How well does test case prioritization integrate with statistical fault localization?

ContextEffective test case prioritization shortens the time to detect failures, and yet the use of fewer test cases may compromise the effectiveness of subsequent fault localization.ObjectiveThe paper aims at finding whether several previously identified effectiveness factors of test case prioritization techniques, namely strategy, coverage granularity, and time cost, have observable consequences on the effectiveness of statistical fault localization techniques.MethodThis paper uses a controlled experiment to examine these factors. The experiment includes 16 test case prioritization techniques and four statistical fault localization techniques using the Siemens suite of programs as well as grep, gzip, sed, and flex as subjects. The experiment studies the effects of the percentage of code examined to locate faults from these benchmark subjects after a given number of failures have been observed.ResultsWe find that if testers have a budgetary concern on the number of test cases for regression testing, the use of test case prioritization can save up to 40% of test case executions for commit builds without significantly affecting the effectiveness of fault localization. A statistical fault localization technique using a smaller fraction of a prioritized test suite is found to compromise its effectiveness seriously. Despite the presence of some variations, the inclusion of more failed test cases will generally improve the fault localization effectiveness during the integration process. Interestingly, during the variation periods, adding more failed test cases actually deteriorates the fault localization effectiveness. In terms of strategies, Random is found to be the most effective, followed by the ART and Additional strategies, while the Total strategy is the least effective. We do not observe sufficient empirical evidence to conclude that using different coverage granularity levels have different overall effects.ConclusionThe paper empirically identifies that strategy and time–cost of test case prioritization techniques are key factors affecting the effectiveness of statistical fault localization, while coverage granularity is not a significant factor. It also identifies a mid-range deterioration in fault localization effectiveness when adding more test cases to facilitate debugging.     

Mutation-oriented test data augmentation for GUI software fault localization

ContextFault localization lies at the heart of program debugging and often proceeds by contrasting the statistics of program constructs executed by passing and failing test cases. A vital issue here is how to obtain these “suitable” test cases. Techniques presented in the literature mostly assume the existence of a large test suite a priori. However, developers often encounter situations where a failure occurs, but where no or no appropriate test suite is available for use to localize the fault.ObjectiveThis paper aims to alleviate this key limitation of traditional fault localization techniques for GUI software particularly, namely, it aims at enabling cost-effective fault localization process for GUI software in the described scenario.MethodTo address this scenario, we propose a mutation-oriented test data augmentation technique, which actually is directed by the “similarity” criterion in GUI software’s test case context towards the generation of test suite with excellent fault localization capabilities. More specifically, the technique mainly uses four proposed novel mutation operators to iteratively mutate some failing GUI test cases’ event sequences to derive new test cases potentially useful to localize the specific encountered fault. We then compare the fault localization performance of the test suite generated using this technique with that of an original provided large event-pair adequate test suite on some GUI applications.ResultsThe results indicate that the proposed technique is capable of generating a test suite that has comparable, if not better, fault localization effectiveness to the event-pair adequate test suite, but it is much smaller and it is generated immediately once a failure is encountered by developers.ConclusionIt is concluded that the proposed technique can truly enable quick-start cost-effective fault localization process under the investigated all-too-common scenario, greatly alleviating one key limitation of traditional fault localization techniques and prompting the test–diagnose–repair cycle.     

Improving the effectiveness of test suite reduction for user-session-based testing of web applications

