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Generating Test Data for path coverage Based Testing Using Genetic Algorithms 1

1st International Conference on Internet Computing and Information Communications (ICICIC)

作者： Panda, Madhumita Mohapatra, Durga Prasad Seemanta Engn Coll Dept MCA Mayurbhanj 757086 Odisha India Natl Inst Technol Dept Comp Sci & Engn Rourkela 769008 India

ISBN: (纸本)9788132212980;9788132212997

In this paper, we have developed an approach to generate test data for path coverage based testing using genetic algorithm. We have used control flow graph and cyclomatic complexity of the example program to find out the number of feasible paths present in the program and compared it with the actual number of paths covered by genetic algorithm. We have used genetic algorithm for generating test data automatically. We have shown that our algorithm is giving cent percent coverage, successfully covering all feasible paths. In our approach, we have observed that genetic algorithm is much more effective in generating test data within less time period, giving better coverage.

关键词： Control flow graph Cyclomatic complexity Connection matrix Genetic algorithm Test data Fitness function path coverage

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Dynamic Sensor Selection for path coverage 19

Dynamic Sensor Selection for Path Coverage

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20th International Conference on Distributed Computing and Networking (ICDCN)

作者： Shamoun, Simon Abdelzaher, Tarek F. Bar-Noy, Amotz CUNY New York NY 10021 USA Univ Illinois Urbana IL USA

ISBN: (纸本)9781450360944

We study the problem of dynamically selecting sensors to maximize the number of steps covered on the path of a mobile target. The sensors are selected from those already deployed in a network as the target moves. The total number of sensors selected and rounds of selection are limited according to budget constraints. The variables are which sensors to select, when, and how. The best settings depend on how much is known about the target's path, such as its start and end locations and mobility pattern, which is updated as the target moves. We study how coverage is affected by these parameters, and how much such dynamic selection strategies can improve coverage over sensors selected in advance.

关键词： path coverage sensor selection mobility model dynamic selection

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A New Method of Evolutionary Testing For path coverage 18

A New Method of Evolutionary Testing For Path Coverage

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18th IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Li, Yijin Zhang, Yuheng Wei, Qingjie Yunnan Power Grid Co Ltd Informat Technol Ctr Kunming Yunnan Peoples R China Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Coll Software Engn Chongqing Peoples R China

ISBN: (纸本)9781538678398

In order to solve the irrationality of the existing methods, we developed a new method of automatic test data generation for path coverage based on the evolutionary testing. We developed a new method of path coding and built a new objective function for automatic test data generation of multiple path coverage. According to the experiments, we found that our method can improve the convergence rate, stabilization and the coverage rate of target path effectively, especially when the logic structure of program under test is complicated.

关键词： software engineering software testing evolution testing test data generation path coverage genetic algorithm

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A New Mutant Generation Algorithm Based on Basic path coverage for Mutant Reduction 9th

A New Mutant Generation Algorithm Based on Basic Path Covera...

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9th International Workshop on Structured Object-Oriented Formal Language (SOFL)+The Modeling, Simulation and Verification Language (MSVL) for Reliability and Security

作者： Qin, Xu Liu, Shaoying Tao, Zhang Hosei Univ Fac Comp & Informat Sci Tokyo Japan Polytech Univ Sch Software Northwestern Xian Peoples R China

ISBN: (纸本)9783030414184;9783030414177

Mutation testing is a fault-based testing technique that can be used to measure the adequacy of a test set, but its application usually incurs a high cost due to the necessity of generating and executing a great number of mutants. How to reduce the cost still remains a challenge for research. In this paper, we present a new mutant generation algorithm based on a basic path coverage that can help reduce mutants. The algorithm is characterized by implementing a basic path segments identification criterion for determining appropriate program points at which faults are inserted and a mutant generation priority criterion for selecting proper mutant operators to make a fault for insertion. We discuss the algorithm by analysing how the two criteria are realized based on analysing the control flow graph of the program and applying effective mutation operators on the appropriate statements in the relevant path segments. We also present an automated mutation testing tool that supports the proposed approach, and a small experiment to evaluate our tool by comparing it with a traditional mutation testing method on six programs. The result of the experiment suggests that the proposed method can significantly reduce the number of mutants and improve the efficiency of mutation testing.

