Package de.rwth.swc.coffee4j.engine.characterization.aifl

Class Aifl

java.lang.Object

de.rwth.swc.coffee4j.engine.characterization.SuspiciousCombinationAlgorithm

de.rwth.swc.coffee4j.engine.characterization.aifl.Aifl

All Implemented Interfaces:

FaultCharacterizationAlgorithm

Direct Known Subclasses:

IterationBasedIterAifl

public class Aifl
extends SuspiciousCombinationAlgorithm

The implementation of the AIFL fault characterization algorithm as described in "A Software Debugging Method Based on Pairwise Testing". Generates only one set of new test inputs if necessary. As a result, all following iterations will return an empty set of test inputs. Failure-inducing combinations are calculated by subtracting all sub- combinations of failed test inputs and then subtracting those in successful ones.

Additional test inputs are generated by mutating values in failed tests one by one. This means an additional n test inputs per failed test input (n being the number of parameters). For example, if test input (0, 1, 2, 0, 1) failed, 5 new test inputs would be generated: (-, 1, 2, 0, 1), (0, -, 1, 2, 0, 1), (0, 1, -, 0, 1), (0, 1, 2, -, 1), (0, 1, 2, 0, -). "-" stands for an arbitrary different other value.

Important Information: -Generates many additional test inputs if there are many parameters -Generates many additional test inputs if there are many failing test inputs -Does not order failure-inducing combinations by probability and can return quite a few of them -Does not consider constraints

Field Summary

Fields inherited from class de.rwth.swc.coffee4j.engine.characterization.SuspiciousCombinationAlgorithm

configuration, previousSuspiciousCombinations, suspiciousCombinations, testResults

Constructor Summary

Constructors

Constructor	Description
Aifl(FaultCharacterizationConfiguration configuration)	Builds a new instance of the algorithm for a given configuration.

Method Summary

Modifier and Type	Method	Description
static FaultCharacterizationAlgorithmFactory	aifl()	Can be used as a convenience method to describe that AIFL should be used as a FaultCharacterizationAlgorithmFactory.
java.util.List<int[]>	computeFailureInducingCombinations()	Computes a list of most likely failure inducing combinations refined from previous calls to FaultCharacterizationAlgorithm.computeNextTestInputs(Map).
java.util.List<IntArrayWrapper>	generateNextTestInputs(java.util.Map<int[],TestResult> newTestResults)	The concrete algorithm generates test for which it needs the result for better fault characterization.
java.util.Set<IntArrayWrapper>	getRelevantSubCombinations(int[] combination)	Defined which sub-combinations of any given combination can be part of the suspicious set.
boolean	shouldGenerateFurtherTestInputs()

Methods inherited from class de.rwth.swc.coffee4j.engine.characterization.SuspiciousCombinationAlgorithm

computeNextTestInputs, getChecker, getModel, getReporter

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

Aifl

public Aifl(FaultCharacterizationConfiguration configuration)

Builds a new instance of the algorithm for a given configuration. The constraints checker in this configuration will be ignored.

Parameters:

configuration - for knowing which combinations can be failure-inducing/which test inputs can be generated. Must not be null

Throws:

java.lang.NullPointerException - if configuration is null

Method Detail

aifl

public static FaultCharacterizationAlgorithmFactory aifl()

Can be used as a convenience method to describe that AIFL should be used as a FaultCharacterizationAlgorithmFactory.

Returns:

a factory using the constructor (Aifl(FaultCharacterizationConfiguration)) to create new FaultCharacterizationAlgorithm instances

getRelevantSubCombinations

public java.util.Set<IntArrayWrapper> getRelevantSubCombinations(int[] combination)

Description copied from class: SuspiciousCombinationAlgorithm

Defined which sub-combinations of any given combination can be part of the suspicious set. For example, BEN only works on all t-value-combinations while AIFL considers all possible sub-combinations.

Specified by:

getRelevantSubCombinations in class SuspiciousCombinationAlgorithm

Parameters:

combination - for which the relevant sub-combinations are needed

Returns:

all sub-combination the concrete algorithm considers

shouldGenerateFurtherTestInputs

public boolean shouldGenerateFurtherTestInputs()

Specified by:

shouldGenerateFurtherTestInputs in class SuspiciousCombinationAlgorithm

Returns:

Whether further test inputs should be generated. If not, FaultCharacterizationAlgorithm.computeFailureInducingCombinations() will be called next

generateNextTestInputs

public java.util.List<IntArrayWrapper> generateNextTestInputs(java.util.Map<int[],TestResult> newTestResults)

Description copied from class: SuspiciousCombinationAlgorithm

The concrete algorithm generates test for which it needs the result for better fault characterization. If no further characterization is needed, an empty list should be returned.

Specified by:

generateNextTestInputs in class SuspiciousCombinationAlgorithm

Parameters:

newTestResults - the results from the test inputs generated in the last iteration of the initially generated test inputs if in the first iteration

Returns:

test inputs for which the result is needed

computeFailureInducingCombinations

public java.util.List<int[]> computeFailureInducingCombinations()

Description copied from interface: FaultCharacterizationAlgorithm

Computes a list of most likely failure inducing combinations refined from previous calls to FaultCharacterizationAlgorithm.computeNextTestInputs(Map). The combinations returned by this method are not guaranteed to be faulty, but it is guaranteed that no test input executed which contained this combination was successful.

Returns:

a list of faulty combinations. The list may be ranked, depending on the underlying algorithm. If this list is ranked the combinations on positions with smaller indices are more likely to be failure inducing