ContextTest suite reduction is the problem of creating and executing a set of test cases that are smaller in size but equivalent in effectiveness to an original test suite. However, reduced suites can still be large and executing all the tests in a reduced test suite can be time consuming.ObjectiveWe propose ordering the tests in a reduced suite to increase its rate of fault detection. The ordered reduced test suite can be executed in time constrained situations, where, even if test execution is stopped early, the best test cases from the reduced suite will already be executed.MethodIn this paper, we present several approaches to order reduced test suites using experimentally verified prioritization criteria for the domain of web applications. We conduct an empirical study with three subject applications and user-session-based test cases to demonstrate how ordered reduced test suites often make a practical contribution. To enable comparison between test suites of different sizes, we develop Mod_APFD_C, a modification of the traditional prioritization effectiveness measure.ResultsWe find that by ordering the reduced suites, we create test suites that are more effective than unordered reduced suites. In each of our subject applications, there is at least one ordered reduced suite that outperforms the best unordered reduced suite and the best prioritized original suite.ConclusionsOur results show that when a tester does not have enough time to execute the entire reduced suite, executing an ordered reduced suite often improves the rate of fault detection. By coupling the underlying system’s characteristics with observations from our study on the criteria that produce the best ordered reduced suites, a tester can order their reduced test suites to obtain increased testing effectiveness.     

Similarity testing for access control

ContextAccess control is among the most important security mechanisms, and XACML is the de facto standard for specifying, storing and deploying access control policies. Since it is critical that enforced policies are correct, policy testing must be performed in an effective way to identify potential security flaws and bugs. In practice, exhaustive testing is impossible due to budget constraints. Therefore the tests need to be prioritized so that resources are focused on their most relevant subset.ObjectiveThis paper tackles the issue of access control test prioritization. It proposes a new approach for access control test prioritization that relies on similarity.MethodThe approach has been applied to several policies and the results have been compared to random prioritization (as a baseline). To assess the different prioritization criteria, we use mutation analysis and compute the mutation scores reached by each criterion. This helps assessing the rate of fault detection.ResultsThe empirical results indicate that our proposed approach is effective and its rate of fault detection is higher than that of random prioritization.ConclusionWe conclude that prioritization of access control test cases can be usefully based on similarity criteria.     

回归测试中的测试用例优先排序技术述评

测试用例优先排序(test case prioritization,简称TCP)问题是回归测试研究中的一个热点.通过设定特定排序准则,对测试用例进行排序以优化其执行次序,旨在最大化排序目标,例如最大化测试用例集的早期缺陷检测速率.TCP问题尤其适用于因测试预算不足以致不能执行完所有测试用例的测试场景.首先对TCP问题进行描述,并依次从源代码、需求和模型这3个角度出发对已有的TCP技术进行分类;然后对一类特殊的TCP问题(即测试资源感知的TCP问题)的已有研究成果进行总结;随后依次总结实证研究中常用的评测指标、评测数据集和缺陷类型对实证研究结论的影响;接着依次介绍TCP技术在一些特定测试领域中的应用,包括组合测试、事件驱动型应用测试、Web服务测试和缺陷定位等;最后对下一步工作进行展望.     

Fault-based test suite prioritization for specification-based testing

Context

Existing test suite prioritization techniques usually rely on code coverage information or historical execution data that serve as indicators for estimating the fault-detecting ability of test cases. Such indicators are primarily empirical in nature and not theoretically driven; hence, they do not necessarily provide sound estimates. Also, these techniques are not applicable when the source code is not available or when the software is tested for the first time.

Objective

We propose and develop the novel notion of fault-based prioritization of test cases which directly utilizes the theoretical knowledge of their fault-detecting ability and the relationships among the test cases and the faults in the prescribed fault model, based on which the test cases are generated.

Method

We demonstrate our approach of fault-based prioritization by applying it to the testing of the implementation of logical expressions against their specifications. We then validate our proposal by an empirical study that evaluates the effectiveness of prioritization techniques using two different metrics.

Results

A theoretically guided fault-based prioritization technique generally outperforms other techniques under study, as assessed by two different metrics. Our empirical results also show that the technique helps to reveal all target faults by executing only about 72% of the prioritized test suite, thereby reducing the effort required in testing.

Conclusions

The fault-based prioritization approach is not only applicable to the instance empirically validated in this paper, but should also be adaptable to other fault-based testing strategies. We also envisage new research directions to be opened up by our work.     

