Show simple item record

dc.contributor.advisorEl Fakih, Khaled
dc.contributor.advisorBarlas, Gerassimos
dc.contributor.authorHaddad, Abdul Rahim
dc.date.accessioned2016-09-20T04:51:49Z
dc.date.available2016-09-20T04:51:49Z
dc.date.issued2016-06
dc.identifier.other35.232-2016.37
dc.identifier.urihttp://hdl.handle.net/11073/8471
dc.descriptionA Master of Science thesis in Computer Engineering by Abdul Rahim Haddad entitled, "Efficient Algorithms for Constructing Preset Distinguishing Sequences for Nondeterministic Finite State Machines," submitted in June 2016. Thesis advisor is Dr. Khaled El-Fakih and thesis co-advisor is Dr. Gerassimos Barlas. Soft and hard copy available.en_US
dc.description.abstractDerivation of input sequences for distinguishing states of a finite state machine (FSM) specification is well studied in the context of FSM-based functional testing. We present three heuristics for the derivation of distinguishing sequences for nondeterministic FSM specifications. The first is based on a cost function that guides the derivation process, and the second is a genetic algorithm that evolves a population of individuals of possible solutions (or input sequences) using a fitness function and a crossover operator specifically tailored for the considered problem. The third heuristic is a mutation based algorithm that considers a candidate distinguishing sequence, and if the candidate is not a distinguishing sequence, then the algorithm tries to find a solution by appropriately mutating the candidate. Experiments are conducted to assess the performance of the proposed heuristics in addition to an existing algorithm, called exact algorithm, that derives distinguishing sequences of optimal length. Performance is assessed with respect to execution time, virtual memory consumption, and quality (length) of obtained sequences. Experiments are conducted using randomly generated machines with various numbers of states, inputs, outputs, and degrees of nondeterminism. Further, we assess the impact of varying the number of states, inputs, outputs, and degree of nondeterminism. Finally, in addition to the three proposed heuristics, we present a parallel multithreaded implementation of the exact algorithm using Open Multi-Processing. Experiments are conducted to assess the performance of the parallel implementation as compared to the sequential using both execution time speedup and efficiency.en_US
dc.description.sponsorshipCollege of Engineeringen_US
dc.description.sponsorshipDepartment of Computer Science and Engineeringen_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesMaster of Science in Computer Engineering (MSCoE)en_US
dc.subjectSoftware Engineeringen_US
dc.subjectFunctional Testingen_US
dc.subjectConformance Testingen_US
dc.subjectDistinguishing Experimentsen_US
dc.subjectNondeterministic Finite State Machinesen_US
dc.subjectHeuristicsen_US
dc.subjectGenetic Algorithmsen_US
dc.subject.lcshSequential machine theoryen_US
dc.subject.lcshComputer algorithmsen_US
dc.subject.lcshSequences (Mathematics)en_US
dc.titleEfficient Algorithms for Constructing Preset Distinguishing Sequences for Nondeterministic Finite State Machinesen_US
dc.title.alternativeEfficient algortihms for constructing preset distinguishing sequences for nondeterministic finite state machinesen_US
dc.typeThesisen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record