A framework for intelligent test data generation

Test data generation using traditional software testing methods generally requires considerable manual effort and generates only a limited number of test cases before the amount of time expanded becomes unacceptably large. A rule-based framework that will automatically generate test data to achieve maximal branch coverage is presented. The design and discovery of rules used to generate meaningful test cases are also described. The rule-based approach allows this framework to be extended to include additional testing requirements and test case generation knowledge.This work was supported in part by George C. Marshall Space Flight Center, NASA/MSFC, AL 35812 (NASA-NCC8-14).     

基于路径引导的回归测试用例集扩增方法

为了全面测试演化软件,回归测试通常需要生成新的测试用例。concolic测试是一种沿着具体执行路径进行符号执行的软件验证技术,通过生成测试数据来执行程序的所有可行路径。回归测试中,由于concolic测试关注于程序本身,没有利用已有测试用例和软件演化信息,导致生成大量无效测试数据,浪费资源和时间。为解决此问题,提出一种基于路径引导的回归测试用例集扩增方法。该方法将目标路径作为引导,根据软件演化信息选择有利于覆盖目标路径的测试用例,利用已有测试用例跳过重叠初始子路径,对后续目标子路径进行concolic测试并生成覆盖目标路径的测试数据。案例分析表明,本文方法相比传统concolic测试,本方法在覆盖程序可行路径的同时,可有效减少concolic测试路径,提高测试数据生成效率。     

基于测试需求的互操作性测试用例生成方法

网络化应用将成为未来软件技术发展的主导模式。为了保证网络化应用之间能够有效地协同工作,必须对其进行互操作性测试。互操作性测试具有测试需求复杂易变、测试用例设计困难的特点,因而需要消耗大量成本。为此,提出一种基于测试需求的互操作性测试用例生成方法。该方法采用模型驱动的测试思想,以测试需求模型描述互操作性测试需求,以状态图描述各被测应用的规格说明,通过两者的结合生成满足测试需求的测试用例。     

Automatic generation of basis test paths using variable length genetic algorithm

Path testing is the strongest coverage criterion in white box testing. Finding target paths is a key challenge in path testing. Genetic algorithms have been successfully used in many software testing activities such as generating test data, selecting test cases and test cases prioritization. In this paper, we introduce a new genetic algorithm for generating test paths. In this algorithm the length of the chromosome varies from iteration to another according to the change in the length of the path. Based on the proposed algorithm, we present a new technique for automatically generating a set of basis test paths which can be used as testing paths in any path testing method. The proposed technique uses a method to verify the independency of the generated paths to be included in the basis set of paths. In addition, this technique employs a method for checking the feasibility of the generated paths. We introduce new definitions for the key concepts of genetic algorithm such as chromosome representation, crossover, mutation, and fitness function to be compatible with path generation. In addition, we present a case study to show the efficiency of our technique. We conducted a set of experiments to evaluate the effectiveness of the proposed path generation technique. The results showed that the proposed technique causes substantial reduction in path generation effort, and that the proposed GA algorithm is effective in test path generation.     

An approach for experimentally evaluating effectiveness and efficiency of coverage criteria for software testing

Experimental work in software testing has generally focused on evaluating the effectiveness and effort requirements of various coverage criteria. The important issue of testing efficiency has not been sufficiently addressed. In this paper, we describe an approach for comparing the effectiveness and efficiency of test coverage criteria using mutation analysis. For each coverage criterion under study, we generate multiple coverage-adequate minimal test suites for a test-program from a test-pool, which are then executed on a set of systematically generated program mutants to obtain the fault data. We demonstrate the applicability of the proposed approach by describing the results of an experiment comparing the three code-based testing criteria, namely, block coverage, branch coverage, and predicate coverage. Our results suggest that there is a trade-off between effectiveness and efficiency of a coverage criterion. Specifically, the predicate coverage criterion was found to be most effective but least efficient whereas the block coverage criterion was most efficient but least effective. We observed high variability in the performance of block test suites whereas branch and predicate test suites were relatively consistent. Overall results suggest that the branch coverage criterion performs consistently with good efficiency and effectiveness, and it appears to be the most viable option for code-based control flow testing.     

