Automatic Detection and Removal of Conformance Faults in Feature Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F16%3A10331916" target="_blank" >RIV/00216208:11320/16:10331916 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ICST.2016.10" target="_blank" >http://dx.doi.org/10.1109/ICST.2016.10</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICST.2016.10" target="_blank" >10.1109/ICST.2016.10</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automatic Detection and Removal of Conformance Faults in Feature Models

Popis výsledku v původním jazyce

Building a feature model for an existing SPL can improve the automatic analysis of the SPL and reduce the effort in maintenance. However, developing a feature model can be error prone, and checking that it correctly identifies each actual product of the SPL may be unfeasible due to the huge number of possible configurations. We apply mutation analysis and propose a method to detect and remove conformance faults by selecting special configurations that distinguish a feature model from its mutants. We propose a technique that, by iterating this process, is able to repair a faulty model. We devise several variations of a simple hill climbing algorithm for automatic fault removal and we compare them by a series of experiments on three different sets of feature models. We find that our technique is able to improve the conformance of around 90% of the models and find the correct model in around 40% of the cases.

Název v anglickém jazyce

Automatic Detection and Removal of Conformance Faults in Feature Models

Popis výsledku anglicky

Building a feature model for an existing SPL can improve the automatic analysis of the SPL and reduce the effort in maintenance. However, developing a feature model can be error prone, and checking that it correctly identifies each actual product of the SPL may be unfeasible due to the huge number of possible configurations. We apply mutation analysis and propose a method to detect and remove conformance faults by selecting special configurations that distinguish a feature model from its mutants. We propose a technique that, by iterating this process, is able to repair a faulty model. We devise several variations of a simple hill climbing algorithm for automatic fault removal and we compare them by a series of experiments on three different sets of feature models. We find that our technique is able to improve the conformance of around 90% of the models and find the correct model in around 40% of the cases.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

2016 IEEE International Conference on Software Testing, Verification and Validation

ISBN

978-1-5090-1827-7

ISSN

2381-2834

e-ISSN

—

Počet stran výsledku

11

Strana od-do

102-112

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Neuveden

Místo konání akce

Chicago

Datum konání akce

10. 4. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000391252900010