关键词： Mutation testing path coverage Mutant reduction

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Mutation Methods for Structured Input to Enhance path coverage of Fuzzers 24th

Mutation Methods for Structured Input to Enhance Path Covera...

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24th World International Conference on Information Security Applications (WISA)

作者： Park, Yonggon Ko, Youngjoo Kim, Jong Pohang Univ Sci & Technol POSTECH Dept Comp Sci & Engn Pohang South Korea

ISBN: (纸本)9789819980239;9789819980246

Existing mutation methods used in coverage-based grey-box fuzzing (CGF), such as those employed by AFL and AFL++, can lead to biased testing for structured inputs. While fuzzing, certain input sections of structured input may receive fewer mutations, resulting in less testing of the code that handles those sections, which leads to lower path coverage in those code parts. In this paper, we propose two mutation methods for the structured input to address the unbalanced problem and improve path coverage. The first method, Uniform Mutation, involves conducting additional mutations in input sections that trigger less testing, thereby achieving a more balanced path coverage across the target program. However, this method requires prior knowledge of the input format, which reduces its usability when the format of the target program changes. To overcome the limitation, we propose the second method, Format-agnostic Mutation, which automatically partitions the input into sections based on coverage feedback. This method redistributes the number of mutations and resizes the sections to improve path coverage without knowing the input format. We evaluate the effectiveness of these methods using two real-world programs (Xpdf and libxml2) and compare them with AFL. The experimental results demonstrate that Uniform and Format-agnostic mutations (weight and resizing) outperform AFL regarding path coverage exploration.

关键词： Fuzzing Mutation Structured Input path coverage AFL

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Software Test Case Generation Based on the Fault Propagation path coverage

Software Test Case Generation Based on the Fault Propagation...

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Annual Reliability and Maintainability Symposium

作者： Wang Kun Wang Yichen Beihang Univ Sci & Technol Reliabil & Environm Engn Lab Beijing 100191 Peoples R China

ISBN: (纸本)9781509002498

In the process of software testing, correlated defects raise researchers' attention worldwide. Some potential defects are hard to be detected in the test. To address these potential defects, this paper adopts an error propagation model to describe the process of defect evolution and applies fault injection method to introduce known seed-defects. Subsequently, seed-defects are activated and related potential defects are induced through test case design. This method employs intelligent algorithms to constantly design test cases to cover paths of seed-defects and paths of propagation. As a result, more undetected and correlated defects can be detected in paths of propagation.

关键词： correlated defects fault injection seed-defect fault propagation path path coverage intelligent algorithm

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Towards Linux based safety systems-A statistical approach for software execution path coverage

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JOURNAL OF SYSTEMS ARCHITECTURE 2021年 116卷 102047-102047页

作者： Allende, Imanol Mc Guire, Nicholas Perez, Jon Monsalve, Lisandro G. Obermaisser, Roman Basque Res & Technol Alliance BRTA Ikerlan Technol Res Ctr Arrasate Mondragon Spain OpenTech EDV Res GmbH Bullendorf Austria Univ Siegen Siegen Germany

Several industrial domains are currently developing innovative safety-related autonomous systems characterized by increasing software complexity and high-performance requirements. Due to these features, different research initiatives aim to pave the way towards the usage of Linux for the development of such complex safety-related systems. However, the high execution path variability of the Linux kernel challenges test coverage-based verification, Highly Recommended (HR) technique by safety standards. This research describes a novel statistical analysis method for Linux kernel execution path coverage quantification, which includes the uncertainty estimation of the software execution. The method is applied to a simple, but reproducible case study, and the results of which are analyzed and explained.