An Experimental Evaluation of Data Flow and Mutation Testing

Two experimental comparisons of data flow and mutation testing are presented. These techniques are widely considered to be effective for unit-level software testing, but can only be analytically compared to a limited extent. We compare the techniques by evaluating the effectiveness of test data developed for each. We develop ten independent sets of test data for a number of programs: five to satisfy the mutation criterion and five to satisfy the all-uses data-flow criterion. These test sets are developed using automated tools, in a manner consistent with the way a test engineer might be expected to generate test data in practice. We use these test sets in two separate experiments. First we measure the effectiveness of the test data that was developed for one technique in terms of the other. Second, we investigate the ability of the test sets to find faults. We place a number of faults into each of our subject programs, and measure the number of faults that are detected by the test sets. Our results indicate that while both techniques are effective, mutation-adequate test sets are closer to satisfying the data flow criterion, and detect more faults.     

Achievement of minimized combinatorial test suite for configuration-aware software functional testing using the Cuckoo Search algorithm

ContextSoftware has become an innovative solution nowadays for many applications and methods in science and engineering. Ensuring the quality and correctness of software is challenging because each program has different configurations and input domains. To ensure the quality of software, all possible configurations and input combinations need to be evaluated against their expected outputs. However, this exhaustive test is impractical because of time and resource constraints due to the large domain of input and configurations. Thus, different sampling techniques have been used to sample these input domains and configurations.ObjectiveCombinatorial testing can be used to effectively detect faults in software-under-test. This technique uses combinatorial optimization concepts to systematically minimize the number of test cases by considering the combinations of inputs. This paper proposes a new strategy to generate combinatorial test suite by using Cuckoo Search concepts.MethodCuckoo Search is used in the design and implementation of a strategy to construct optimized combinatorial sets. The strategy consists of different algorithms for construction. These algorithms are combined to serve the Cuckoo Search.ResultsThe efficiency and performance of the new technique were proven through different experiment sets. The effectiveness of the strategy is assessed by applying the generated test suites on a real-world case study for the purpose of functional testing.ConclusionResults show that the generated test suites can detect faults effectively. In addition, the strategy also opens a new direction for the application of Cuckoo Search in the context of software engineering.     

面向云测试的并行测试用例自动生成方法

为实现云计算环境下的高效软件测试,提出一种Web 应用系统功能测试的并行测试用例自动生成方法。该方法首先根据场景流图采用深度优先遍历算法生成并行测试路径,录制组合产生并行测试脚本,并进行参数化处理;然后,使用基于搜索的软件测试(SBST)方法自动生成可经过目标路径的有效测试数据集,脚本与数据耦合形成大量可并行部署的自动化测试用例。为验证方法的有效性设计了自动化云测试原型系统。实验结果表明,该并行测试用例自动生成方法可以高效地为云平台提供测试用例输入,提高测试效率。     

Search-based automated testing of continuous controllers: Framework,tool support,and case studies

ContextTesting and verification of automotive embedded software is a major challenge. Software production in automotive domain comprises three stages: Developing automotive functions as Simulink models, generating code from the models, and deploying the resulting code on hardware devices. Automotive software artifacts are subject to three rounds of testing corresponding to the three production stages: Model-in-the-Loop (MiL), Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) testing.ObjectiveWe study testing of continuous controllers at the Model-in-Loop (MiL) level where both the controller and the environment are represented by models and connected in a closed loop system. These controllers make up a large part of automotive functions, and monitor and control the operating conditions of physical devices.MethodWe identify a set of requirements characterizing the behavior of continuous controllers, and develop a search-based technique based on random search, adaptive random search, hill climbing and simulated annealing algorithms to automatically identify worst-case test scenarios which are utilized to generate test cases for these requirements.ResultsWe evaluated our approach by applying it to an industrial automotive controller (with 443 Simulink blocks) and to a publicly available controller (with 21 Simulink blocks). Our experience shows that automatically generated test cases lead to MiL level simulations indicating potential violations of the system requirements. Further, not only does our approach generate significantly better test cases faster than random test case generation, but it also achieves better results than test scenarios devised by domain experts. Finally, our generated test cases uncover discrepancies between environment models and the real world when they are applied at the Hardware-in-the-Loop (HiL) level.ConclusionWe propose an automated approach to MiL testing of continuous controllers using search. The approach is implemented in a tool and has been successfully applied to a real case study from the automotive domain.     

