A bat-inspired algorithm for prioritizing test cases

Öztürk, Muhammed Maruf

doi:10.1007/s40595-017-0100-x

Cited by 16 publications

(8 citation statements)

References 43 publications

(58 reference statements)

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“…A bat-inspired algorithm BITCP is proposed for TCP providing a good complexity percentage of fault detection correlation [33]. The cost of individual test cases is considered for prioritizing the test cases.…”

Section: Related Workmentioning

confidence: 99%

Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms

Ahmed¹,

Majeed²,

Khan³

2023

Computers, Materials &Amp; Continua

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Test Case Prioritization (TCP) techniques perform better than other regression test optimization techniques including Test Suite Reduction (TSR) and Test Case Selection (TCS). Many TCP techniques are available, and their performance is usually measured through a metric Average Percentage of Fault Detection (APFD). This metric is value-neutral because it only works well when all test cases have the same cost, and all faults have the same severity. Using APFD for performance evaluation of test case orders where test cases cost or faults severity varies is prone to produce false results. Therefore, using the right metric for performance evaluation of TCP techniques is very important to get reliable and correct results. In this paper, two value-based TCP techniques have been introduced using Genetic Algorithm (GA) including Value-Cognizant Fault Detection-Based TCP (VCFDB-TCP) and Value-Cognizant Requirements Coverage-Based TCP (VCRCB-TCP). Two novel value-based performance evaluation metrics are also introduced for value-based TCP including Average Percentage of Fault Detection per value (APFD v ) and Average Percentage of Requirements Coverage per value (APRC v ). Two case studies are performed to validate proposed techniques and performance evaluation metrics. The proposed GA-based techniques outperformed the existing state-of-the-art TCP techniques including Original Order (OO), Reverse Order (REV-O), Random Order (RO), and Greedy algorithm.

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Section: Related Workmentioning

confidence: 99%

Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms

Ahmed¹,

Majeed²,

Khan³

2023

Computers, Materials &Amp; Continua

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“…Each researcher executes the chosen technique based on specific analysis and considerations. Some of the algorithms used include Greedy and Additional Greedy for search-based TCP [37], [40], Firefly Algorithm [38], [78], Neural Network Classifier [44], Ant Colony Optimization [55], [70], FAST Algorithm [79], Support Vector Machine/SVM [80], Genetic [42], [59], [76], [81], Fuzzy Expert [77], Dynamic Programming [45], Recommender System [58], Clustering Technique [73], [82], [83], Particle Swarm Optimization [61], Natural Language Processing (NLP) [74], and Bat-inspired Algorithm [48].…”

Section: ) What Is the State Of The Art For Tcp Based On Tcp Approaches And Techniques? (Rq1)mentioning

confidence: 99%

A Systematic Literature Review on Regression Test Case Prioritization

Rahmani¹,

Ahmad²,

Jalil³

et al. 2021

IJACSA

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Test case prioritization (TCP) is deemed valid to improve testing efficiency, especially in regression testing, as retest all is costly. The TCP schedule the test case execution order to detect bugs faster. For such benefit, test case prioritization has been intensively studied. This paper reviews the development of TCP for regression testing with 48 papers from 2017 to 2020. In this paper, we present four critical surveys. First is the development of approaches and techniques in regression TCP studies, second is the identification of software under test (SUT) variations used in TCP studies, third is the trend of metrics used to measure the TCP studies effectiveness, and fourth is the stateof-the-art of requirements-based TCP. Furthermore, we discuss development opportunities and potential future directions on regression TCP. Our review provides evidence that TCP has increasing interests. We also discovered that requirement-based utilization would help to prepare test cases earlier to improve TCP effectiveness.

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“…e establishment of formative and summative evaluation systems can be guided by the quality standards of English teaching in education, follow the English teaching standards, conduct English teaching quality evaluation seminars by inviting experts, combine the English level of our students, social needs, and other focus points, and also draw on the questionnaires completed by students and the results of English teaching interviews completed by teachers to clarify the evaluation of each. We can also draw on the results of the English teaching quality evaluation questionnaires completed by students and the English teaching interviews completed by teachers to clarify the elements and indicators of the evaluation, formulate corresponding evaluation criteria and conduct quantitative analysis, and finally form an English teaching quality evaluation system suitable for our college [12]. Huang et al directly assigned zero weights to the features with less information and then clustered the selected subspaces.…”

Section: Related Workmentioning

confidence: 99%

A Study on the Quality Evaluation of English Teaching Based on the Fuzzy Comprehensive Evaluation of Bat Algorithm and Big Data Analysis

Ji¹,

Tsai

2021

Mathematical Problems in Engineering

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In this paper, the fuzzy comprehensive evaluation model based on the bat algorithm quantifies the qualitative evaluation effectively and provides a feasible and convenient English teaching quality evaluation system by combining quantitative evaluation with objective index data. Firstly, the English teaching quality evaluation model is constructed based on the fuzzy comprehensive evaluation analysis method and the weight values of each factor are calculated; secondly, the three types of data in the model are processed separately. This includes standardizing the data of objective indicators such as students’ course grades and weakening the influence of course difficulty on this indicator. The fuzzy comprehensive evaluation model based on the bat algorithm quantifies the qualitative evaluation to make the calculated comprehensive evaluation of English teaching quality more comprehensive and objective; then the comprehensive calculation of English teaching quality evaluation is completed, and the English teaching quality evaluation model is constructed by extracting keywords based on the qualitative evaluation; finally, a runnable English teaching quality evaluation system is designed and implemented. A fuzzy comprehensive evaluation algorithm based on improved bat algorithm optimization is proposed. The algorithm uses the improved fuzzy comprehensive evaluation algorithm to optimize the initial clustering centers and adopts a new objective function to guide the clustering process, thus improving the clustering quality of the fuzzy comprehensive evaluation algorithm. Comparative analysis through models shows that the improved algorithm improves the clustering accuracy to a certain extent when compared with the traditional fuzzy comprehensive evaluation clustering algorithm for analysis. The bat algorithm is one of the stochastic global optimization models. It can take advantage of the group, integrate global search and local search, and achieve rapid convergence. Therefore, it plays an important role in optimizing the evaluation of English teaching quality. This study enriches the theoretical study of English teaching quality evaluation to a certain extent and can play a role in strengthening and improving English teaching quality evaluation at the present stage.

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A bat-inspired algorithm for prioritizing test cases

Cited by 16 publications

References 43 publications

Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms

Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms

A Systematic Literature Review on Regression Test Case Prioritization

A Study on the Quality Evaluation of English Teaching Based on the Fuzzy Comprehensive Evaluation of Bat Algorithm and Big Data Analysis

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