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Retrospective on: Constraint-Based Automatic test data generation

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2025年第3期51卷 754-758页

作者： Offutt, Jeff Demillo, Richard Univ Albany Albany NY 12222 USA Georgia Inst Technol Atlanta GA 30332 USA

This article is a retrospective reflection on our paper Constraint-Based Automatic test data generation (DeMillo and Offutt, 1991), which was selected by the editorial board of the IEEE Transactions on Software Engineering as one of the most influential papers of the second decade of the journal. Published in 1991, this paper introduced a novel approach to automated test case generation using symbolic execution and constraint-solving techniques, a fundamental shift in how to approach software testing. The retrospective explores the impact of the paper on the development of mutation testing, the creation of the Mothra toolset, the evolution of test automation practices, and the technical innovations that made the paper a cornerstone of modern testing research. Beyond its technical contributions, the article has become known for its role in fostering a culture of rigorous systems-oriented research that continues to influence both academia and industry. This reflection highlights the enduring relevance of Constraint-Based test Case generation and its role as a foundational work in the field of software engineering.

关键词： Software testing data collection Microwave integrated circuits Codes Software testing Reflection Industries Heuristic algorithms Automation test data generation mutation analysis constraints Mothra

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Search-Based MC/DC test data generation With OCL Constraints

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SOFTWARE testING VERIFICATION & RELIABILITY 2025年第1期35卷

作者： Sartaj, Hassan Iqbal, Muhammad Zohaib Jilani, Atif Aftab Ahmed Khan, Muhammad Uzair Natl Univ Comp & Emerging Sci Dept Comp Sci Islamabad Pakistan Quest Lab Islamabad Pakistan

System-level testing of avionics software systems requires compliance with different international safety standards such as DO-178C. An important consideration of the avionics industry is automated test data generation according to the criteria suggested by safety standards. One of the recommended criteria by DO-178C is the modified condition/decision coverage (MC/DC) criterion. Current model-based test data generation approaches use constraints written in Object Constraint Language (OCL) and apply search techniques to generate test data. These approaches either do not support MC/DC criterion or suffer from performance issues while generating test data for large-scale avionics systems. In this paper, we propose an effective way to automate MC/DC test data generation during model-based testing. We develop a strategy that utilizes case-based reasoning (CBR) and range reduction heuristics designed to solve MC/DC-tailored OCL constraints. We performed an empirical study to compare our proposed strategy for MC/DC test data generation using CBR, range reduction, both CBR and range reduction, with an original search algorithm, and random search. We also empirically compared our strategy with existing constraint-solving approaches. The results show that both CBR and range reduction for MC/DC test data generation outperform the baseline approach. Moreover, the combination of both CBR and range reduction for MC/DC test data generation is an effective approach compared to existing constraint solvers.

关键词： model-based testing (MBT) modified condition/decision coverage (MC/DC) Object Constraint Language (OCL) test data generation

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test data generation for Multiple Paths Based on Local Evolution

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Chinese Journal of Electronics 2015年第1期24卷 46-51页

作者： YAO Xiangjuan GONG Dunwei WANG Wenliang College of Science China University of Mining and Technology School of Information and Electrical Engineering China University of Mining and Technology

Generating test data by genetic algorithms is a promising research direction in software testing, among which path coverage is an important test method. The efficiency of test data generation for multi-path coverage needs to be further improved. We propose a test data generation method for multi-path coverage based on a genetic algorithm with local evolution. The mathematical model is established for all target paths, while in the algorithm the individuals are evolved locally according to different objective functions. We can improve the utilization efficiency of test data. The computation cost can be reduced by using fitness functions of different granularity in different phases of the algorithm.

关键词： test data generation Path coverage Genetic algorithm Local evolution.

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test data generation for Stateful Network Protocol Fuzzing Using a Rule-Based State Machine

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Tsinghua Science and Technology 2016年第3期21卷 352-360页

作者： Rui Ma Daguang Wang Changzhen Hu Wendong Ji Jingfeng Xue Beijing Key Laboratory of Software Security Engineering Technology School of Software Beijing Institute of Technology

To improve the efficiency and coverage of stateful network protocol fuzzing, this paper proposes a new method, using a rule-based state machine and a stateful rule tree to guide the generation of fuzz testing data. The method first builds a rule-based state machine model as a formal description of the states of a network protocol. This removes safety paths, to cut down the scale of the state space. Then it uses a stateful rule tree to describe the relationship between states and messages, and then remove useless items from it. According to the message sequence obtained by the analysis of paths using the stateful rule tree and the protocol specification, an abstract data model of test case generation is defined. The fuzz testing data is produced by various generation algorithms through filling data in the fields of the data model. Using the rule-based state machine and the stateful rule tree, the quantity of test data can be reduced. Experimental results indicate that our method can discover the same vulnerabilities as traditional approaches, using less test data, while optimizing test data generation and improving test efficiency.

