Test-Suite Prioritisation by Application Navigation Tree Mining

Muzammal, Muhammad

doi:10.1109/fit.2016.045

Cited by 2 publications

(7 citation statements)

References 20 publications

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“…The utility of the ChainSQL is evident from its business usecases in domains including finance and supplychain and therefore, it offers promising application scenarios for future. Future research on spatial data, dynamics, data analytics, sharding, and verification may be conducted based on the system [21,22,23,24,25,26,27,28,29,30,31].…”

Section: Discussionmentioning

confidence: 99%

A Blockchain Database Application Platform

Muzammal,

Qu,

Nasrulin

et al. 2018

Preprint

Self Cite

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A blockchain is a decentralised linked data structure that is characterised by its inherent resistance to data modification, but it is deficient in search queries, primarily due to its inferior data formatting. A distributed database is also a decentralised data structure which features quick query processing and well-designed data formatting but suffers from data reliability. In this demonstration, we showcase a blockchain database application platform developed by integrating the blockchain with the database, i.e. we demonstrate a system that has the decentralised, distributed and audibility features of the blockchain and quick query processing and well-designed data structure of the distributed databases. The system features a tamper-resistant, consistent and cost-effective multi-active database and an effective and reliable data-level disaster recovery backup. The system is demonstrated in practice as a multi-active database along with the data-level disaster recovery backup feature.

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Section: Discussionmentioning

confidence: 99%

A Blockchain Database Application Platform

Muzammal,

Qu,

Nasrulin

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Faults are seeded or injected through software. In the first step, frequent and rare paths are extracted by sequential pattern mining [13,27] method. Sequence mining is a handy data mining technique for understanding the user and tester behavior with the system [13].…”

Section: Evaluation Performance Evaluationmentioning

confidence: 99%

“…The fundamental challenge of software testing is to improve the order of the test cases for implementation to identify maximum faults in the given test suite [6]. To overcome this challenge, three common solutions are studied in the literature: test suite selection (TSS) [7,8], test suite minimization (TSM) [9,10] and test-suite prioritization (TSP) [1][2][3][4]6,[11][12][13][14][15]. TSP re-orders the test cases to identify more defects in the top few test cases.…”

Section: Introductionmentioning

confidence: 99%

“…There are many approaches used for the test case prioritization, such as data mining approaches [10,13], machine learning approaches [4,12,15,22], clustering approaches [5,26], multiple criteria-based approaches [1], weighted method for test case prioritization [3], and code coverage (CC) based TSP [7,9]. A similar study on the prioritization of test cases is done through data mining techniques called application navigation tree mining and interaction pattern mining [13]. The work is done in different phases; in the first phase, the model creates the tree with the help of user and tester interaction with the system.…”

Section: Introductionmentioning

confidence: 99%

“…Then in the next phase, the sequence mining algorithm identifies the frequent interaction of the user and tester with the system. The third phase [13] identifies the longest common sub-sequence, which shows the most used workflow of the system. So finally, the researcher prioritizes the test cases based on the longest common sub-sequences using data mining.…”

Section: Introductionmentioning

confidence: 99%

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Test Suite Prioritization Based on Optimization Approach Using Reinforcement Learning

et al. 2022

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Regression testing ensures that modified software code changes have not adversely affected existing code modules. The test suite size increases with modification to the software based on the end-user requirements. Regression testing executes the complete test suite after updates in the software. Re-execution of new test cases along with existing test cases is costly. The scientific community has proposed test suite prioritization techniques for selecting and minimizing the test suite to minimize the cost of regression testing. The test suite prioritization goal is to maximize fault detection with minimum test cases. Test suite minimization reduces the test suite size by deleting less critical test cases. In this study, we present a four-fold methodology of test suite prioritization based on reinforcement learning. First, the testers’ and users’ log datasets are prepared using the proposed interaction recording systems for the android application. Second, the proposed reinforcement learning model is used to predict the highest future reward sequence list from the data collected in the first step. Third, the proposed prioritization algorithm signifies the prioritized test suite. Lastly, the fault seeding approach is used to validate the results from software engineering experts. The proposed reinforcement learning-based test suite optimization model is evaluated through five case study applications. The performance evaluation results show that the proposed mechanism performs better than baseline approaches based on random and t-SANT approaches, proving its importance for regression testing.

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Test-Suite Prioritisation by Application Navigation Tree Mining

Cited by 2 publications

References 20 publications

A Blockchain Database Application Platform

A Blockchain Database Application Platform

Test Suite Prioritization Based on Optimization Approach Using Reinforcement Learning

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