Optimizing combinatorial input-output based relations testing using Ant Colony algorithm

Ramli, Norazan Mohamed; Othman, Rozmie Razif; Ali, Mohd Shaiful Aziz Rashid

doi:10.1109/iced.2016.7804713

Cited by 7 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some defining features of FPA are its simplicity, flexibility, and low complexity. Ant Colony System (ACS) [6] is an AI-based strategy and is a type of Ant Colony Optimization (ACO). ACS has effectively resolved numerous combinatorial optimization issues.…”

Section: Related Workmentioning

confidence: 99%

“…However, the spiral bubble-net attacking technique includes a randomness factor (i.e. explorational side), as seen in Equation (6). Meanwhile, in the exploration stage, a random search is conducted, where the position of the current search agent is updated based on a generated random search agent, as illustrated in Algorithm 1.…”

Section: Whale Optimization Algorithmmentioning

confidence: 99%

“…First, the coefficients are updated. Second, based on the random values of A and p, the algorithm updates the position of a solution using either Equation (2) or Equation (9) or Equation (6). Lastly, the WOA returns the best solution obtained.…”

Section: Consider the Example Inmentioning

confidence: 99%

“…To-date, in line with the emergence of a new field called Search-based Software Engineering (SBSE), which deals with solving optimization problems within the Software Engineering lifecycle, many related works have adopted metaheuristics to address the combinatorial t-way test suite gen-eration. Such applications include PSO [3], Cuckoo Search (CS) [4], the Flower Pollination Algorithm (FPA) [5], Ant Colony System (ACS) [6], and High Level Hyper-Heuristics (HHH) [7].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm

et al. 2020

View full text Add to dashboard Cite

The potentially many software system input combinations make exhaustive testing practically impossible. To address this issue, combinatorial t-way testing (where t indicates the interaction strength, i.e. the number of interacting parameters (input)) was adopted to minimize the number of cases for testing. Complimentary to existing testing techniques (e.g. boundary value, equivalence partitioning, cause and effect graphing), combinatorial testing helps to detect faults caused by the faulty interaction between input parameters. In the last 15 years, applications of meta-heuristics as the backbone of t-way test suite generation have shown promising results (e.g. Particle Swarm Optimization, Cuckoo Search, Flower Pollination Algorithm, and Hyper-Heuristics (HHH), to name a few). Supporting the new free lunch theorem, as well as potentially offering new insights into the whole process of t-way generation, this paper proposes a new strategy with constraint support based on the Whale Optimization Algorithm (WOA). Our work is the first attempt to adopt the WOA as part of a search-based software engineering (SBSE) initiative for t-way test suite generation with constraint support. The experimental results of the test-suite generation indicate that WOA produces competitive outcomes compared to some selected single-based and population-based metaheuristic algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Whale Optimization Algorithmmentioning

confidence: 99%

Section: Consider the Example Inmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Another proposed strategy found in 2016 is Ant Colony System (ACS) [33]. ACS is a variant of the Ant Colony Optimization (ACO) algorithm.…”

Section: Another Algorithm That Uses Metaheuristic Search Technique Tmentioning

confidence: 99%

A Review on Recent T-way Combinatorial Testing Strategy

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract. T-way combinatorial testing aims to generate a smaller test suite size. The purpose of t-way combinatorial testing is to overcome exhaustive testing. Although many existing strategies have been developed for t-way combinatorial testing, study in this area is encouraging as it falls under NP-hard optimization problem. This paper focuses on the analysis of existing algorithms or tools for the past seven years. Taxonomy of combinatorial testing is proposed to ease the analysis. 20 algorithms or tools were analysed based on strategy approach, search technique, supported interaction and year published. 2015 was the most active year in which researchers developed t-way algorithms or tools. OTAT strategy and metaheuristic search technique are the most encouraging research areas for t-way combinatorial testing. There is a slight difference in the type of interaction support. However, uniform strength is the most utilized form of interaction from 2010 to the first quarter of 2017.

show abstract

A novel approach for combinatorial test case generation using multi objective optimization

Sabbaghi

Keyvanpour

2017

2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)

View full text Add to dashboard Cite

Optimizing combinatorial input-output based relations testing using Ant Colony algorithm

Cited by 7 publications

References 16 publications

Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm

Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm

A Review on Recent T-way Combinatorial Testing Strategy

A novel approach for combinatorial test case generation using multi objective optimization

Contact Info

Product

Resources

About