Integrated Pairwise Testing based Genetic Algorithm for Test Optimization

Swathi, Baswaraju; Tiwari, Harshvardhan

doi:10.14569/ijacsa.2021.0120419

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…A test case comprises a collection of conditions with diverse combinations of input values, executed within a specific scenario to verify the functionality of a completed software configuration system [36], [37]. Finding the best possible final test suite to cover every possible combination of a given interaction strength is a challenge in this field, and the combinatorial explosion problem is one such problem.…”

Section: Pairwise Test Case Generationmentioning

confidence: 99%

Pairwise test case generation with harmony search, one-parameter-at-at-time, seeding, and constraint mechanism integration

Aminu Muazu,

Hashim,

Maiwada

et al. 2024

IJECE

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Pairwise testing is a method for identifying defects through combinatorial analysis. It involves testing all possible combinations of input parameters in pairs within a system, ensuring that each pair is tested at least once. The field of test case generation is highly active in the realm of combinatorial interaction testing. Research in this area is particularly encouraged, as it falls under the category of non-deterministic polynomial-time hardness. A big challenge in this field is the combinatorial explosion problem. It is about finding the best test suite that covers all possible combinations of interaction strength. In this paper, we present the task of discovering a pairwise test set as a search problem and introduce an innovative testing tool referred to as pairwise test case generation in harmony search algorithm with seeding and constraint mechanism (PHOSC). Experimental results show that PHOSC performs better compared to some existing pairwise strategies in terms of test suite size. Additionally, PHOSC provides a comprehensive framework and serves as a research platform for the generation of pairwise test sets employing the harmony search algorithm. It adopts an approach that focuses on one parameter at a time (OPAT) and incorporates seeding and constraint mechanisms at the same time, thereby enhancing the efficiency and effectiveness of the testing process.

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Section: Pairwise Test Case Generationmentioning

confidence: 99%

Pairwise test case generation with harmony search, one-parameter-at-at-time, seeding, and constraint mechanism integration

Aminu Muazu,

Hashim,

Maiwada

et al. 2024

IJECE

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“…Simultaneously, traffic accidents typically exhibit characteristics of imbalance, such as the far smaller number of accident samples compared to normal traffic samples [3], a significantly higher number of accidents in high-risk areas compared to low-risk areas [4], and an imbalance in the sample proportions of different accident types. Particularly, in traffic accidents, the number of severe events is much lower than that of common events.…”

Section: Introductionmentioning

confidence: 99%

ReMAHA–CatBoost: Addressing Imbalanced Data in Traffic Accident Prediction Tasks

Li,

Wu,

Bai

et al. 2023

Applied Sciences

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Using historical information from traffic accidents to predict accidents has always been an area of active exploration by researchers in the field of transportation. However, predicting only the occurrence of traffic accidents is insufficient for providing comprehensive information to relevant authorities. Therefore, further classification of predicted traffic accidents is necessary to better identify and prevent potential hazards and the escalation of accidents. Due to the significant disparity in the occurrence rates of different severity levels of traffic accidents, data imbalance becomes a critical issue. To address the challenge of predicting extremely imbalanced traffic accident events, this paper introduces a predictive framework named ReMAHA–CatBoost. To evaluate the effectiveness of ReMAHA–CatBoost, we conducted experiments on the US–Accidents traffic accident dataset, where the class label imbalance reaches up to 91.40 times. The experimental results demonstrate that the proposed model in this paper exhibits exceptional predictive performance in the domain of imbalanced traffic accident prediction.

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