. (2020). A pragmatic introduction to software testing. Communications of the ACM, 63(9), 76-85.

[5] Afzal, W., & Torkar, R. (2021). Automated test case generation using many-objective search: a systematic literature review. Information and Software Technology, 135, 106555.

[6] Aho, A. V., Lam, M. S., Sethi, R., & Ullman, J. D. (2006). Compilers: Principles, Techniques, and Tools (2nd ed.). Pearson Education.

[7] Silva, J. P., & Abreu, R. (2020). Program Slicing: State-of-the-art and research challenges. ACM Computing Surveys, 53(4), 1-35.

[8] Gligoric, M., Eloussi, L., & Marijan, D. (2021). Path-Based Test Generation: From Theory to Practice. In Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (pp. 1-12).

[9] Zhang, L., et al. (2022). Path-Oriented Symbolic Execution for Automated Test Generation. IEEE Transactions on Reliability, 71(2), 567-582.

[10] McMinn, P. (2023). Data-Dependent Testing: Where are we now, and where are we going? In Proceedings of the 45th International Conference on Software Engineering (pp. 1412-1423).

[11] Tufano, M., et al. (2023). An Empirical Investigation into the Effectiveness of AI-Based Test Generation Tools. Empirical Software Engineering, 28, Article 103.

[12] ISO/IEC/IEEE. (2011). Systems and software engineering -- Vocabulary. (ISO/IEC/IEEE 24765:2011).

[13] Murphy, C., & Kaiser, G. E. (2022). A comparative analysis of control-flow and data-flow coverage criteria for detecting software faults. Information and Software Technology, 145, 106827.

[14] Javed, Z., Shamshirband, S., & Alzaqebah, A. (2023). A Review on Test Coverage Criteria and Their Applications in Software Testing. Applied Sciences, 13(10), 6025.

[15] Vasilescu, C., Posnett, D., Ray, B., & Filkov, V. (2021). Continuous Integration Practices and Perceived Productivity: A Qualitative Study of Software Developers’ Experiences. IEEE Transactions on Software Engineering, 47(7), 1493-1510.

[16] Yoo, S., & Harman, M. (2021). Regression testing minimization, selection, and prioritization: a survey. Software Testing, Verification and Reliability, 31(2), e1739.