Grey Box testing Tor Stålhane

What is Grey Box testing Grey Box testing is testing done with limited knowledge of the internal of the system. Grey Box testers have access to detailed design documents with information beyond requirements documents. Grey Box tests are generated based on information such as state-based models or architecture diagrams of the target system.

State based testing The tests are derived from a state model of the system. We can derive the state model in several way, e.g. from Expected system behavior State part of a UML design or requirements specification. Other state diagrams Most system will, however, have a large number of states

Binder’s state control faults – 1 Binder has make a list of common state –related problems in software systems. This list may be used as an input to State based testing State machine or code inspection

Binder’s state control faults – 2 Missing or incorrect – Transitions – new state is legal but incorrect – Events – valid message ignored Extra, missing or corrupt state – unpredictable behavior Sneak path – message accepted when it should not be accepted Illegal message failure – unexpected message causes a failure Trap door – system accepts undefined message.

State test criteria We can choose one or more of the following test selection criteria: All states – testing passes through all states All events – testing forces all events to occur at least once All actions – testing forces all actions to be produced at least once

State test strategies All round-trip paths All transition sequences beginning and ending in the same state All simple paths from initial to final state This strategy will help you to find All invalid or missing states Some extra states All event an action faults

Round-trip path tree – 1 A round-trip path tree Is built from a state transition diagram Includes all round-trip paths – Transition sequences beginning and ending in the same state – Simple paths for initial to final state. If a loop is present, we use only one iteration Is used to – Check conformance to explicit behavioral models – Find sneak paths

Round-trip path tree – 2 A test strategy based on round-trip path trees will reveal: All state control faults All sneak paths – messages are accepted when they should not Many corrupt states - unpredictable behavior

Round-trip path tree – 3 It is important to remember that a test based on the round-trip path tree is model-based. Full coverage from the round-trip path tree implies full model coverage. Full path coverage – which we will look at later – implies full code coverage.

Challenge for round-trip path testing In order to test a system based on state transitions via triggers, predicates (guards) and activities, we need to be able to observe and register these entities. Thus, we may need to include “points of observations” in the code that gives us access to the necessary information.

Round-trip tree – small example   C B A C BA A  B A  A  a[p1] / w b[p2] / u a[p1] / w b[p2] / u

Transitions Each transition in a state diagram has the form trigger-signature [guard] / activity. All parts are optional trigger-signature: usually a single event that triggers a potential change of state. guard: a Boolean condition that must be true for the transition to take place. activity: an action that is performed during the transition.

Test description – 1 Each test completes one branch of the round- trip tree – from  to . The necessary transitions describes the test case. The table on the next slide shows the test case for  -> A -> C -> A C BA A  B A  A 

Test description – 2 IDStart state EventConditionReactionNew state 1  constructor--A 2Aap1wC 3Cbp2uA C BA A  B A  A  b[p2] / u a[p1] / w   C B A b[p2] / u

Sneak path test cases A sneak path – message accepted when it should not be accepted – can occur if There is an unspecified transition The transition occur even if the guard predicate is false

Sneak path test description IDStart state EventConditionReactionNew state 1  constructor--A 2Acp1 Error message A 3Aap1 - false Error message A C BA A  B A  A  b[p2] / u a[p1] / w b[p2] / u a[p1] / w   C B A b[p2] / u

State diagram for a sensor - 1 B   ED C A

State diagram for a sensor - 2  ABCED 

A  EBD  EEC  [sensor alarm] / sound alarm [no sensor alarm] / test [false alarm] / test [alarm OK / request reset] [ ACK] / reset [test OK] [test fails] / replace / test Sensor round-trip path tree

Acknowledgement Most of the previous presentation is based on a slide set from the University of Ottawa, Canada

Mutation testing Tor Stålhane

Type 1 mutation testing – 1 Type 1 mutation testing is done as follows: 1.Write a chunk of code 2.Write a set of tests 3.Test and correct until the test suite runs without errors 4.Change a random part of the code – e.g. a “+” to a “-”. This is called a code mutant. We will only consider mutants that compiles without error messages 5.Run the test suite again

Type 1 mutation testing – 2 6.If the tests suite – runs without errors, then we need to extend the test suite until we discover the defect. – diagnoses the defect and got back to step 4 to create a new mutant. The test process stops when all of X new mutants are discovered by the current test suite.

Type 2 mutation testing Type 2 mutation testing – also called “fuzzing” – has many ideas in common with random testing. The main difference is that: Random testing requires us to generate random tests from scratch. Type 2 mutation testing starts with an input that works OK and then change part of it in a random way.

Software functions are not continuous When we discuss mutation testing, it is important to remember that a function implemented in software is not continuous. E.g. x = 2.00 and x = 1.99 can give dramatically different results. A small changes in input can have a large effect on the output.

Type 2 mutation testing example – 1 SUT – a system for computing F(x) – takes an input consisting of F – a three character string identifying a probability distribution function. A real number x. The allowed value range will depend on F, e.g. if F = “ exp”, then x must be a positive number, while if F = “nor”, then x may be any number.

Type 2 mutation testing example – 2 We can perform type 2 mutation testing as follows: 1.Run a test with input 2.Check that the result is correct 3.Make a mutant by drawing a random integer value 1 (F) or 2 (x). – If we draw a 1, generate a random integer n from 0 to 10 – string size – and generate a random string of length n – If we draw a 2, generate a random real value x 4.Compute F(x) 5.Check the result – especially any error messages 6.If we are satisfied then stop, otherwise repeat from step 3

Mutant testing strategies – 1 The number of possible mutants is large. In order to have a reasonably sized test set there are several strategies for reducing the number of mutated systems of components. The following slides will give a short description of these strategies. Those who will use the strategies should consult the paper by Papdakis and Malevris on mutation testing.

Mutant testing strategies – 2 Mutation testing strategies are either of the first order or the second order. First order strategy – perform a random selection of a portion of the generated mutants – e.g. 10% or 20%. Second order strategy – combine two mutants to get one component to test. The strategy will differ depending on how we choose the mutants.

Assumptions The following strategies assume that Different mutants of the same code can be combined – i.e. they do not concern the same operator, ID etc. Mutants that do not introduce errors – e.g. mutations of comments – are removed from the mutant set.

Second order mutation testing – 1 Random Mix: combine two mutants selected at random from the mutant set. Delete after selection to avoid choosing the same mutant twice. First2Last: sort all mutants according to code line number. Combine the first with the last, the second with the second-to-last and so on. Same Node: same as Random Mix but selected from the same code block

Second order mutation testing – 2 Same Unit: same as Random Mix but selected from the same unit – e.g. class or method DiffOp: same as Random Mix but we select mutants where different operators are mutated – e.g. one mutation of a “+” and one mutation of a “>”. The strategies described above can also be combined – e.g. SU_DiffOp consists of using the DiffOp strategy but only on two mutation from the same unit

Effectiveness measures We will use two measures for mutant test effectiveness – test effectiveness and cost effectiveness. Note that low values implies good results – number of exposed faults is large.

Comments to First Order mutants Selecting 10% of all generated mutants is best both with regard to cost effectiveness and test effectiveness. Strong Mutation – using all generated mutants – is the worst.

Comments to Second Order mutants SU_F2Last – Same Unit and First combined with Last – scores highest on test effectiveness Random Mix – score highest on cost effectiveness No second order strategy is more effective than the Rand(10%) strategy. Here we have that F D = No. of test cases / 1.34