Search for Prioritized Test Cases in Multi-Objective Environment During Web Application Testing

Khanna, Munish; Chauhan, Naresh; Sharma, Dilip Kumar; Toofani, Abhishek; Chaudhary, Achint

doi:10.1007/s13369-017-2830-6

Cited by 13 publications

(10 citation statements)

References 27 publications

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“…This method leads to the advent of popular performance metric Average Percentage of Fault Detection, i.e., APFD [2]. Khanna et al [39], used historical information like fault severity (how much damage a fault can do to the software) and fault detection capability to produce more efficient sorting of test cases.…”

Section: B Tcp Classification (Rq 12)mentioning

confidence: 99%

“…However, due to confidentiality issues, industrial datasets cannot be reused. The remaining studies (14%) have used laboratory/lab programs (controlled experimentation with their private programs) for experimental purposes [29], [39], [43]. The datasets should be publicly available for the new researchers so that they can reproduce the research.…”

Section: ) Dataset Typementioning

confidence: 99%

“…Emma [13] and Cantataa++ [16] are other tools to collect code coverage information. Khanna et al [39], used the Selenium framework for the automatic test case generation and other testing operations. Only one study used jTester for fault seeding purposes [13].…”

Section: Tools Used For Implementation (Rq 22)mentioning

confidence: 99%

“…They found different algorithms suitable for different objectives, but RWGA performed well when they combined all the objectives. Other researchers used NSGA-II and WBGA, [39] for web application testing, and, RWGA and WBGA, for highly configurable software [47].…”

Section: E Genetic Algorithms (Rq 31)mentioning

confidence: 99%

“…From the analysis of selected studies, it is evident that the single objective optimization TCP problems used coverage/performance metrics, e.g., APFD [11] for fault coverage, CE [29] for requirement coverage and APSC [3] for statement coverage as a fitness function, respectively. The multi-objective TCP provides a trade-off among the various objectives, so the studies [21], [28], [39]- [42], [44]- [46] designed multiple objective functions for different solutions utilizing the coverage/performance metrics and/or cost-effectiveness measures. For example, Li et al [46], found the trade-off solutions by maximizing the code coverage using the APSC metric and minimizing the execution cost simultaneously.…”

Section: G Fitness Function Design (Rq 33)mentioning

confidence: 99%

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A Systematic Literature Review of Test Case Prioritization Using Genetic Algorithms

Bajaj

Sangwan

2019

IEEE Access

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Regression testing is the essential process of software maintenance and evolution phase of the software development life cycle for assuring the quality and reliability of updated software. Test case prioritization is the technique of regression testing to reduce the time and effort required for regression testing. Search-based algorithms are used to enhance the efficiency and effectiveness of the method. Among these search-based optimization algorithms, genetic algorithms are becoming more popular among researchers since the last decade. In this paper, we are doing a systematic literature review, i.e., a secondary study of test case prioritization using genetic algorithms. The objective of this review is to examine and classify the current state of use of the genetic algorithm in test case prioritization. In other words, to give a base for the advancement of test case prioritization research using genetic algorithms. With the use of the systematic literature review protocol, we selected the most relevant studies (20 out of 384) from the appropriate repositories by using a set of search keywords, inclusion/exclusion criteria and the quality assessment of studies. The data extraction and synthesis process and the taxonomic classification are used to answer the research questions. We also performed a rigorous analysis of the techniques by comparing them on research methodology, the prioritization method, dataset specification, test suite size, types of genetic algorithms used, performance metrics, and the validation criteria. The whole process took four months for comprehensive analysis and classification of primary studies. We observed that the parameter settings, the type of operators, the probabilistic rate of operators, and fitness function design have a significant impact on the quality of the solutions obtained. This systematic literature review yields that genetic algorithms have great potential in solving test case prioritization problems, and the area is open for further improvements. Future researchers can fill the research gaps by following the suggestions given in the review. From this review, we found that the use of the appropriate approach can make a genetic algorithm based test case prioritization one of the effective methods in regression testing.INDEX TERMS Genetic algorithm, NSGA-II, regression testing, systematic review, test case prioritization.

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Section: B Tcp Classification (Rq 12)mentioning

confidence: 99%

Section: ) Dataset Typementioning

confidence: 99%

Section: Tools Used For Implementation (Rq 22)mentioning

confidence: 99%

Section: E Genetic Algorithms (Rq 31)mentioning

confidence: 99%

Section: G Fitness Function Design (Rq 33)mentioning

confidence: 99%

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A Systematic Literature Review of Test Case Prioritization Using Genetic Algorithms

Bajaj

Sangwan

2019

IEEE Access

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A systematic literature review on software security testing using metaheuristics

Ahsan,

Anwer

2024

Autom Softw Eng

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A new binary chaos-based metaheuristic algorithm for software defect prediction

Arasteh,

Ghaffari

et al. 2024

Cluster Comput

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Software defect prediction is a critical challenge within software engineering aimed at enhancing software quality by proactively identifying potential defects. This approach involves selecting defect-prone modules ahead of the testing phase, thereby reducing testing time and costs. Machine learning methods provide developers with valuable models for categorising faulty software modules. However, the challenge arises from the numerous elements present in the training dataset, which frequently reduce the accuracy and precision of classification. Addressing this, selecting effective features for classification from the dataset becomes an NP-hard problem, often tackled using metaheuristic algorithms. This study introduces a novel approach, the Binary Chaos-based Olympiad Optimisation Algorithm, specifically designed to select the most impactful features from the training dataset. By selecting these influential features for classification, the precision and accuracy of software module classifiers can be notably improved. The study's primary contributions involve devising a binary variant of the chaos-based Olympiad optimisation algorithm to meticulously select effective features and construct an efficient classification model for identifying faulty software modules. Five real-world and standard datasets were utilised across both the training and testing phases of the classifier to evaluate the proposed method's effectiveness. The findings highlight that among the 21 features within the training datasets, specific metrics such as basic complexity, the sum of operators and operands, lines of code, quantity of lines containing code and comments, and the sum of operands have the most significant influence on software defect prediction. This research underscores the combined effectiveness of the proposed method and machine learning algorithms, significantly boosting accuracy (91.13%), precision (92.74%), recall (97.61%), and F1 score (94.26%) in software defect prediction.

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Search for Prioritized Test Cases in Multi-Objective Environment During Web Application Testing

Cited by 13 publications

References 27 publications

A Systematic Literature Review of Test Case Prioritization Using Genetic Algorithms

A Systematic Literature Review of Test Case Prioritization Using Genetic Algorithms

A systematic literature review on software security testing using metaheuristics

A new binary chaos-based metaheuristic algorithm for software defect prediction

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