一种新的数据流覆盖测试数据进化生成方法

数据流覆盖可有效地检测软件中的缺陷与错误.针对该覆盖准则中存在的插装监测开销庞大和测试数据生成效率不高的问题,提出一种新的基于定值-引用对覆盖的测试数据进化生成方法.该方法主要分为两部分,首先,通过约减测试目标来减少插装开销,提出的包含关系算法可找到一个定值—引用对子集,使得覆盖该子集就能保证所有测试目标被覆盖;然后,采用遗传算法为所有测试目标生成测试数据,设计的适应度函数综合考虑个体实际执行的路径与每个测试目标的定义明确路径的匹配程度.将该方法用于8个基准程序的测试数据生成,并与其他方法比较,结果显示其可有效提高程序覆盖率和测试数据生成效率.     

Automated test data generation using a scatter search approach

The techniques for the automatic generation of test cases try to efficiently find a small set of cases that allow a given adequacy criterion to be fulfilled, thus contributing to a reduction in the cost of software testing. In this paper we present and analyze two versions of an approach based on the scatter search metaheuristic technique for the automatic generation of software test cases using a branch coverage adequacy criterion. The first test case generator, called TCSS, uses a diversity property to extend the search of test cases to all branches of the program under test in order to generate test cases that cover these. The second, called TCSS-LS, is an extension of the previous test case generator which combines the diversity property with a local search method that allows the intensification of the search for test cases that cover the difficult branches. We present the results obtained by our generators and carry out a detailed comparison with many other generators, showing a good performance of our approach.     

The effort required by LCSAJ testing: an assessment via a new path generation strategy

In general, LCSAJ testing more thoroughly exercises the control structure of a piece of software than does either statement or branch testing. Despite this, the exceptionally small number of papers which detail experience of using LCSAJ testing clearly indicates that its popularity falls considerably short of that of the other two methods. One factor that has contributed to this situation is the apparent absence of any attempt to assess the effort entailed by LCSAJ testing in practice, thereby precluding any meaningful cost-effectiveness analysis of the method. Such an attempt is reported in this paper.A significant influence on the effort associated with LCSAJ testing is the number of test paths that must be generated to achieve a specified level of cover, and this is determined by the number of such paths that are found to be infeasible. Effort, therefore, is dependent upon the effectiveness of the path generation method/strategy in avoiding infeasible paths. The attempt to assess the effort entailed by LCSAJ testing which is reported here makes use of a new path selection strategy. This strategy seeks to reduce,a priori, the incidence of infeasible paths in the test set that is generated. Details of the strategy are presented, as are the results which were obtained by applying it to a set of subroutines. From the results, a measure of the effort entailed in employing the strategy is deduced. Subsequently, it is argued that features of the strategy enable pertinent comments to be made not only about the effort associated with employing the strategy, but also about the effort entailed by LCSAJ testing in general.     

Using compiler optimization techniques to detect equivalent mutants

Mutation analysis is a software testing technique that requires the tester to generate test data that will find specific, well-defined errors. Mutation testing executes many slightly differing versions, called mutants, of the same program to evaluate the quality of the data used to test the program. Although these mutants are generated and executed efficiently by automated methods, many of the mutants are functionally equivalent to the original program and are not useful for testing. Recognizing and eliminating equivalent mutants is currently done by hand, a time-consuming and arduous task. This problem is currently a major obstacle to the practical application of mutation testing. This paper presents extensions to previous work in detecting equivalent mutants; specifically, algorithms for determining several classes of equivalent mutants are presented, an implementation of these algorithms is discussed, and results from using this implementation are presented. These algorithms are based on data flow analysis and six compiler optimization techniques. Each of these techniques is described together with how they are used to detect equivalent mutants. The design of the tool and some experimental results using it are also presented.     

The Economics of Unit Testing

Conventional wisdom and anecdote suggests that testing takes between 30 to 50% of a project's effort. However testing is not a monolithic activity as it consists of a number of different phases such as unit testing, integration testing and finally system and acceptance test. Unit testing has received a lot of criticism in terms of the amount of time that it is perceived to take and its perceived costs. However it still remains an important verification activity being an effective means to test individual software components for boundary value behavior and ensure that all code has been exercised adequately. We examine the available data from three safety-related, industrial software projects that have made use of unit testing. Using this information we argue that the perceived costs of unit testing may be exaggerated and that the likely benefits in terms of defect detection are quite high in relation to those costs. We also discuss the different issues that have been found applying the technique at different phases of the development and using different methods to generate those tests. We also compare results we have obtained with empirical results from the literature and highlight some possible weakness of research in this area.     