Diversity maximization speedup for localizing faults in single-fault and multi-fault programs

Fault localization is useful for reducing debugging effort. Such techniques require test cases with oracles, which can determine whether a program behaves correctly for every test input. Although most fault localization techniques can localize faults relatively accurately even with a small number of test cases, choosing the right test cases and creating oracles for them are not easy. Test oracle creation is expensive because it can take much manual labeling effort (i.e., effort needed to decide whether the test cases pass or fail). Given a number of test cases to be executed, it is challenging to minimize the number of test cases requiring manual labeling and in the meantime achieve good fault localization accuracy. To address this challenge, this paper presents a novel test case selection strategy based on Diversity Maximization Speedup (Dms). Dms orders a set of unlabeled test cases in a way that maximizes the effectiveness of a fault localization technique. Developers are only expected to label a much smaller number of test cases along this ordering to achieve good fault localization results. We evaluate the performance of Dms on 2 different types of programs, single-fault and multi-fault programs. Our experiments with 411 faults from the Software-artifact Infrastructure Repository show (1) that Dms can help existing fault localization techniques to achieve comparable accuracy with on average 67 and 6 % fewer labeled test cases than previously best test case prioritization techniques for single-fault and multi-fault programs, and (2) that given a labeling budget (i.e., a fixed number of labeled test cases), Dms can help existing fault localization techniques reduce their debugging cost (in terms of the amount of code needed to be inspected to locate faults). We conduct hypothesis test and show that the saving of the debugging cost we achieve for the real C programs are statistically significant.     

State dependency probabilistic model for fault localization

ContextFault localization is an important and expensive activity in software debugging. Previous studies indicated that statistically-based fault-localization techniques are effective in prioritizing the possible faulty statements with relatively low computational complexity, but prior works on statistical analysis have not fully investigated the behavior state information of each program element.ObjectiveThe objective of this paper is to propose an effective fault-localization approach based on the analysis of state dependence information between program elements.MethodIn this paper, state dependency is proposed to describe the control flow dependence between statements with particular states. A state dependency probabilistic model uses path profiles to analyze the state dependency information. Then, a fault-localization approach is proposed to locate faults by differentiating the state dependencies in passed and failed test cases.ResultsWe evaluated the fault-localization effectiveness of our approach based on the experiments on Siemens programs and four UNIX programs. Furthermore, we compared our approach with current state-of-art fault-localization methods such as SOBER, Tarantula, and CP. The experimental results show that, our approach can locate more faults than the other methods in every range on Siemens programs, and the overall efficiency of our approach in the range of 10–30% of analyzed source code is higher than the other methods on UNIX programs.ConclusionOur studies show that our approach consistently outperforms the other evaluated techniques in terms of effectiveness in fault localization on Siemens programs. Moreover, our approach is highly effective in fault localization even when very few test cases are available.     

On the Use of Mutation Faults in Empirical Assessments of Test Case Prioritization Techniques

Regression testing is an important activity in the software life cycle, but it can also be very expensive. To reduce the cost of regression testing, software testers may prioritize their test cases so that those which are more important, by some measure, are run earlier in the regression testing process. One potential goal of test case prioritization techniques is to increase a test suite's rate of fault detection (how quickly, in a run of its test cases, that test suite can detect faults). Previous work has shown that prioritization can improve a test suite's rate of fault detection, but the assessment of prioritization techniques has been limited primarily to hand-seeded faults, largely due to the belief that such faults are more realistic than automatically generated (mutation) faults. A recent empirical study, however, suggests that mutation faults can be representative of real faults and that the use of hand-seeded faults can be problematic for the validity of empirical results focusing on fault detection. We have therefore designed and performed two controlled experiments assessing the ability of prioritization techniques to improve the rate of fault detection of test case prioritization techniques, measured relative to mutation faults. Our results show that prioritization can be effective relative to the faults considered, and they expose ways in which that effectiveness can vary with characteristics of faults and test suites. More importantly, a comparison of our results with those collected using hand-seeded faults reveals several implications for researchers performing empirical studies of test case prioritization techniques in particular and testing techniques in general     

