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Defect testing

Defect testing. Testing programs to establish the presence of system defects. Test data and test cases. Test data Inputs which have been devised to test the system

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Defect testing

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  1. Defect testing • Testing programs to establish the presence of system defects

  2. Test data and test cases • Test data Inputs which have been devised to test the system • Test cases Inputs to test the system and the predicted outputs from these inputs if the system operates according to its specification

  3. Equivalence partitioning • Input data and output results often fall into different classes where all members of a class are related • Each of these classes is an equivalence partition where the program behaves in an equivalent way for each class member • Test cases should be chosen from each partition

  4. Equivalence partitioning • Partition system inputs and outputs into ‘equivalence sets’ • If input is a 5-digit integer between 10,000 and 99,999, equivalence partitions are <10,000, 10,000-99, 999 and > 10, 000 • Choose test cases at the boundary of these sets • 00000, 09999, 10000, 99999, 10001

  5. Structural testing • Sometime called white-box testing • Derivation of test cases according to program structure. Knowledge of the program is used to identify additional test cases • Objective is to exercise all program statements (not all path combinations)

  6. White-box testing

  7. Binary search (Java)

  8. Binary search - equiv. partitions • Pre-conditions satisfied, key element in array • Pre-conditions satisfied, key element not in array • Pre-conditions unsatisfied, key element in array • Pre-conditions unsatisfied, key element not in array • Input array has a single value • Input array has an even number of values • Input array has an odd number of values

  9. Binary search equiv. partitions

  10. Binary search - test cases

  11. Path testing • The objective of path testing is to ensure that the set of test cases is such that each path through the program is executed at least once • The starting point for path testing is a program flow graph that shows nodes representing program decisions and arcs representing the flow of control • Statements with conditions are therefore nodes in the flow graph

  12. Program flow graphs • Describes the program control flow. Each branch is shown as a separate path and loops are shown by arrows looping back to the loop condition node • Used as a basis for computing the cyclomatic complexity – a function of the number of distinct paths of execution within a module • Use graph theory to suggest a basis set of test

  13. Basic Control Constructs Sequence If-Then-Else While-Do Do-Until Case

  14. Cyclomatic complexity • The number of tests to test all control statements equals the cyclomatic complexity • Cyclomatic complexity equals number of conditions in a program • Useful if used with care. Does not imply adequacy of testing. • Although all paths are executed, all combinations of paths are not executed

  15. Binary search flow graph

  16. Independent paths • 1, 2, 3, 8, 9 • 1, 2, 3, 4, 6, 7, 2 • 1, 2, 3, 4, 5, 7, 2 • 1, 2, 3, 4, 6, 7, 2, 8, 9 • Test cases should be derived so that all of these paths are executed • A dynamic program analyser may be used to check that paths have been executed

  17. Graph Theory • The cyclomatic complexity, V(G) of a graph, G, with N vertices and E edges is V(G) = N – E + 2 • A predicate node is a node at which the path splits. If P is the number of predicate nodes, then V(G) = P + 1

  18. V(G) for Binary Search V(G) – the cyclomatic complexity for Binary Search = Nr Edges – Nr Nodes + 2 = 11 – 9 + 2 = 4 ----------------------------------------------- V(G) = Nr predicate nodes + 1 = 3 + 1 (nodes 2, 3 & 4) = 4

  19. More Graph Theory Euler’s Formula:If G is a connected plane graph (no edges crossing) with N vertices, E edges and R regions, then: N – E + R = 2, or equivalently R = E – N + 2 Since V(G) = E – N + 2, and R = E – N + 2, it follows that V(G) = R I.e. The cyclomatic complexity = the number of regions in the graph

  20. 1 2 3 4 Binary search flow graph

  21. Application to Testing • Associate a module with a graph with unique entry and exit nodes • Each node corresponds to a block of sequential code • Edges represent the program’s branches • Assumes each node can be reached from the entry node and each node can reach the exit node

  22. Application to Testing (cont’d) • The maximum number of independent paths in G is V(G). • V(G) represents the maximum number of test; not all of these tests may be possible

  23. General Notes • Cyclomatic Complexity has no direct relationship to the number of lines of code in a module • So, trying to limit complexity by limiting the number of lines of code is meaningless • Except when case statements are used, the cyclomatic complexity of a module should not exceed 10 • Studies show • 50% of the run time is spent in 4% of the code • For complexity < 5, conventional testing result in full coverage

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