关键词： Linux Safety Complex-systems path coverage Uncertainty

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Automated test case generation for path coverage using Hierarchical Surrogate-Assisted Differential Evolution

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APPLIED SOFT COMPUTING 2024年 158卷

作者： Gao, Lin Bai, Songyan Liu, Mingxing Li, Fan Chengdu Univ Informat Technol Ind Coll Blockchain Chengdu 610225 Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Nucl Power Inst China Sci & Technol Reactor Syst Design Technol Lab Chengdu 610213 Sichuan Peoples R China Sichuan Univ Natl Key Lab Fundamental Sci Synthet Vis Chengdu 610065 Sichuan Peoples R China

In the domain of automated test case generation for path coverage (ATCG-PC), commonly used evolutionary optimization algorithms face significant computational overhead due to the necessity of executing the test program for individual fitness evaluations. To address this challenge and enhance efficiency while ensuring comprehensive coverage, this paper proposes a Hierarchical Surrogate-Assisted Differential Evolution (HSADE) approach. HSADE innovatively incorporates two surrogate models: a Global Surrogate Model (GSM) utilizing Gaussian Process Regression (GPR) for a macroscopic view of the fitness landscape, and a Local Surrogate Model (LSM) employing Radial Basis Function Network (RBFN) for precise fitness estimations in targeted subspaces. This hierarchical approach facilitates a two-phase optimization process: an initial coarse search to pinpoint areas of interest followed by a focused, fine-grained search for precision, where LSM is combined with differential evolution (DE) algorithm for fine optimization in these regions. To mitigate the risk of converging to local optima, HSADE employs Random Opposition -Based Learning (ROBL) to enhance the population diversity, leveraging the uncertainty assessment capabilities of the GPR. Our experimental validation, conducted on a diverse set of benchmark programs, demonstrates HSADE's effectiveness in reducing the number of fitness evaluations and computational time required for generating high-quality test cases. Compared to established evolutionary algorithms, HSADE achieves superior path coverage with significantly less computational effort.

关键词： Automated test case generation Differential evolution Hierarchical surrogate model path coverage Random opposition-based learning

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PC-TRT: A Test Case Reuse and generation Tool to achieve high path coverage for Unit Test

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SOFTWAREX 2024年 28卷

作者： Guo, Zhonghao Chen, Sinong Xu, Xinyue Chen, Xiangxian Zhejiang Univ Coll Biomed Engn & Instrument Sci Hangzhou Zhejiang Peoples R China

After software or program updates, it is crucial to establish a new set of test cases. Reusing parts of the old test case set in unit testing is a cost-effective, efficient, and common approach. However, only a few commercial software are utilized for this purpose, and their techniques for reusing test cases are not publicly available. PC-TRT is a test case reuse tool primarily designed for software and programs written in the C language. PCTRT reuses test cases from historical program versions and generates test data for uncovered paths, resulting in a high path coverage test case set. Its key functions include analyzing test case path coverage information, selecting reusable cases from old test case sets based on path similarity, and generating test data for uncovered paths. PC-TRT significantly improves both the efficiency and reliability of software testing.

关键词： Test case reuse path coverage Unit test Test case generation

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Test-data generation directed by program path coverage through imperialist competitive algorithm

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SCIENCE OF COMPUTER PROGRAMMING 2019年 184卷 102304-000页

作者： Saadatjoo, Mohammad Ali Babamir, Seyed Morteza Univ Kashan Dept Comp Engn Kashan Iran

path coverage testing is an approach to ensure all paths of a program from starting node to terminal node are traversed at least once. Such testing considerably helps reveal program faults. However, disregarding iterated paths in loops, any module of a program with n decisions can have up to 2(n) paths. Therefore, finding adequate test data to cover all or most of such paths throughout a program with numerous modules is an NP-Hard problem because it requires an exhaustive search among all possible data. Another concern is determining the efficiency and adequacy of test data according to the coverage criterion. For the path coverage criterion, a test data set is fully efficient if each item of the set covers a separate program path and is fully adequate if the test data cover all program paths. Providing such test data for a program is a very time-consuming action when the program has high complexity (i.e., many paths). A candidate solution for these problems is using Evolutionary Algorithms (EAs). We use an EA named the Imperialist Competitive Algorithm (ICA) to generate test data and assess its effectiveness based on both path coverage and discovered faults. The focus here is on the EA cost function, because it influences the generation of adequate test data. Considering the nondeterministic nature of the EAs in data reproduction, several experiments are carried out by applying statistical tests, ANOVA and T-test to indicate the significant difference between them in producing efficient test data. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Structural testing path coverage Test data generation Evolutionary algorithm (EA) Imperialist Competitive Algorithm (ICA) Mutation testing ANOVA T-test

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