satel semi automatic testing language university of geneva levi lúcio, didier buchs m-toos,...

SATELSemi Automatic TEsting Language

University of Geneva

Levi Lúcio, Didier Buchs

M-TOOS, Portland 04/18/23

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Presentation Plan

CO-OPN and testing; Semi-automatic test selection; Concepts of SATEL; How do we find tests with SATEL; Conclusions.

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Presentation Plan


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CO-OPN Specification Language

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Algebraic Data Type (ADT)

Petri Net (extended)

MethodGate

CO-OPN Class Account

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Transactional Semantics and Concurrency



Concurrency is automaticallymanaged by Petri Nets…



A transaction is either possible orthe state of the system is not changed!

Synchronization operators: Sim (//), Seq (..), Alt (+)

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CO-OPN Specs as Modelsfor Testing

CO-OPN specs may be seen as reactive systems: Stimulations: method events Observations: method success and gate events (may

be absent…)

We can extend these notions to: Stimulations: simple or synchronized method events Observations: simple or synchronized gate events

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Test Representation

A Test is a graphof Stimulation / Observation pairs with a truth value;

The graphs are expressed in Hennessy-Milner (HML) temporal logic: Sequence Negation Conjunction QuickTime™ and a

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Presentation Plan


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Why is our Test SelectionSemi-Automatic?

The test engineer employs her semantic knowledge of the SUT to manually: Select execution paths to test (HML formulas); Select the shape of stimulation/observation

synchronizations; Constrain parameters of stimulation/observation pairs.

… and automatically find method parameters that correspond to equivalence classes of the method’s behavior

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Test Selection Process



“Manual” selection“Automatic” selection


Presentation Plan



What are Test Intentions?3x

A test intention selectsa part of the model’s transition system andabstracts it…

Test Intention 1

Test Intention 2


Writing Test Intentions

A SATEL test intention is defined by axioms with constrained variables:

Parameters of methods or gates ADT types or Class types

Stimulations Observations HML formulas

A test intention “produces” a test set!


Language Features

Test intentions described by axioms with constraints over variables;

Recursion for describing repetitive paths in the labeled transition system;

Reuse of test intentions for test composition.


Axiom Structure

hmlFormula in intention cond1 & cond2 & … condn => hmlFormula in intention

HML(<loginUser(usr) with obs> T> in login;

Axioms

Variables obs : observation usr : username (ADT)

Non constrained variablesassume all their possible values

(usr into jean::luc::[]) = true =>HML(<loginUser(usr) with obs> T) in login;

Algebraic conditionconstraining the usr variable

subUniformity(usr) => HML(<login(usr) with obs> in login;

Select one value for “usr” perBehavior of the login operation


All constraints over variables

ADT Class Types

Algebraic conditions defined by term equalities

Equalities between object

identifiersonlySimultaneity bool depth int

onlySequence bool nbEvents int

onlyAlternative boolnbOccurrences (of a method)

int

simpleEvent bool sequence boolnbSynchronizations int positive bool

trace boolonlyConstructor boolonlyMutator boolonlyObserver bool

uniformity uniformity uniformity uniformitysubuniformity subuniformity subuniformity subuniformity

Observations / Stimulations HML Formulas

“Automatic” constraining predicate


Presentation Plan



How to find tests

For each test intention:

1. For each axiom find the set of variable substitutions that makes the condition true

Except variables marked with subuniformity

2. Generate per axiom a set of partially instantiated HML formulas


How do we find tests (2)

3. Validate the partially instantiated tests through the Spec’s transition system to find values for the remaining variables…

withdraw(m):(b >= m) => balance b -> balance b-m

Algebraic condition allowing themethod to fire

Petri net pre-conditionPetri net post-conditionAppying a subuniformity predicate on m implies at eachfire of the withdraw calculating one m for each equation:

(b >= m) = true(b >= m) = false

Calculated through logic programming…


Presentation Plan



Conclusions

The technique allows model based test generation;

The test engineer specifies customized reusable test intentions;

Abstractions from the transition system are performed automatically!

Work in progress…


Recursive Axioms and Test Intention Reuse

Several axioms may exist for one test intention and they may be recursive:

Variables f : HML

[] in nWrongPins;

f in nWrongPins => f . HML(<login(newUser(mario)) with errorLogin> T) in nWrongPins;

Base case for the recursion(empty test intention)Recursive definition

Axioms may be reused in other test intentions:

f in nWrongPins & nbEvents(f) < 4 => f in 4LessWrongPins

Condition over HML formulaTest intention reuse


“Automatic” Constraints

subUniformity(usr) => HML(<login(usr) with obs> in login;

During the transition induced by the “login” method select one value for “usr” per behavior of the login operation;

This depends on the behavioral axioms defining the method!

satel semi automatic testing language university of geneva levi lúcio, didier buchs m-toos,...

Documents

satel test intention

test representation

test composition

test engineer

test set

test selection semiautomatic

writing test intentions

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