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A Comprehensive Investigation of Modern test suite optimization Trends, Tools and Techniques

IEEE ACCESS 2019年 7卷 89093-89117页

作者： Kiran, Ayesha Butt, Wasi Haider Anwar, Muhammad Waseem Azam, Farooque Maqbool, Bilal NUST Coll Elect & Mech Engn Dept Comp & Software Engn Islamabad 44000 Pakistan

Software testing is an important but expensive activity of software development life cycle, as it accounts for more than 52% of entire development cost. testing requires the execution of all possible test cases in order to find the defects in the software. Therefore, different test suite optimization approaches like the genetic algorithm and the greedy algorithm, etc., are widely used to select the representative test suite without compromising the effectiveness. test suite optimization is frequently researched to enhance its competences but there is no study published until now that analyzes the latest developments from 2016 to 2019. Hence, in this article, we systematically examine the state-of-the-art optimizations' approaches, tools, and supporting platforms. Principally, we conducted a systematic literature review (SLR) to inspect and examine 58 selected studies that are published during 2016-2019. Subsequently, the selected researches are grouped into five main categories, i.e., greedy algorithm (seven studies), meta-heuristic (28 studies), hybrid (six studies), clustering (five studies), and general (12 studies). Finally, 32 leading tools have been presented, i.e., existing tools (25 tools) and proposed/developed tools (seven tools) along 14 platform supports. Furthermore, it is noted that several approaches aim at solving the single-objective optimization problem. Therefore, researchers should focus on dealing with the multi-objective problem, as multi-objective versions outperform the single-objective ones. Moreover, less attention has been given to clustering-based techniques. Thus, we recommend exploring the machine learning and artificial intelligence-based optimization approaches in the future. A broad exploration of tools and techniques, in this article, will help researchers, practitioners, and developers to opt for adequate techniques, tools, or platforms as per requirements.

关键词： Software testing test suite optimization single objective optimization multi-objective optimization

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Neuro-Fuzzy Modeling for Multi-Objective test suite optimization

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JOURNAL OF INTELLIGENT SYSTEMS 2016年第2期25卷 123-146页

作者： Anwar, Zeeshan Ahsan, Ali Catal, Cagatay Ctr Adv Studies Engn Dept Elect & Comp Engn Islamabad 44000 Pakistan Ctr Adv Studies Engn Dept Engn Management Islamabad 44000 Pakistan Istanbul Kultur Univ Dept Comp Engn TR-34156 Istanbul Turkey

Regression testing is a type of testing activity, which ensures that source code changes do not affect the unmodified portions of the software adversely. This testing activity may be very expensive in, some cases, due to the required time to execute the test suite. In order to execute the regression tests in a cost-effective manner, the optimization of regression test suite is crucial. This optimization can be achieved by applying test suite reduction (TSR), regression test selection (RTS), or test case prioritization (TCP) techniques. In this paper, we designed and implemented an expert system for TSR problem by using neuro-fuzzy modeling-based approaches known as "adaptive neuro-fuzzy inference system with grid partitioning" (ANFIS-GP) and "adaptive neuro-fuzzy inference system with subtractive clustering" (ANFIS-SC). Two case studies were performed to validate the model and fuzzy logic, multi-objective genetic algorithms (MOGAs), non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithms were used for benchmarking. The performance of the models were evaluated in terms of reduction of test suite size, reduction in fault detection rate, reduction in test suite execution time, and reduction in requirement coverage. The experimental results showed that our ANFIS-based optimization system is very effective to optimize the regression test suite and provides better performance than the other approaches evaluated in this study. Size and execution time of the test suite is reduced up to 50%, whereas loss in fault detection rate is between 0% and 25%.

关键词： Regression testing test suite optimization neuro-fuzzy modeling computational intelligence

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An Efficient Regression test suite optimization Approach Using Hybrid Spider Monkey optimization Algorithm

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2021年第4期12卷 57-80页

作者： Agrawal, Arun Prakash Choudhary, Ankur Nand, Parma Sharda Univ Greater Noida India

Regression testing validates the modified software and safeguards against the introduction of new errors during modification. A number of test suite optimization techniques relying on meta-heuristic techniques have been proposed to find the minimal set of test cases to execute for regression purposes. This paper proposes a hybrid spider monkey optimization based regression test suite optimization approach and empirically compares its performance with three other approaches based on bat search, ant colony, and cuckoo search. The authors conducted an empirical study with various subjects retrieved from software artifact infrastructure repository. Fault coverage and execution time of algorithm are used as fitness measures to meet the optimization criteria. Extensive experiments are conducted to evaluate the performance of the proposed approach with other search-based approaches under study using various statistical tests like m-way ANOVA and post hoc tests including odds ratio. Results indicate the superiority of the proposed approach in most of the cases and comparable in others.

