Ant Colony Optimization Based Algorithm for Test Path Generation Problem with Negative Constraints

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG42__%2F25%3A00563694" target="_blank" >RIV/60162694:G42__/25:00563694 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/24:00378799

Výsledek na webu

<a href="https://ieeexplore.ieee.org/xpl/conhome/9724650/proceeding" target="_blank" >https://ieeexplore.ieee.org/xpl/conhome/9724650/proceeding</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/QRS62785.2024.00075" target="_blank" >10.1109/QRS62785.2024.00075</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Ant Colony Optimization Based Algorithm for Test Path Generation Problem with Negative Constraints

Popis výsledku v původním jazyce

Path-based testing is an established method for creating test cases comprising sequences of steps executed in a System Under Test (SUT). Several algorithms for generating the test sequences (paths) that satisfy various test coverage criteria determining their properties are published in the literature. However, existing path-based testing techniques have limited applicability in numerous practical cases, such as when executing a particular test step in the test further excludes the execution of another test step. More complex exclusion requirements (further denoted negative constraints) exist in real systems, depending on the number of executions of a particular test step. In the paper, we discuss two possible negative constraints, namely, (1) the complete exclusion of a step as a consequence of the execution of a particular previous step and (2) the requirement to include a particular step maximally once in one test path, when another step was executed previously. We present a novel ant-colony-optimization (ACO) principle-based algorithm, accepting a SUT model based and a set of negative constraints, and computing a set of test paths while maximizing edge coverage and satisfying the given set of negative constraints. We compare the results of the ACO-based algorithm with those returned by a baseline, an alternative algorithm that excludes specific test paths from a set of test paths satisfying edge coverage, and, for reference, with results returned by an algorithm that generates test paths that satisfy edge coverage. Evaluated on 152 problem instances, the presented ACO-based algorithm outperformed the baseline in the average length of test paths (representing the testing costs) lower by 32.62%. Also, the ratio of edge coverage satisfaction in the set of test paths computed by the ACO-based algorithm is better by 3.41% compared to the baseline.

Název v anglickém jazyce

Ant Colony Optimization Based Algorithm for Test Path Generation Problem with Negative Constraints

Popis výsledku anglicky

Path-based testing is an established method for creating test cases comprising sequences of steps executed in a System Under Test (SUT). Several algorithms for generating the test sequences (paths) that satisfy various test coverage criteria determining their properties are published in the literature. However, existing path-based testing techniques have limited applicability in numerous practical cases, such as when executing a particular test step in the test further excludes the execution of another test step. More complex exclusion requirements (further denoted negative constraints) exist in real systems, depending on the number of executions of a particular test step. In the paper, we discuss two possible negative constraints, namely, (1) the complete exclusion of a step as a consequence of the execution of a particular previous step and (2) the requirement to include a particular step maximally once in one test path, when another step was executed previously. We present a novel ant-colony-optimization (ACO) principle-based algorithm, accepting a SUT model based and a set of negative constraints, and computing a set of test paths while maximizing edge coverage and satisfying the given set of negative constraints. We compare the results of the ACO-based algorithm with those returned by a baseline, an alternative algorithm that excludes specific test paths from a set of test paths satisfying edge coverage, and, for reference, with results returned by an algorithm that generates test paths that satisfy edge coverage. Evaluated on 152 problem instances, the presented ACO-based algorithm outperformed the baseline in the average length of test paths (representing the testing costs) lower by 32.62%. Also, the ratio of edge coverage satisfaction in the set of test paths computed by the ACO-based algorithm is better by 3.41% compared to the baseline.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

50900 - Other social sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

IEEE International Conference on Software Quality Reliability and Security

ISBN

979-8-3503-6563-4

ISSN

2693-9185

e-ISSN

2693-9177

Počet stran výsledku

12

Strana od-do

701-712

Název nakladatele

IEEE COMPUTER SOC

Místo vydání

—

Místo konání akce

Cambridge, ENGLAND

Datum konání akce

1. 7. 2024

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

001327094200065