General Optimization Strategies for Refining the In-Parameter-Order Algorithm

Gao, Shiwei; Lv, Jianghua; Du, Binglei; Jiang, Yaruo; Ma, Shilong

doi:10.1109/qsic.2014.15

Cited by 4 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Six of our models under test have more than 20 parameters (i.e., Program 6, 12-15 and 17 shown in Table II). Eight modeled programs have more than 10 values per parameter on average (i.e., Program 1,7,9,10,[12][13][14][15][16][17]. Three programs contain more than 20 parameters and more than 10 values per parameter on average (i.e., Program 12,15 and 17).…”

Section: B Generation Time and Number Of Test Casesmentioning

confidence: 99%

“…Several testing strategies have been proposed to reduce the number of combinations that have to be enumerated and the time to generate test cases. A combinatorial testing strategy called In-Parameter-Order (IPO) has been used in several different algorithms to handle problems such as the sheer number of test cases and long generation times [12] for 2-way testing. The In-Parameter-Order-General (IPOG) algorithm has been proposed as a variant of the IPO strategy that supports higher strength than 2-way [13], [14].…”

Section: A Combinatorial Testingmentioning

confidence: 99%

See 1 more Smart Citation

Combinatorial Modeling and Test Case Generation for Industrial Control Software Using ACTS

Ericsson¹,

Enoiu²

2018

2018 IEEE International Conference on Software Quality, Reliability and Security (QRS)

View full text Add to dashboard Cite

Combinatorial testing has been suggested as an effective method of creating test cases at a lower cost. However, industrially applicable tools for modeling and combinatorial test generation are still scarce. As a direct effect, combinatorial testing has only seen a limited uptake in industry that calls into question its practical usefulness. This lack of evidence is especially troublesome if we consider the use of combinatorial test generation for industrial safety-critical control software, such as are found in trains, airplanes, and power plants.To study the industrial application of combinatorial testing, we evaluated ACTS, a popular tool for combinatorial modeling and test generation, in terms of applicability and test efficiency on industrial-sized IEC 61131-3 industrial control software running on Programmable Logic Controllers (PLC). We assessed ACTS in terms of its direct applicability in combinatorial modeling of IEC 61131-3 industrial software and the efficiency of ACTS in terms of generation time and test suite size. We used 17 industrial control programs provided by Bombardier Transportation Sweden AB and used in a train control management system. Our results show that not all combinations of algorithms and interaction strengths could generate a test suite within a realistic cut-off time. The results of the modeling process and the efficiency evaluation of ACTS are useful for practitioners considering to use combinatorial testing for industrial control software as well as for researchers trying to improve the use of such combinatorial testing techniques.

show abstract

Section: B Generation Time and Number Of Test Casesmentioning

confidence: 99%

Section: A Combinatorial Testingmentioning

confidence: 99%

Combinatorial Modeling and Test Case Generation for Industrial Control Software Using ACTS

Ericsson¹,

Enoiu²

2018

2018 IEEE International Conference on Software Quality, Reliability and Security (QRS)

View full text Add to dashboard Cite

show abstract

“…Then the IPOG algorithm is given to generate t-way covering array in [9]. IPOG-D [17], IPOG-F and IPOG-F2 algorithms [18,19,20], which are the variation of IPOG, are put forward. Generally speaking, the first class greedy algorithm is able to construct a relatively small size of covering arrays at the cost of time, while the second class greedy algorithm is able to construct a test suite with trivial covering arrays but less time overhead.…”

Section: Related Workmentioning

confidence: 99%

Automatic Test Case Generation and Optimization Strategies based on Components with Sequential Constraints

Gao¹,

Lv²,

Yang³

et al. 2016

IJCA

Self Cite

View full text Add to dashboard Cite

Combinatorial testing has been proved to be an efficient strategy in software testing. The research on automatic test case generation for system level based on components with constraints is a challenging but significant problem by using combinatorial testing. This paper focuses on the test case generation for system level based on components with sequential constraints. There are three main contributions: 1) the new test case generation strategy for system level is proposed based on IPOG (In-Parameter-Order-General) algorithm; 2) a filling strategy for don't care positions is given to enhance the higher strength coverage for system level, which can improve the quality of the generated test suite; 3) a randomized post-optimization algorithm is applied to reduce the size of the test suite for system level. Experimental results show that the proposed filling strategy can improve the quality of the covering array, the post-optimization algorithm has a good optimization effect on reducing the size of the array, and trades off better between the size of the covering array and computational time than simulated annealing (SA).

show abstract

Evaluation of Tie-Breaking and Parameter Ordering for the IPO Family of Algorithms Used in Covering Array Generation

Kleine

Kotsireas

Simos

2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

General Optimization Strategies for Refining the In-Parameter-Order Algorithm

Cited by 4 publications

References 22 publications

Combinatorial Modeling and Test Case Generation for Industrial Control Software Using ACTS

Combinatorial Modeling and Test Case Generation for Industrial Control Software Using ACTS

Automatic Test Case Generation and Optimization Strategies based on Components with Sequential Constraints

Evaluation of Tie-Breaking and Parameter Ordering for the IPO Family of Algorithms Used in Covering Array Generation

Contact Info

Product

Resources

About