Nature-inspired metaheuristic methods in software testing

Khoshniat, Niloofar; Jamarani, Amirhossein; Ahmadzadeh, Ahmad; Haghi Kashani, Mostafa; Mahdipour, Ebrahim

doi:10.1007/s00500-023-08382-8

Cited by 5 publications

(4 citation statements)

References 129 publications

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“…Stage 3-Establishing the review protocol In line with the objectives of this SLR, the preceding stage involved the identification of research questions and the delineation of the review's scope to fine-tune search strings for literature extraction, as per (Brereton et al 2007). Additionally, a protocol was formulated by drawing inspiration from the approach outlined by Brereton, et al (Brereton et al 2007) and our past involvement in SLRs (Bazzaz Abkenar et al 2021Abkenar et al , 2023Khoshniat et al 2023;Songhorabadi et al 2023Songhorabadi et al , 2011Kashani and Mahdipour 2023;Nikravan and Haghi Kashani 2022;Sheikh Sofla et al 2022;Haghi Kashani et al 2021Ahmadi et al 2021;Rahimi et al 2020;Nemati et al 2023;Abkenar et al 2011;Kashani et al 2011). To evaluate the formulated protocol prior to its implementation, external expertise was enlisted from a specialist with proficiency in conducting SLRs within this specific domain.…”

Section: Planning the Systematic Reviewmentioning

confidence: 99%

Big data and predictive analytics: A systematic review of applications

Jamarani,

Haddadi,

Sarvizadeh

et al. 2024

Artif Intell Rev

Self Cite

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Big data involves processing vast amounts of data using advanced techniques. Its potential is harnessed for predictive analytics, a sophisticated branch that anticipates unknown future events by discerning patterns observed in historical data. Various techniques obtained from modeling, data mining, statistics, artificial intelligence, and machine learning are employed to analyze available history to extract discriminative patterns for predictors. This study aims to analyze the main research approaches on Big Data Predictive Analytics (BDPA) based on very up-to-date published articles from 2014 to 2023. In this article, we fully concentrate on predictive analytics using big data mining techniques, where we perform a Systematic Literature Review (SLR) by reviewing 109 articles. Based on the application and content of current studies, we introduce taxonomy including seven major categories of industrial, e-commerce, smart healthcare, smart agriculture, smart city, Information and Communications Technologies (ICT), and weather. The benefits and weaknesses of each approach, potentially important changes, and open issues, in addition to future paths, are discussed. The compiled SLR not only extends on BDPA’s strengths, open issues, and future works but also detects the need for optimizing the insufficient metrics in big data applications, such as timeliness, accuracy, and scalability, which would enable organizations to apply big data to shift from retrospective analytics to prospective predictive if fulfilled.

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Section: Planning the Systematic Reviewmentioning

confidence: 99%

Big data and predictive analytics: A systematic review of applications

Jamarani,

Haddadi,

Sarvizadeh

et al. 2024

Artif Intell Rev

Self Cite

View full text Add to dashboard Cite

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“…Among others, multi-objective evolutionary algorithm has been proposed for SPL testing [6]. Their motivation was to minimize the tests suite by sorting the product lines.…”

Section: Related Workmentioning

confidence: 99%

“…In conventional meta-heuristics approaches, probabilities are implicitly employed in the selection and re-production operators, such as mutation operator, to produce offspring [5], [6]. We identified a research gap in feature configuration exhaustive testing, such that, one can explicitly build a probabilistic model of features distribution from an initial set of test configurations.…”

Section: Introductionmentioning

confidence: 99%

Marginal Distribution Algorithm for Feature Model Test Configuration Generation

Sahid,

Saringat,

Hamzah

et al. 2023

IJACSA

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Generating test configuration for Software Product Line (SPL) is difficult, due to the exponential effect of feature combination. Pairwise testing can generate test input for a single software product that deviates from exhaustive testing, nevertheless proven to be effective. In the context of SPL testing, to generate minimal test configuration that maximizes pairwise coverage is not trivial, especially when dealing with a huge number of features and when constraints must be satisfied, which is the case in most SPL systems. In this paper, we propose an estimation of distribution algorithm, based on pairwise testing, to alleviate this problem. Comparisons are made against a greedybased and a constraint handling based approach. The experiments demonstrate the feasibility of the proposed algorithm, such that it achieves better test configurations dissimilarity and at the same time maintain the test configuration size and pairwise coverage. This is supported by analysis using descriptive statistics.

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“…Testing accounts for almost half of the development cost [1,2]. As a result, one of the major and difficult issues of software engineering is the requirement to lower the overall costs of software tests [3][4][5]. One method for regulating and lowering the expenses of software testing is thought to be the automation of testing processes.…”

Section: Introductionmentioning

confidence: 99%

A Bioinspired Test Generation Method Using Discretized and Modified Bat Optimization Algorithm

Arasteh,

Kiani

et al. 2024

Mathematics

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The process of software development is incomplete without software testing. Software testing expenses account for almost half of all development expenses. The automation of the testing process is seen to be a technique for reducing the cost of software testing. An NP-complete optimization challenge is to generate the test data with the highest branch coverage in the shortest time. The primary goal of this research is to provide test data that covers all branches of a software unit. Increasing the convergence speed, the success rate, and the stability of the outcomes are other goals of this study. An efficient bioinspired technique is suggested in this study to automatically generate test data utilizing the discretized Bat Optimization Algorithm (BOA). Modifying and discretizing the BOA and adapting it to the test generation problem are the main contributions of this study. In the first stage of the proposed method, the source code of the input program is statistically analyzed to identify the branches and their predicates. Then, the developed discretized BOA iteratively generates effective test data. The fitness function was developed based on the program’s branch coverage. The proposed method was implemented along with the previous one. The experiments’ results indicated that the suggested method could generate test data with about 99.95% branch coverage with a limited amount of time (16 times lower than the time of similar algorithms); its success rate was 99.85% and the average number of required iterations to cover all branches is 4.70. Higher coverage, higher speed, and higher stability make the proposed method suitable as an efficient test generation method for real-world large software.

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Nature-inspired metaheuristic methods in software testing

Cited by 5 publications

References 129 publications

Big data and predictive analytics: A systematic review of applications

Big data and predictive analytics: A systematic review of applications

Marginal Distribution Algorithm for Feature Model Test Configuration Generation

A Bioinspired Test Generation Method Using Discretized and Modified Bat Optimization Algorithm

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