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Test case prioritization

Test case prioritization. Paolo Tonella Fondazione Bruno Kessler tonella@fbk.eu. Motivation. The order in which test cases are executed affects: The rate of fault detection : good orderings reveal faults earlier than bad ones, or (alternatively) they reveal high risk faults earlier;

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Test case prioritization

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  1. Test case prioritization Paolo Tonella Fondazione Bruno Kessler tonella@fbk.eu

  2. Motivation The order in which test cases are executed affects: • The rate of fault detection: good orderings reveal faults earlier than bad ones, or (alternatively) they • reveal high risk faults earlier; • reveal faults related to critical code sections earlier; • give satisfactory confidence in the system’s reliability earlier. • The rate of code coverage: good orderings meet the required coverage level earlier than bad ones.

  3. The need for a heuristic approach The faults revealed by a test case are unknown until the test case is executed and its output is evaluated against the oracle. Heuristics are required to estimate the ability of a test case to reveal faults.

  4. Main prioritization heuristics • Available heuristics take advantage of execution information, assuming this can be obtained at a relatively low cost (if the output is not validated) or can be estimated from previous runs: • Coverage-based prioritization. • Mutation-based prioritization.

  5. Coverage-based heuristics • Total statement coverage: test cases are prioritized by number of statements covered. • Additional statement coverage: test cases are prioritized by incremental number of statements covered. • Total branch coverage: test cases are prioritized by number of branches covered. • Additional branch coverage: test cases are prioritized by incremental number of branches covered.

  6. Statement coverage Total statement coverage: (TC3, TC1, TC2) Additional statement coverage: (TC3, TC2, TC1)

  7. Mutation-based heuristics • Total Fault-Exposing-Potential (FEP): test cases are prioritized by FEP. • Additional Fault-Exposing-Potential (FEP): test cases are prioritized by incremental confidence ΔC. Underlying assumption: very low execution cost. A mutant of a statement is generated (automatically) by randomly changing the statement (e.g., “x++” →“x--”). A mutant is killed by a test case if the original and the mutated program give different outputs when the test case is executed.

  8. Mutation-based heuristics • Additional Fault-Exposing-Potential (FEP): test cases are prioritized by incremental confidence ΔC. C estimates the confidence in the correctness of a statement. Initially, C is zero for every statement. When the test case with highest ΔC is selected, the confidence is updated as follows:

  9. FEP-based prioritization Total FEP: (TC2, TC1, TC3)

  10. ΔC-based prioritization ΔC: (TC2)

  11. ΔC-based prioritization

  12. ΔC-based prioritization ΔC: (TC2, TC3)

  13. ΔC-based prioritization

  14. ΔC-based prioritization ΔC: (TC2, TC3, TC1)

  15. Empirical validation APFD: weighted Average of the Percentage of Faults Detected. 100% faults APFD = 50% A B C D E Prioritized test cases

  16. Empirical validation APFD: weighted Average of the Percentage of Faults Detected. 100% faults APFD = 84% C E B A D Prioritized test cases

  17. Experimental results • All the heuristics outperform the untreated or randomly prioritized test suites. • In almost every case, additional FEP prioritization outperforms coverage-based prioritization, but with only a marginal gain in APFD. • Given their expense, FEP-based prioritization may not be as cost effective as coverage based techniques More details in: Gregg Rothermel, Roland Untch, Chengyun Chu, and Mary Jean Harrold, Test Case Prioritization. IEEE Transactions on Software Engineering, vol.27, no.10, pp.929-948, October, 2001.

  18. Taking advantage of user knowledge Pair-wise comparisons TCi < TCj (TCi, TCj) Prioritized TS 1: TCi1 1 3 5 7 9 AHP 2: TCi2 Intensity of importance n: TCin TS = {TC1, …, TCn} AHP (Analytic Hierarchy Process) determines the target ranking of the test cases in TS from user input (pair-wise comparisons).

  19. Analytic Hierarchy Process 1 Equal 3 Moderate 5 Strong 7 Very strong 9 Extreme TC1 TC2 TC3 TC4 TC1 1/1 3/1 1/5 1/9 1/3 1/1 3/1 3/1 5/1 1/3 1/1 5/1 9/1 1/3 1/5 1/1 A = TC2 TC3 TC4 N * (N-1) / 2 pair-wise comparisons elicited vmax A vmax = λmax vmax TC1 TC3 0.289 0.544 0.634 0.466 TC2 TC2 TC3 TC4 Principal eigenvector TC4 TC1 λmax = 7.248

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