关键词： fuzzing stateful network protocol test data generation rule-based state machine stateful rule tree

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test data generation for Path Coverage of MPI Programs Using SAEO

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2021年第2期30卷 p1-37页

作者： Gong, Dunwei Sun, Baicai Yao, Xiangjuan Tian, Tian China Univ Min & Technol Sch Informat & Control Engn 1 Univ Rd Xuzhou 221116 Jiangsu Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol 99 Songling Rd Qingdao 266061 Shandong Peoples R China China Univ Min & Technol Sch Math 1 Univ Rd Xuzhou 221116 Jiangsu Peoples R China Shandong Jianzhu Univ Sch Comp Sci & Technol Fengming Rd Jinan 250101 Shandong Peoples R China

Message-passing interface (MPI) programs, a typical kind of parallel programs, have been commonly used in various applications. However, it generally takes exhaustive computation to run these programs when generating test data to test them. In this article, we propose a method of test data generation for path coverage of MPI programs using surrogate-assisted evolutionary optimization, which can efficiently generate test data with high quality. We first divide a sample set of a program into a number of clusters according to the multi-mode characteristic of the coverage problem, with each cluster training a surrogate model. Then, we estimate the fitness of each individual using one or more surrogate models when generating test data through evolving a population. Finally, a small number of representative individuals are selected to execute the program, with the purpose of obtaining their real fitness, to guide the subsequent evolution of the population. We apply the proposed method to seven benchmark MPI programs and compare it with several state-of-the-art approaches. The experimental results show that the proposed method can generate test data with reduced computation, thus improving the testing efficiency.

关键词： MPI program path coverage test data generation evolutionary optimization surrogate model

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test data generation for Mutation testing Based on Markov Chain Usage Model and Estimation of Distribution Algorithm

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2024年第3期50卷 551-573页

作者： Wei, Changqing Yao, Xiangjuan Gong, Dunwei Liu, Huai China Univ Min & Technol Sch Math Xuzhou 221116 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Shandong Peoples R China Swinburne Univ Technol Dept Comp Technol Melbourne Vic 3122 Australia

Mutation testing, a mainstream fault-based software testing technique, can mimic a wide variety of software faults by seeding them into the target program and resulting in the so-called mutants. test data generated in mutation testing should be able to kill as many mutants as possible, hence guaranteeing a high fault-detection effectiveness of testing. Nevertheless, the test data generation can be very expensive, because mutation testing normally involves an extremely large number of mutants and some mutants are hard to kill. It is thus a critical yet challenging job to find an efficient way to generate a small set of test data that are able to kill multiple mutants at the same time as well as reveal those hard-to-detect faults. In this paper, we propose a new approach for test data generation in mutation testing, through the novel applications of the Markov chain usage model and the estimation of distribution algorithm. We first utilize the Markov chain usage model to reduce the so-called mutant branches in weak mutation testing and generate a minimal set of extended paths. Then, we regard the problem of generating test data as the problem of covering extended paths and use an estimation of distribution algorithm based on probability model to solve the problem. Finally, we develop a framework, TAMMEA, to implement the new approach of generating test data for mutation testing. The empirical studies based on fifteen object programs show that TAMMEA can kill more mutants using fewer test data compared with baseline techniques. In addition, the computation overhead of TAMMEA is lower than that of the baseline technique based on the traditional genetic algorithm, and comparable to that of the random method. It is clear that the new approach improves both the effectiveness and efficiency of mutation testing, thus promoting its practicability.

关键词： testing Markov processes Estimation Software algorithms Genetic algorithms data models Costs Mutation testing weak mutation test data generation Markov chain usage model coverage of extended path estimation of distribution algorithm

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test data generation with a Kalman filter-based adaptive genetic algorithm

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JOURNAL OF SYSTEMS AND SOFTWARE 2015年第C期103卷 343-352页

作者： Aleti, Aldeida Grunske, Lars Monash Univ Fac Informat Technol Clayton Vic 3800 Australia Univ Stuttgart Inst Software Technol Stuttgart Germany

Software testing is a crucial part of software development. It enables quality assurance, such as correctness, completeness and high reliability of the software systems. Current state-of-the-art software testing techniques employ search-based optimisation methods, such as genetic algorithms to handle the difficult and laborious task of test data generation. Despite their general applicability, genetic algorithms have to be parameterised in order to produce results of high quality. Different parameter values may be optimal for different problems and even different problem instances. In this work, we introduce a new approach for generating test data, based on adaptive optimisation. The adaptive optimisation framework uses feedback from the optimisation process to adjust parameter values of a genetic algorithm during the search. Our approach is compared to a state of the art test data optimisation algorithm that does not adapt parameter values online, and a representative adaptive optimisation algorithm, outperforming both methods in a wide range of problems. (C) 2014 Elsevier Inc. All rights reserved.

