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Analyzing Regression Test Selection Techniques -presented by Xuan Lin.

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Presentation on theme: "Analyzing Regression Test Selection Techniques -presented by Xuan Lin."— Presentation transcript:

1 Analyzing Regression Test Selection Techniques -presented by Xuan Lin

2 Outline Introduction Concepts and Assumptions Analysis Framework Examples Techniques Conculsion and Discussion

3 Outline Introduction Concepts and Assumptions Analysis Framework Examples Techniques Conculsion and Discussion

4 Introduction What is Regression Testing? -Everybody knows … Retest-all strategy VS. Test Selection Notions: P,P,S,S,T,T,

5 Typical Selective Retest Process 1. Select T'T, a set of tests to execute on P' 2.Test P with T, to establish the correctness of P with respect to T 3.If necessary, create T, a set of new functional or structural tests for P 4.Test P with T, to establish the correctness of P with respect to T 5.Create T, a new test suite and test history for P, from T, T and T Regression Test Selection Problem Test Suite Execution Problem Coverage Identification Problem Test Suite Maintenance

6 Typical Selective Retest Process 1. Select T'T, a set of tests to execute on P' 2.Test P with T, to establish the correctness of P with respect to T 3.If necessary, create T, a set of new functional or structural tests for P 4.Test P with T, to establish the correctness of P with respect to T 5.Create T, a new test suite and test history for P, from T, T and T Regression Test Selection Problem Test Suite Execution Problem Coverage Identification Problem Test Suite Maintenance

7 Test Selection Techniques Specification-based VS. Code-based Three distinct goals of code-based test selection techniques -Coverage Techniques -Minimization Techniques -Safe Techniques Compare and Evaluation !!!

8 Outline Introduction Concepts and Assumptions Analysis Framework Examples Techniques Conculsion and Discussion

9 Concepts and Assumptions Fault-realing for P: cause P to fail -No Effective procedure by which to find tests in T that are fault-realing for P [1] -Under certain conditions, a technique can select a Superset of the set of fault-revealing for P Modification-revealing: casue the outputs of P and P to differ.

10 Concepts and Assumptions Modification-revealing = Fault-revealing ??? P-Correct-for-T Assumption: For each test t in T, when P was tested with t, P halted and produced the correct output Obsolet-Test-Identification Assumption: There is an effective procedure for determining, for each test in t, whether t is obsolete for P. Test t is obsolete for P if and only if t either specifies an input to P that, according to S, is invalid for P, or t specifies an invalid input-output relation for P

11 Concepts and Assumptions Up to now, we can find the fault-revealing test cases by: 1. Run our procedure for identifying obsolete test in T. 2. Remove them. 3. Find the modification-revealing test cases. - In the set of non-obsolete test cases, modification- revealing=fault-revealing

12 Obsolete Nonbsolete Fault-Revealing Modification-Revealing Concepts and Assumptions ???

13 Concepts and Assumptions Modification-traversing: a test t is modification-traversing for P and P if and only if it (a) executes new or modified code in P, or (b) formerly executed code that has since been deleted

14 Obsolete Nonbsolete Fault-Revealing Modification-Revealing Modification-Traversing Concepts and Assumptions ???

15 Concepts and Assumptions Controlled Regression Testing Assumption: when P is tested with t, we hold all factors that might infuence the output of P, except for the code in P, constant with respect to their states when we tested P with t.

16 Why We Need Define These Concepts and Assumptions? Evaluate test selection techniques in terms of their ablities to select and avoid discarding fault-revealing tests. Three classes can be used to distinguish techniques even CRTA is not satisfied. Coverage techniques may omit tests from T that may reveal faults in P

17 Outline Introduction Concepts and Assumptions Analysis Framework Examples Techniques Conculsion and Discussion

18 Analysis Framework Incusiveness Precision Efficiency Generality

19 Analysis Framework- Inclusiveness

20 There is no algorithm to determine the inclusiveness! However… We can prove M is safe. We can prove M is not safe. We can compare techinques in terms of inclusiveness We can experiment to approximate

21 Analysis Framework-Precision

22 There is no algorithm to determine the precision! However… We can compare techinques in terms of precision. We can prove M is not precise We can show M is precise. We can experiment to compare.

