Automation of software test data generation using genetic algorithm and reinforcement learning

Esnaashari, Mehdi; Damia, Amir Hossein

doi:10.1016/j.eswa.2021.115446

Cited by 43 publications

(8 citation statements)

References 23 publications

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“…Esnaashari and Damia use reinforcement learning to manipulate tests within the population generated by the Genetic Algorithm by modifying their input [27]. Paduraru et al similarly use reinforcement learning to improve the effectiveness of a random testing tool by taking generated input and modifying it to raise its coverage or execution path length [49].…”

Section: System Test Generationmentioning

confidence: 99%

The integration of machine learning into automated test generation: A systematic mapping study

Fontes

Gay

2023

Software Testing Verif & Rel

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Machine learning (ML) may enable effective automated test generation. We characterize emerging research, examining testing practices, researcher goals, ML techniques applied, evaluation, and challenges in this intersection by performing. We perform a systematic mapping study on a sample of 124 publications. ML generates input for system, GUI, unit, performance, and combinatorial testing or improves the performance of existing generation methods. ML is also used to generate test verdicts, property‐based, and expected output oracles. Supervised learning—often based on neural networks—and reinforcement learning—often based on Q‐learning—are common, and some publications also employ unsupervised or semi‐supervised learning. (Semi‐/Un‐)Supervised approaches are evaluated using both traditional testing metrics and ML‐related metrics (e.g., accuracy), while reinforcement learning is often evaluated using testing metrics tied to the reward function. The work‐to‐date shows great promise, but there are open challenges regarding training data, retraining, scalability, evaluation complexity, ML algorithms employed—and how they are applied—benchmarks, and replicability. Our findings can serve as a roadmap and inspiration for researchers in this field.

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Section: System Test Generationmentioning

confidence: 99%

The integration of machine learning into automated test generation: A systematic mapping study

Fontes

Gay

2023

Software Testing Verif & Rel

View full text Add to dashboard Cite

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“…Esnaashari and Damia used a combination of genetic algorithms and reinforcement learning to generate test data. They used reinforcement learning to search locally within the genetic algorithm [25]. Rijwan Khan introduced a method for the automated generation of software test data by combining a genetic algorithm and a cuckoo search algorithm.…”

Section: Adaptive Strategymentioning

confidence: 99%

Automatic Test Data Generation based on the Prime Path Coverage Criterion: A Grouping-based GA Approach

Damia

Esnaashari

Parvizimosaed

2023

Preprint

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Software testing is the process of running an application with the goal of finding bugs and subsequently improving its quality. Software testing, as a key process, plays a role in ensuring the quality of software systems. Testing is currently considered an industry in the field of software. Given that about 40% of the cost of producing any software is spent on testing, creating tools for automatically generating test data will significantly reduce the current costs of software development. This process can be considered an optimization problem, and thus, search algorithms can be used for tackling it. The Genetic Algorithm (GA) is one of the widest algorithms in this field. In this paper, we have proposed a novel GA approach, called Group-based GA (G-GA), which differs from the standard GA algorithm in the following ways. First of all, a new fitness function has been utilized that uses search space information to guide the population. The population is divided into four groups, each of which is updated according to its fitness level. Finally, in the proposed algorithm, the selection operator has been omitted and thus, the algorithm has less complexity and calculations than the standard GA. Also, the proposed algorithm considers a good level of exploration and exploitation at each step. Experiments have shown that the proposed G-GA method, in terms of the convergence speed and the search time, significantly outperforms the basic GA, its variations, PSO, Tabu Search, and Simulated Annealing.

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“…The results suggest that the proposed approach can generate better outcomes 100% in terms of control-flow graph coverage of all linearly independent paths than the traditional fitness function. Esnaashari (2021) [33] presented a structure for generating test cases based on path coverage. They proposed a mimetic algorithm that employs reinforcement learning as a local search approach within a genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

Object-Oriented Test Case Generation Using Teaching Learning-Based Optimization (TLBO) Algorithm

Al-Masri

Al-Sorori

2022

IEEE Access

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Researchers are currently seeking effective methods for automated software testing to reduce time, avoid test case redundancy, and create comprehensive test cases to cover (paths, benches, conditions, and statements). Generating a minimum number of test cases and covering all code paths is challenging in automated test case generation. Therefore, the use of optimization algorithms has become a popular trend for generating test cases to achieve many goals. In this study, we used a teaching-learning-based optimization algorithm to generate the minimum number of test cases. We compared our results with those of other state-of-the-art methods based on the path coverage for ten Java programs. The motive for using this algorithm is to optimize the number of test cases that cover all code paths in the unit test. The results emphasize that the proposed algorithm generates the minimum number of test cases and covers all paths in the code at a full-coverage rate.INDEX TERMS Test suite generation, unit testing, object-oriented test case generation, coverage-based optimization.

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Automation of software test data generation using genetic algorithm and reinforcement learning

Cited by 43 publications

References 23 publications

The integration of machine learning into automated test generation: A systematic mapping study

The integration of machine learning into automated test generation: A systematic mapping study

Automatic Test Data Generation based on the Prime Path Coverage Criterion: A Grouping-based GA Approach

Object-Oriented Test Case Generation Using Teaching Learning-Based Optimization (TLBO) Algorithm

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