Assessing combinatorial interaction strategy for reverse engineering of combinational circuits

Younis, Mohammed Issam; Zamli, Kamal Z.

doi:10.1109/isiea.2009.5356419

Cited by 8 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GMIPOG is a combinatorial test generator based on specified inputs and parameter interaction. Running on a Grid environment, GMIPOG adopts both the horizontal and vertical extension mechanism (i.e., similar to that of IPOG [3]) in order to derive the required test suite for a given interaction strength [9]. GMIPOG supports high degree of interaction and can be run in cumulative mode (i.e., support one-test-at-a-time approach with the capability to vary t automatically until the exhaustive testing is reached) [9].…”

Section: Related Workmentioning

confidence: 99%

Cumulative interaction strength for T-way Test data Generator (TTG)

Harron¹,

Saod²,

Ramlan³

et al. 2016

AIP Conference Proceedings

View full text Add to dashboard Cite

Abstract. This paper present the development and implementation of cumulative interaction strength for T-way Test data Generation, called Cumulative-TTG, supporting seeding and constraint inputs. Several testing have been conducted to demonstrate the correctness of algorithm for uniform and non-uniform input variables with two level of interaction strength and also to prove the correctness of the implementation.

show abstract

Section: Related Workmentioning

confidence: 99%

Cumulative interaction strength for T-way Test data Generator (TTG)

Harron¹,

Saod²,

Ramlan³

et al. 2016

AIP Conference Proceedings

View full text Add to dashboard Cite

show abstract

“…Based on empirical evidence in four application domains (involving medical devices, a web browser, an HTTP server, and a NASA-distributed database application [32,33]), Kuhn et al concluded that all faults in any typical software system can be detected at t = 6. Using a different application domain, Younis and Zamli demonstrated that only after t = 7 can the behavior of the combinatorial circuits of interest be predicted for reverse engineering applications [59].…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of a t-way test data generation strategy with automated execution tool support

Zamli

Klaib

Younis

et al. 2011

Information Sciences

Self Cite

View full text Add to dashboard Cite

a b s t r a c tTo ensure an acceptable level of quality and reliability of a typical software product, it is desirable to test every possible combination of input data under various configurations. However, due to the combinatorial explosion problem, exhaustive testing is practically impossible. Resource constraints, cost factors, and strict time-to-market deadlines are some of the main factors that inhibit such a consideration. Earlier research has suggested that a sampling strategy (i.e., one that is based on a t-way parameter interaction) can be effective. As a result, many helpful t-way sampling strategies have been developed and can be found in the literature.Several advances have been achieved in the last 15 years, which have, in particular, served to facilitate the test planning process by systematically minimizing the test size required (based on certain t-way parameter interactions). Despite this significant progress, the integration and automation of strategies (from planning process to execution) are still lacking. Additionally, strategizing to sample (and construct) a minimum test set from the exhaustive test space is an NP-complete problem; that is, it is often unlikely that an efficient strategy exists that could regularly generate an optimal test set. Motivated by these challenges, this paper discusses the design, implementation, and validation of an efficient strategy for t-way testing, the GTWay strategy. The main contribution of GTWay is the integration of t-way test data generation with automated (concurrent) execution as part of its tool implementation. Unlike most previous methods, GTWay addresses the generation of test data for a high coverage strength (t > 6).

show abstract

“…Zamli et al [24] presented a similar idea and proposed GTWay addresses, the generation of test data for a high-covering strength t > 6. In a different application domain, Younis and Zamli [25] demonstrated that the behaviour of the combinatorial circuits of interest could be predicted for reverse engineering applications only after t = 7. Recently, a few methods that can support covering strength higher than six have been proposed [18,[26][27][28].…”

mentioning

confidence: 99%

A memetic algorithm for high‐strength covering array generation

et al. 2023

View full text Add to dashboard Cite

Covering array generation (CAG) is the key research problem in combinatorial testing and is an NP-complete problem. With the increasing complexity of software under test and the need for higher interaction covering strength t, the techniques for constructing highstrength covering arrays are expected. This paper presents a hybrid heuristic memetic algorithm named QSSMA for high-strength CAG problem. The sub-optimal solution acceptance rate is introduced to generate multiple test cases after each iteration to improve the efficiency of constructing high-covering strength test suites. The QSSMA method could successfully build high-strength test suites for some instances where t up to 15 within one day cutoff time and report five new best test suite size records. Extensive experiments demonstrate that QSSMA is a competitive method compared to state-of-theart methods. K E Y W O R D S optimisation, particle swarm optimisation, software engineering | INTRODUCTIONSoftware testing is an important area of software engineering and has great significance in ensuring software quality. Due to the large amount of test data required by modern software, if a complete test is performed, the size of the test data will increase sharply as increasing parameters or parameter values, leading to a lower testing efficiency. Thus, effective failure detection at a low cost is a critical issue for test case generation. Combinatorial testing (CT), a popular testing method, can detect failures triggered by various interactions of factors [1].There are two key issues in the field of combinatorial testing: (1) how to construct a test suite as small as possible; (2) how to find the combination of parameters that trigger the fault in a particular test case that triggers a software under test (SUT) failure. Researchers are more concerned with problem one, known as covering array generation (CAG) [2]. The main idea of CT is to construct a test suite represented by a covering array (CA) that contains all combinations of parameter's value of the SUT at least once and hopes the array is as small as possible. The generation of the smallest size test suite has been proven to be an NP-complete problem [3]. Numerous methods have been proposed, including mathematical, greedy, and artificial intelligence-based search methods. However, all the proposed mathematical methods apply only to restrictive parameter sets. Besides, greedy algorithms often result in oversized test suites. Due to this limitation, scholars focus more on AI-based (heuristic) search methods [4].Many efficient heuristic search algorithms have been successfully applied to CA generation, including geneticThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

Assessing combinatorial interaction strategy for reverse engineering of combinational circuits

Cited by 8 publications

References 21 publications

Cumulative interaction strength for T-way Test data Generator (TTG)

Cumulative interaction strength for T-way Test data Generator (TTG)

Design and implementation of a t-way test data generation strategy with automated execution tool support

A memetic algorithm for high‐strength covering array generation

Contact Info

Product

Resources

About