关键词： Ant Colony optimization Bat Search Cuckoo Search Regression testing Software Maintenance Spider Monkey optimization test suite optimization

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Application of Search-Based Software Engineering Methodologies for test suite optimization and Evolution in Mission Critical Mobile Application Development 2017

Application of Search-Based Software Engineering Methodologi...

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11th Joint Meeting of European Software Engineering Conference (ESEC) / ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE)

作者： Schuler, Andreas Univ Appl Sci Upper Austria Softwarepk 13 A-4232 Hagenberg Austria

ISBN: (纸本)9781450351058

The demand for high quality mobile applications is constantly rising, especially in mission critical settings. Thus, new software engineering methodologies are needed in order to ensure the desired quality of an application. The research presented proposes a quality assurance methodology for mobile applications through test automation by optimizing test suites. The desired goal is to find a minimal test suite while maintaining efficiency and reducing execution cost. Furthermore to avoid invalidating an optimized test suite as the system under test evolves, the approach further proposes to extract patterns from the applied changes to an application. The evaluation plan comprises a combination of an empirical and an industrial case study based on open source projects and an industrial project in the healthcare domain. It is expected that the presented approach supports the testing process on mobile application platforms.

关键词： test automation test suite optimization mobile application development multi-objective optimization

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Resource-Aware test suite optimization

Resource-Aware Test Suite Optimization

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9th International Conference on Quality Software (QSIC 2009)

作者： Zhang, Xiaofang Shan, Huamao Qian, Ju Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Informat Sci & Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781424459124

Regression testing is an expensive and frequently executed activity, which is often performed in a resource constrained execution environment. As resource-aware test suite optimization problem can be reduced to the 0-1 knapsack problem, which can be efficiently approximated with multiple algorithms. This paper proposes a new resource-aware test suite optimization technique and corresponding metric. The effectiveness of the technique and metric are illustrated by a case study.

关键词： regression testing test suite optimization 0-1 knapsack resource-aware

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Safe Regression test suite optimization: A Review

Safe Regression Test Suite Optimization: A Review

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10th International Conference on Open Source Systems and Technologies (ICOSST)

作者： Haider, Aftab Ali Nadeem, Aamer Akram, Shamaila MAJU Ctr Software Dependabil Islamabad Pakistan UofC Calgary AB Canada

ISBN: (纸本)9781509055869

Systems are frequently regression tested for frequently occurring changes due to corrective, preventive, adaptive or perfective actions. Regression testing is used to prevent the undesired effect of these changes on the previously tested version. Due to these changes, new test cases become part of the test suite making it huge and inefficient for 'retest all' strategy. The ultimate solution of this problem is optimization or reduction of the test suite. Computational intelligence (CI) based approaches like evolutionary computation, fuzzy logic, neural networks and swarm optimization has been used for test suite reduction. optimization approaches reduce the test suite by compromising its safety. Ideally optimization of test suite must guarantee safe reduction. In this work, we have optimized the test suite using some CI based approaches and then analysed the test suite for 'safe reduction'. Safe reduction can be gauged using control flow graphs. test cases of optimal solutions were traversed on these graphs. We found that these solutions partially cover control flow graph. This showed that optimal solutions returned by CI based approaches except fuzzy logic are not safe and will be inadequate for regression testing.

关键词： test suite optimization Multi Objective optimization Computational Intelligence Safety Parameters and Safe Reduction

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On the Fly test suite optimization with FuzzyOptimizer

On the Fly Test Suite Optimization with FuzzyOptimizer

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11th International Conference on Frontiers of Information Technology (FIT)

作者： Haider, Aftab Ali Nadeem, Aamer Rafiq, Shahzad MAJU Ctr Software Dependabil Islamabad Pakistan

ISBN: (纸本)9781479922932

There occur frequent requirement changes in software systems even after the software has been developed. Regression testing is continuously performed to identify the undesired affects of these requirement changes on already tested system. test suites grow enormously with these changes due to addition of new test cases for enhanced functionality. optimization of test suite to perform regression testing within the budgetary and time restrictions is ultimate choice for a tester because "Retest all" test suite is un-economical and is not suitable choice. test suite optimization can be either static or on the fly. With on the fly optimization, optimal suite keeps on changing with the requirement changes. On the fly optimization of test suite is preferable option for regression testing. Presently, static test suite optimization approaches exist. We have proposed an application specific, on the fly optimization approach for test suite optimization problem. We have implemented our approach on an academic testing problem. We use fuzzy logic to optimize the test suite with multiple optimization objectives. Our approach has been successful to generate on the fly optimized test suites for changing requirements. In future, we will implement this approach on considerably large sized testing problems.