A formal analysis of the subsume relation between software testadequacy criteria

Software test adequacy criteria are rules to determine whether a software system has been adequately tested. A central question in the study of test adequacy criteria is how they relate to fault detecting ability. We identify two idealized software testing scenarios. In the first scenario, which we call prior testing scenario, software testers are provided with an adequacy criterion in addition to the software under test. The knowledge of the adequacy criterion is used to generate test cases. In the second scenario, which we call posterior testing scenario, software testers are not provided with the knowledge of adequacy criterion. The criterion is only used to decide when to stop the generation of test cases. In 1993, Frankl and Weyuker proved that the subsume relation between software test adequacy criteria does not guarantee better fault detecting ability in the prior testing scenario. We investigate the posterior testing scenario and prove that in this scenario the subsume relation does guarantee a better fault detecting ability. Two measures of fault detecting ability will be used, the probability of detecting faults and the expected number of exposed errors     

The cost of data flow testing: an empirical study

A family of test data adequacy criteria employing data-flow information was previously proposed, and a theoretical complexity analysis was performed. The author describes an empirical study to determine the actual cost of using these criteria. The aim is to establish the practical usefulness of these criteria in testing software and provide a basis for predicting the amount of testing needed for a given program. The first goal of the study is to confirm the belief that the family of software testing criteria considered is practical to use. An attempt is made to show that even as the program size increases, the amount of testing, expressed in terms of the number of test cases sufficient to satisfy a given criterion, remains modest. Several ways of evaluating this hypothesis are explored. The second goal is to provide the prospective user of these criteria with a way of predicting the number of test cases that will be needed to satisfy a given criterion for a given program. This provides testers with a basis for selecting the most comprehensive criterion that they can expect to satisfy. Several plausible bases for such a prediction are considered     

一种基于测试需求约简的测试用例集优化方法

测试用例集优化的目标是用尽可能少的测试用例充分满足给定的测试目标.针对给定的测试目标,获得精简的测试需求集有助于提高测试用例集优化的效率和效果.从测试需求约简的角度考虑测试用例集优化,首先给出可以精确描述测试需求间相互关系的测试需求约简模型;基于此模型,提出一种测试需求约简方法,可以获得精简测试需求集,作为测试用例集生成和约简的基础,从而实现测试用例集优化.实验结果表明,测试需求约简有助于获得规模较小的测试用例集,实现系统、科学、有效的测试.     

CMuJava:一个面向Java程序并发变异体生成系统

并发程序由多个共享存储空间并发执行的流程组成.由于流程之间执行次序的不确定性,使得并发软件系统的测试比较困难.变异测试是一种基于故障的软件测试技术,广泛用于评估测试用例集的充分性和测试技术的有效性.将变异测试应用于并发程序的一个关键问题是,如何高效地生成大量的模拟并发故障的变异体集合.给出了一种并发程序的变异测试框架,...     

A transformation‐based approach to testing concurrent programs using UML activity diagrams

Unified Modeling Language (UML) activity diagrams are widely used to model concurrent interaction among multiple objects. In this paper, we propose a transformation‐based approach to generating scenario‐oriented test cases for applications modeled by UML activity diagrams. Using a set of transformation rules, the proposed approach first transforms a UML activity diagram specification into an intermediate representation, from which it then constructs test scenarios with respect to the given concurrency coverage criteria. The approach then finally derives a set of test cases for the constructed test scenarios. The approach resolves the difficulties associated with fork and join concurrency in the UML activity diagram and enables control over the number of the resulting test cases. We further implemented a tool to automate the proposed approach and studied its feasibility and effectiveness using a case study. Experimental results show that the approach can generate test cases on demand to satisfy a given concurrency coverage criterion and can detect up to 76.5% of seeded faults when a weak coverage criterion is used. With the approach, testers can not only schedule the software test process earlier, but can also better allocate the testing resources for testing concurrent applications. Copyright © 2015 John Wiley & Sons, Ltd.