关键词： test data generation Optimisation Adaptive parameter control

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test data generation method based on multiple convergence direction adaptive PSO

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SOFTWARE QUALITY JOURNAL 2023年第1期31卷 279-303页

作者： Yang, Feng-yu Fan, Yong-jian Xiao, Peng Du, Qing Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn Nanjing 210016 Jiangsu Peoples R China Nanchang Hangkong Univ Sch Software Nanchang 330063 Jiangxi Peoples R China

Automated test data generation is a traditional technique for reducing the cost and time of software testing. Various metaheuristic techniques have been successfully applied for this task. In contrast to the typical metaheuristic algorithms applied for branch and path coverage, this study focused on low resource consumption and efficient information coverage for critical path coverage. First, we combined the characteristics of branch coverage and path coverage to determine a critical path based on quantified path scores. As a result, we constructed a fine-grained fitness function based on the uniform scale branch distance. Second, we proposed an adaptive particle swarm optimization (MCD-APSO) algorithm with multiple convergence directions to accelerate convergence and escape from local optima. The proposed MCD-APSO algorithm improved the global search ability by enriching the diversity of the particle swarm and enhancing the current evolutionary information use of the particles. Finally, to validate the performance of the MCD-APSO algorithm, we compared the proposed algorithm with six test-data generation algorithms on six normal-scale and six large-scale benchmark programs. The results showed that the MCD-APSO algorithm outperforms the benchmark programs regarding the mean number of iterations, total running time, and coverage failure probability.

关键词： test data generation Critical path Multiple convergence direction adaptive particle swarm optimization Fine-grained fitness function

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test data generation for False data Injection Attack testing in Air Traffic Surveillance 13

Test Data Generation for False Data Injection Attack Testing...

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13th IEEE International Conference on Software testing, Verification and Validation (ICST)

作者： Cretin, Aymeric Vernotte, Alexandre Chevrot, Antoine Peureux, Fabien Legeard, Bruno UBFC FEMTO ST Inst CNRS Besancon France UBFC FEMTO ST Inst CNRS Smartesting Solut & Serv Besancon France

ISBN: (数字)9781728110752

ISBN: (纸本)9781728110752

The ADS-B - Automatic Dependent Surveillance Broadcast - technology requires aircraft to broadcast their position and velocity periodically. The protocol was not specified with cyber security in minds and therefore provides no encryption nor identification. These issues, coupled with the reliance on aircraft to communicate on their status, expose air transport to new cyber security threats, and especially to FDIAs - False data Injection Attacks - where an attacker modifies, blocks, or emits fake ADS-B messages to dupe controllers and surveillance systems. This paper is part of an ongoing research initiative toward FDIA test generation intended to improve the detection capabilities of surveillance systems. It focuses on the mechanisms used to alter existing legitimate ADS-B recordings as if an attacker had tempered with the communication flow. We propose a set of alteration algorithms covering the taxonomy of FDIA attacks for ADS-B previously defined in the literature. We experiment this approach by generating test data for an AI-based FDIA detection system [8]. Experimental results show that the proposed approach is straightforward to generate attack situations and provides a efficient way to easily generate sophisticated alterations that were not picked up by the detection system.

关键词： AI testing test data generation false data injection attacks air surveillance

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test data generation for MC/DC Criterion using Reinforcement Learning 13

Test Data Generation for MC/DC Criterion using Reinforcement...

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13th IEEE International Conference on Software testing, Verification and Validation (ICST)

作者： Cegin, Jan Rastocny, Karol Slovak Univ Technol Bratislava Inst Informat Informat Syst & Software Engn Bratislava Slovakia

ISBN: (纸本)9781728110752

Unit testing focused on MC/DC criterion is essential in development of safety-critical systems. However design of test data that meet the MC/DC criterion needs detailed manual analysis of branching in units under test by test engineers. To deal with this problem we propose a new test data generation approach based on reinforcement learning, which utilize analogy with a game, in which a gamer, the test engineer, plays in an environment, a unit under test, and tries to achieve the highest possible reward, MC/DC coverage. We evaluated our approach for two different granularity levels, test suite and test case, and for two different action types allowed to the gamer, discrete and continuous action spaces. Preliminary results shows that the proposed approach could solve path explosion problem of symbolic approaches and that the proposed approach achieves at least comparable results to the current state-of-the-art searchbased test data generation approaches.

关键词： test data generation neural networks unit tests MC/DC criterion

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