关键词： regression testing regression suite test suite optimization fuzzy logic multi-objective optimization computational intelligence

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Energy-Directed test suite optimization

Energy-Directed Test Suite Optimization

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2nd International Workshop on Green and Sustainable Software (GREENS)

作者： Li, Ding Sahin, Cagri Clause, James Halfond, William G. J. Univ Southern Calif Dept Comp Sci Los Angeles CA 90089 USA Univ Delaware Comp & Informat Sci Dept Newark DE USA

ISBN: (纸本)9781467362672

Post-deployment, in situ testing and validation techniques have become an important means of ensuring the reliability of mobile and embedded systems. However, these techniques do not take into consideration the amount of energy they consume, which is an issue of paramount concern for systems with limited energy budgets. In this paper we propose a new test suite minimization approach that allows developers to generate energy-efficient, minimized test suites. The approach is based on encoding minimization problems as integer linear programming problems. Our empirical evaluation shows that, compared to traditionally generated minimized test suites, the test suites generated by our approach are equally effective in terms of their test coverage, but can realize energy savings of up to 90 %.

关键词： Energy use testing test suite optimization Post-deployment validation

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Effective Lightweight Software Fault Localization Combined with Fault Context and test suite optimization

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ENGINEERING LETTERS 2024年第4期32卷 835-870页

作者： Saxena, Amol Bhatnagar, Roheet Srivastava, Devesh Kumar Manipal Univ Jaipur Comp Sci & Engn Dept SCSE Jaipur 303007 Rajasthan India Manipal Univ Jaipur Dept Informat Technol SIT Jaipur 303007 Rajasthan India

locating software faults during debugging is crucial yet challenging. Automated techniques, such as spectrum -based fault localization (SBFL), aid developers in efficiently localizing faults by analyzing program execution information and utilizing statistical approaches to rank program entities according to their suspiciousness. SBFL is also known as lightweight fault localization because of its scalability and minimal computational overhead. While essential, there has been limited research on how test suites affect fault localization. In this paper, we show how test suites impact fault localization and how they can be optimized for better results. SBFL techniques have some inherent limitations, especially in diagnosing faults within loop bodies or iteration statements. Additionally, identical suspiciousness levels can result in ties. While SBFL techniques effectively rank faulty program entities among the Top -N suspicious entities, they might not consistently position the faulty entity within the initial few positions. To address these research gaps, this paper proposes a hybrid approach that combines test suite optimization, statement execution frequency, and fault context concepts to enhance the performance of existing SBFL techniques in single fault scenarios. We evaluate our approach using three popular SBFL methods (Ochiai, Jaccard, and DStar) on Siemens benchmarks and four large real -world programs (flex, grep, sed, and space) with their test suites. The results demonstrate a significant enhancement in fault localization performance when applying our proposed approach to existing SBFL methods. For example, when applied to Ochiai, it reduces examined statements by 62.76% and 65.23% on average for the two test suites, respectively. Furthermore, it identifies 52% of faults by examining only 1% or less of the code and locates 60% of faults by analyzing only 0.1% or less of the code in Siemens and four large real -world programs, respectively. Similar improvemen

关键词： fault context program spectrum spectrum- based fault localization statement execution frequency suspiciousness testing and debugging test suite optimization

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A Distinctive Genetic Approach for test-suite optimization

A Distinctive Genetic Approach for Test-Suite Optimization

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International Conference on Soft Computing and Software Engineering (SCSE)

作者： Jeyaprakash, Srividhya Alagarsamy, K. Karpagam Univ Dept Comp Sci Coimbatore 641021 Tamil Nadu India Madurai Kamaraj Univ Dept MCA Ctr Comp Madurai 625021 Tamil Nadu India

Software testing is one of the expensive activities in Software development lifecycle but it used in to assure the quality of the Software. One of the important focuses in software engineering process is lessen the effort of software testing and cut down the cost & time of testing. This paper presents a unified method for reducing the test cases by adopting the hypothesis of Genetic algorithm such as crossover, mutation and crowding distance to elucidate the given problem and results in the minimum set of test cases with the ability to cover all the faults in minimum time. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Software testing test case reduction techniques Genetic Algorithm test suite optimization Software testing Cost reduction.

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