Calculating Prioritized Interaction Test Sets with Constraints Using Binary Decision Diagrams

Salecker, Elke; Reicherdt, Robert; Glesner, Sabine

doi:10.1109/icstw.2011.79

Cited by 15 publications

(11 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Calculating prioritized interaction test sets with constraints using binary decision diagrams [SRG11], Elke Salecker presented an approach based on Binary Decision Diagrams (BDDs) to support both the selection of test cases using CIT and their prioritization according to user-defined weights. She developed the various steps of the generation algorithm and presented encouraging preliminary comparative evaluation of the approach.…”

Section: Formal Verification and Testingmentioning

confidence: 99%

VAST 2011 workshop summary

Perrouin

Metzger

Heymans

et al. 2011

SIGSOFT Softw. Eng. Notes

View full text Add to dashboard Cite

show abstract

Section: Formal Verification and Testingmentioning

confidence: 99%

VAST 2011 workshop summary

Perrouin

Metzger

Heymans

et al. 2011

SIGSOFT Softw. Eng. Notes

View full text Add to dashboard Cite

show abstract

“…In addition, an extensive survey that focuses on CIT with constraints is also given in [22]. There are only two approaches (to the best of our knowledge) that support prioritized test data generation: the Deterministic Density Algorithm (DDA) presented in [23] and an approach based on Binary Decision Diagrams (BDD) [24]. However, neither of them have been applied to feature models prioritization.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Testing of Traffic Light Programs: Validation in Smart Mobility Scenarios

Ferrer

García-Nieto

Alba

et al. 2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In smart cities, the use of intelligent automatic techniques to find efficient cycle programs of traffic lights is becoming an innovative front for traffic flow management. However, this automatic programming of traffic lights requires a validation process of the generated solutions, since they can affect the mobility (and security) of millions of citizens. In this paper, we propose a validation strategy based on genetic algorithms and feature models for the automatic generation of different traffic scenarios checking the robustness of traffic light cycle programs. We have concentrated on an extensive urban area in the city of Malaga (in Spain), in which we validate a set of candidate cycle programs generated by means of four optimization algorithms: Particle Swarm Optimization for Traffic Lights, Differential Evolution for Traffic Lights, random search, and Sumo Cycle Program Generator. We can test the cycles of traffic lights considering the different states of the city, weather, congestion, driver expertise, vehicle's features, and so forth, but prioritizing the most relevant scenarios among a large and varied set of them. The improvement achieved in solution quality is remarkable, especially for CO 2 emissions, in which we have obtained a reduction of 126.99% compared with the experts' solutions.

show abstract

“…Regarding the data structure we use, a comparison can be done with works using for CCIT binary decision diagrams (BDDs) which are similar to MDDs. Salecker et al [25] developed a test set calculation algorithm which uses BDDs as efficient data structure to represent the combinatorial interaction testing problem with constraints. Both their and our approach are based on the modeling of the combinatorial interaction test problem with constraints as a single propositional logic formula.…”

Section: Related Workmentioning

confidence: 99%

Efficient Combinatorial Test Generation Based on Multivalued Decision Diagrams

Gargantini

Vavassori

2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Combinatorial interaction testing (CIT) is an emerging testing technique that has proved to be effective in finding faults due to the interaction among inputs. Efficient test generation for CIT is still an open problem especially when applied to real models having meaningful size and containing many constraints among inputs. In this paper we present a novel technique for the automatic generation of compact test suites starting from models containing constraints given in general form. It is based on the use of Multivalued Decision Diagrams (MDDs) which prove to be suitable to efficiently support CIT. We devise and experiment several optimizations including a novel variation of the classical greedy policy normally used in similar algorithms. The results of a thorough comparison with other similar techniques are presented and show that our approach can provide several advantages in terms of applicability, test suite size, generation time, and cost.

show abstract

Calculating Prioritized Interaction Test Sets with Constraints Using Binary Decision Diagrams

Cited by 15 publications

References 14 publications

VAST 2011 workshop summary

VAST 2011 workshop summary

Intelligent Testing of Traffic Light Programs: Validation in Smart Mobility Scenarios

Efficient Combinatorial Test Generation Based on Multivalued Decision Diagrams

Contact Info

Product

Resources

About