Optimizing the software testing efficiency by using a genetic algorithm

Rao, K. Koteswara; Raju, G.; Nagaraj, Srinivasan

doi:10.1145/2464526.2464539

Cited by 9 publications

(5 citation statements)

References 3 publications

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“…Then, TFIDF is premeditated by. 𝑇𝐹𝐼𝐷𝐹(𝑡) = 𝑡/𝑇 × 𝑙𝑜𝑔 𝑁 /𝑛 𝑡 (21) In which: T = word recurrence in apiece report; t = word recurrence in unique record; N = quantity of term presence totally around the archives; and nt = measure of reports has t word. These 2-string measurements be situated subsequently used for computing the weight and distance of the fake experiment [20].…”

Section: H 2 O Algorithm For Ptcmentioning

confidence: 99%

Prioritization of Test Cases in Software Testing Using M 2 H 2 Optimization

Rao¹,

Rao²,

Chaduvula³

et al. 2022

IJMECS

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By and large, software testing can be well thought-out as a adept technique of achieving improved software quality as well as reliability. On the other hand, the eminence of the test cases had significant effect on the fault enlightening competence of testing activity. Prioritization of Test case (PTC) remnants one challenging issue, as prioritizing test cases remains not up in the direction of abrasion by means of respect to Faults Detected Average Percentage (FDAP) and time execution results. The PTC is predominantly anticipated to scheme assortment of test cases in accomplishing timely optimization by means of preferred properties. Earlier readings have been presented for place in order the accessible test cases in upsurge speed the fault uncovering rate in testing. In this phase, this learning schemes a Modern modified Harris Hawks Optimization centered PTC (M 2 H 2 O-PTC) method for testing. The anticipated M 2 H 2 O-PTC method aims to exhaust the possibilities the FDAP and curtail the complete execution time. Besides, the M 2 H 2 O algorithm is considered for boosting the examination and taking advantage abilities of the conservative H 2 O algorithm. For validating the enhanced efficiency of the M 2 H 2 O-PTC method, an extensive variety of simulations occur on contradictory standard programs and the outcomes are inspected underneath numerous characteristics. The investigational results emphasized enhanced proficiency of the M 2 H 2 O-PTC method in excess of the modern methodologies in standings of dissimilar measures.

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Section: H 2 O Algorithm For Ptcmentioning

confidence: 99%

Prioritization of Test Cases in Software Testing Using M 2 H 2 Optimization

Rao¹,

Rao²,

Chaduvula³

et al. 2022

IJMECS

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show abstract

“…Path coverage in the software testing is always an NP problem with the randomly generated test cases, with the genetic algorithm most paths have been found, the randomly generated test cases divided into different groups for the maximum path coverage. K-means algorithm is used to form different clusters and applied GA to generate the new test cases for maximum path coverage [12,14].…”

Section: Literature Surveymentioning

confidence: 99%

Introduction to Data Flow Testing with Genetic Algorithm

Khan¹,

Amjad²

2017

IJCA

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Control flow diagrams are a keystone in testing the structure of software programs. With the help of control flow between the various components of the program, we can select the test cases in a particular domain. In this paper, we introduced a window-based tool for generating the CFG of a C Program automatically. The data flow testing, i.e., control flow testing depends on all def-use of the variables. So selecting the test cases for a particular data flow diagram is not an easy task. In this paper genetic algorithm has been used to generate the test cases automatically for data flow testing.

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“…The results have shown that GA is more effective and efficient than random testing. Rao et al (2013) designed a methodology for test data generation by using GA to cover the most critical path of a program. Singh (2012) applied GA to generate test data for path coverage.…”

Section: Related Workmentioning

confidence: 99%

Automated path testing using the negative selection algorithm

Deris

Mohamad

Aldeen

2017

IJCVR

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Software testing is an important step in the software development process, accounting for more than 50% of software development cost as it is laborious and time-consuming. Generating path test data is the most critical stage in software testing and many approaches have been developed by researchers to automate it. Negative selection algorithm (NSA) has been used in this paper to generate test data for path testing automatically. The proposed algorithm has been applied to the most commonly used benchmarking program which is triangle classifier. The experimental results show that the proposed algorithm is more efficient in time of execution and more effective in the generation of test data when compared with random testing and genetic algorithm.

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Optimizing the software testing efficiency by using a genetic algorithm

Cited by 9 publications

References 3 publications

Prioritization of Test Cases in Software Testing Using M 2 H 2 Optimization

Prioritization of Test Cases in Software Testing Using M 2 H 2 Optimization

Introduction to Data Flow Testing with Genetic Algorithm

Automated path testing using the negative selection algorithm

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