sOrTES: A Supportive Tool for Stochastic Scheduling of Manual Integration Test Cases

Tahvili, Sahar; Pimentel, Rita; Afzal, Wasif; Ahlberg, Marcus; Fornander, Eric; Bohlin, Markus

doi:10.1109/access.2019.2893209

Cited by 8 publications

(6 citation statements)

References 42 publications

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“…We note though that experiments on open-source data are more common, overall, in the community and that we also need industrial cases as a complement. Construct validity is focused on whether a study measures what it intended to measure [6]. The key construct validity threat in this study is concerning the ground truth: can we trust the functional dependencies provided by the engineers?…”

Section: Threats To Validitymentioning

confidence: 99%

“…Previous studies [3][4][5][6][7][8] have demonstrated that understanding the similarities and dependencies between test cases directly impacts test execution efficiency, reducing costs and effort. This knowledge is valuable for optimizing various aspects of testing, including test case selection, prioritization, scheduling, and parallel execution.…”

Section: Background and Related Workmentioning

confidence: 99%

“…An industrial case study at Alstom is conducted, employing selected algorithms from each category. The performance comparison of these categories utilizes a ground truth of functional dependencies between test cases, previously established [6,29]. All three categories focus on extracting pertinent textual information from test artifacts, including input, output, test sequences, and execution logs, among other factors.…”

Section: Text Mining Techniques For Information Extractionmentioning

confidence: 99%

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Comparative Analysis of Text Mining and Clustering Techniques for Assessing Functional Dependency between Manual Test Cases

Tahvili,

Hatvani,

Felderer

et al. 2024

Preprint

Self Cite

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Text mining techniques have garnered increasing interest for qualitative data analysis in software testing, especially with the advent of machine learning for natural language processing. However, the intricate nature of text mining techniques can pose challenges to their applicability, particularly in domains characterized by safety-critical systems. In such contexts, simpler and more transparent solutions may be preferable unless they significantly compromise accuracy. This study explores the trade-off between complexity, effort, accuracy, and utility of text mining and clustering technologies. The study evaluates the performance of different techniques in the context of detecting functional dependencies between manual integration test cases within safety-critical systems. Empirical data from an industrial testing project conducted at ALSTOM Sweden is used to assess the effectiveness of these techniques. The findings shed light on the comparative effectiveness of various text mining and clustering approaches in the specific context of safety-critical systems, providing insights for practitioners and researchers.

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Section: Threats To Validitymentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Text Mining Techniques For Information Extractionmentioning

confidence: 99%

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Comparative Analysis of Text Mining and Clustering Techniques for Assessing Functional Dependency between Manual Test Cases

Tahvili,

Hatvani,

Felderer

et al. 2024

Preprint

Self Cite

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“…This technique outperformed Particle Swarm Optimization (PSO), Local Beam Search (LBS), Greedy algorithm, and Genetic Algorithm (GA). A supportive tool sOrTES is introduced to measure independence and ranking of integration test cases based on execution time and requirements coverage [11]. An approach for ordering JUnit test cases is proposed by using optimization heuristics including the Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO), and Multi-Objective Genetic Algorithm (MOGA) [12].…”

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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“…The modeling and analysis of different issues related to real-time scheduling have been generally performed using informal methods based on simulations and test sequences [11], [14], [21], [23]. In [24], a comparative analysis of different real-time scheduling algorithms is given using only simulations.…”

Section: A Related Workmentioning

confidence: 99%

A Parameterized Formal Model for the Analysis of Preemption-Threshold Scheduling in Real-Time Systems

Hafaiedh¹,

Slimane²

2020

IEEE Access

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Time-Critical systems are both complex and critical. Therefore, thorough verification processes of such systems must be performed, including behavior and timing correctness. These systems are usually designed as a set of several interacting tasks and sharing one or more processors. Several scheduling policies have been proposed to manage the dispatch of these tasks for these shared resources so as to respect their predefined deadlines. In this context, preemption threshold scheduling algorithms have been proposed to improve real-time schedulability by managing preemptiveness of tasks. The advantage of these algorithms highly depends on a proper assignment of a set of scheduling attributes, which are priority and preemption threshold. In this paper, we propose a high-level parameterized formal model for the description, analysis and comparison of preemptive, non-preemptive and threshold-based scheduling policies. This model allows to formally verify a set of timing-constraints, in particular schedulability analysis as well as to provide a set of experimental results based on real-time executions. A stochastic version of our model is also proposed allowing to define probabilistic features and to model stochastic real-time tasks, scheduling mechanisms or energy consumption.INDEX TERMS Formal methods, preemption-threshold scheduling, schedulability analysis, performance analysis.

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sOrTES: A Supportive Tool for Stochastic Scheduling of Manual Integration Test Cases

Cited by 8 publications

References 42 publications

Comparative Analysis of Text Mining and Clustering Techniques for Assessing Functional Dependency between Manual Test Cases

Comparative Analysis of Text Mining and Clustering Techniques for Assessing Functional Dependency between Manual Test Cases

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

A Parameterized Formal Model for the Analysis of Preemption-Threshold Scheduling in Real-Time Systems

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