23 Analysis Framework-Efficiency Time & Space Cost of selecting T < the cost of running T- T Three Factors 1.preliminary phase vs. critical phase 2.automatability 3. calculation informatin on program modifications 4.ability to handle multiple modifications

24 Analysis Framework- Generality Should function for some identifiable and practical class of program Should handle realistic program modifications Should be independent of assumptions about testing or maintenance enviroments. Should be independent of particular program analysis tools Should support intraprocedural or interprocedural test selection

25 Analysis Framework-Tradeoffs Precision vs. Efficiency - both safe and unsafe Inclusiveness vs.Efficiency -not safe Generality vs. Inclusiveness, Efficiency or Precision Multiple modication vs. Efficiency

26 Outline Introduction Concepts and Assumptions Analysis Framework Examples Techniques Conculsion and Discussion

27 Obsolete Nonbsolete Fault-Revealing Modification-Revealing Modification-Traversing Refresh…

28 Depiction of inclusiveness and precision

29 Retest-all

30 Optimum

31 Examples: Dataflow Caculate d-u pairs for both P and P Identify and select d-u pairs that are new in, or modified for P Some techniques also select deleted d-u pairs Incremental / Nonincremental

32 Examples: Dataflow-Inclusion Not safe

33 Examples: Dataflow-Precision Not precise

34 Examples: Dataflow

35 Examples: Dataflow-Effiency Incremental- O(|T|*|P|*|P|) Nonincremental-

36 Examples: Dataflow-Generity Applied to procedural programs generally. Function for all program changes except those that do not alter d-u association Some techiques applied to intraprocedural programs while others applied to interprocedural programs Incremental approach requires incremental dataflow analysis tools.

37 Examples: Graph Walk Techniques Build CFG for P and P Collects traces for tests with CFG edges. Performs synchronous depth-first traversals of the two graphs, selects those are not lexically identical.

38 Examples: Graph Walk Techniques-Inclusiveness [1] shows that for controlled regression testing, the techniques will select all modification-traversing test. So, it is safe.

39 Examples: Graph Walk Techniques-Precision Not precise Multiply-visisted-node

40 Examples: Graph Walk Techniques

41 In practice

42 Improved version

43 Examples: Graph Walk Techniques-Efficiency Generally: Property not hold[1]:

44 Examples: Graph Walk Techniques-Generality Apply to procedural languages generally All type of modifications Both interprocedure and intraprocedure No assumption on test suite or coverage Require tools for constructing dataflow and tools for dataflow analysis

45 Examples: Path Analysis Takes set of program paths in P expressed as an algebraic expression Manipulates the expression to get a set of cycle-free exemplar paths. Compare such paths in P with P Tests that traverse modified exemplar paths will be selected

46 Examples: Path Analysis- Inclusiveness Selects only modified paths and omits the cancel and new paths. Not safe.

47 Examples: Path Analysis- Precision It will select all the test cases that are modification-traversing and execute modified exemplar paths.

48 Examples: Path Analysis

49 Examples: Path Analysis- Efficiency Exponential in |P| and |P|

50 Examples: Path Analysis- Generality Assumption: low-level program designs are depicted by language-independent algebraic representations. Does not handle test cases for additions or deletings of code. Does not require any coverage criterian or test generation technique. Require tool for collecting traces at the statement level.

51 Conclusions Framework for evaluating regression test selection technique that classifies techniques in terms of inclusiveness, precision, efficiency, and generality. Several test selection techiques are evaluated

52 Reference [1]G.Rotherel. Efficient, Effective Regression Testing Using Safe TestSelection Techniques.


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