An Ant Colony Optimization approach for test pattern generation

Farah, Rana; Harmanani, Haidar M.

doi:10.1109/ccece.2008.4564771

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…Vats et al [31] gather different ACO applications on software testing, where some of the work focus on OOP testing. The most relevant either generate simple inputs for programs following the classical method of partitioning the domain [12,22], or focus on other aspects of testing like iteration testing [11] and test suite minimization [29]. None of this work considers generating sequences of methods and input values as part of the same process, as Taco does.…”

Section: Related Workmentioning

confidence: 99%

Ant Colony Optimization for Object-Oriented Unit Test Generation

Bruce

Menéndez

Barr

et al. 2020

Lecture Notes in Computer Science

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Generating useful unit tests for object-oriented programs is difficult for traditional optimization methods. One not only needs to identify values to be used as inputs, but also synthesize a program which creates the required state in the program under test. Many existing Automated Test Generation (ATG) approaches combine search with performanceenhancing heuristics. We present Tiered Ant Colony Optimization (Taco) for generating unit tests for object-oriented programs. The algorithm is formed of three Tiers of ACO, each of which tackles a distinct task: goal prioritization, test program synthesis, and data generation for the synthesised program. Test program synthesis allows the creation of complex objects, and exploration of program state, which is the breakthrough that has allowed the successful application of ACO to object-oriented test generation. Taco brings the mature search ecosystem of ACO to bear on ATG for complex object-oriented programs, providing a viable alternative to current approaches. To demonstrate the effectiveness of Taco, we have developed a proof-of-concept tool which successfully generated tests for an average of 54% of the methods in 170 Java classes, a result competitive with industry standard Randoop.

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Section: Related Workmentioning

confidence: 99%

Ant Colony Optimization for Object-Oriented Unit Test Generation

Bruce

Menéndez

Barr

et al. 2020

Lecture Notes in Computer Science

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“…First one is regarded as test pattern generation and concerns finding those input vectors that can reveal more faults. Many different researches are performed on this subject considering its different aspects, some of which could be found in [1], and [2].…”

Section: Introductionmentioning

confidence: 99%

Automatic selection of efficient observability points in combinational gate level circuits using particle swarm optimization

Ghofrani

Javaheri

Safari

et al. 2010

2010 International Symposium on System on Chip

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The ever-increasing size of digital circuits makes the process of testing such designs more complex everyday. This complexity leads to more complicated logic cones, which results in harder to control and observe nodes in digital circuits. Reduced controllability and observability will decrease circuit's fault coverage, resulting in harder to test circuits.Addition of test points, which enables us to control and observe nodes, can give the test engineer direct access to these locations. From another perspective, addition of observability points is more economical in terms of silicon area than adding control points. Still, observability points must be carefully chosen to reduce hardware overhead and gain better fault coverage.In this paper, we select the minimal set of observability points to obtain the maximum coverage. For this purpose, a novel method based on particle swamp optimization (PSO), which is a bio-inspired optimization algorithm, is adopted so that it automatically selects the efficient observability points in order to increase the fault coverage. One should note that this is an offline method which is performed only once for each circuit.

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Speed-Up in Test Methods Using Probabilistic Merit Indicators

Fooladi

Kamran

2020

J Electron Test

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An Ant Colony Optimization approach for test pattern generation

Cited by 7 publications

References 20 publications

Ant Colony Optimization for Object-Oriented Unit Test Generation

Ant Colony Optimization for Object-Oriented Unit Test Generation

Automatic selection of efficient observability points in combinational gate level circuits using particle swarm optimization

Speed-Up in Test Methods Using Probabilistic Merit Indicators

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