Putting your best tests forward

Test case prioritization orders tests so that they help you meet your testing goals earlier during regression testing. Prioritization techniques can, for example, order tests to achieve coverage at the fastest rate possible, exercise features in order of expected frequency of use, or reveal faults as early as possible. We focus on the last goal, which we describe as "increasing a test suite's rate of fault detection" or the speed with which the test suite reveals faults. A faster fault detection rate during regression testing provides earlier feedback on a system under test, supporting earlier strategic decisions about release schedules and letting engineers begin debugging sooner. Also, if testing time is limited or unexpectedly reduced, prioritization increases the chance that testing resources will have been spent as cost effectively as possible in the available time.     

IPSETFUL: an iterative process of selecting test cases for effective fault localization by exploring concept lattice of program spectra

Fault localization is an important and challenging task during software testing. Among techniques studied in this field, program spectrum based fault localization is a promising approach. To perform spectrum based fault localization, a set of test oracles should be provided, and the effectiveness of fault localization depends highly on the quality of test oracles. Moreover, their effectiveness is usually affected when multiple simultaneous faults are present. Faced with multiple faults it is difficult for developers to determine when to stop the fault localization process. To address these issues, we propose an iterative fault localization process, i.e., an iterative process of selecting test cases for effective fault localization (IPSETFUL), to identify as many faults as possible in the program until the stopping criterion is satisfied. It is performed based on a concept lattice of program spectrum (CLPS) proposed in our previous work. Based on the labeling approach of CLPS, program statements are categorized as dangerous statements, safe statements, and sensitive statements. To identify the faults, developers need to check the dangerous statements. Meantime, developers need to select a set of test cases covering the dangerous or sensitive statements from the original test suite, and a new CLPS is generated for the next iteration. The same process is proceeded in the same way. This iterative process ends until there are no failing tests in the test suite and all statements on the CLPS become safe statements. We conduct an empirical study on several subject programs, and the results show that IPSETFUL can help identifymost of the faults in the program with the given test suite. Moreover, it can save much effort in inspecting unfaulty program statements compared with the existing spectrum based fault localization techniques and the relevant state of the art technique.     

测试用例集约简问题研究及其进展

测试用例集约简问题是软件测试中的关键问题之一,其目的是使用尽可能少的测试用例充分满足给定的测试目标,从而提高测试效率、降低测试成本。在简要介绍了测试用例集约简问题基本概念的基础上,总结了求解该问题的几种主要方法,分析比较了这些方法的效率和特性。随后探讨了与测试用例集约简问题强相关的测试用例集错误检测效率的问题,并研究了测试用例优先级技术。最后指出了测试用例集约简问题的下一步研究方向。     

Prioritizing test cases for regression testing

Test case prioritization techniques schedule test cases for execution in an order that attempts to increase their effectiveness at meeting some performance goal. Various goals are possible; one involves rate of fault detection, a measure of how quickly faults are detected within the testing process. An improved rate of fault detection during testing can provide faster feedback on the system under test and let software engineers begin correcting faults earlier than might otherwise be possible. One application of prioritization techniques involves regression testing, the retesting of software following modifications; in this context, prioritization techniques can take advantage of information gathered about the previous execution of test cases to obtain test case orderings. We describe several techniques for using test execution information to prioritize test cases for regression testing, including: 1) techniques that order test cases based on their total coverage of code components; 2) techniques that order test cases based on their coverage of code components not previously covered; and 3) techniques that order test cases based on their estimated ability to reveal faults in the code components that they cover. We report the results of several experiments in which we applied these techniques to various test suites for various programs and measured the rates of fault detection achieved by the prioritized test suites, comparing those rates to the rates achieved by untreated, randomly ordered, and optimally ordered suites     

回归测试中测试用例优先级技术研究综述

测试用例优先级技术是一种高效实用的回归测试技术,其目的是将不同的测试用例按照其重要程度排序后使用,从而获得更高的回归测试效率。首先介绍了与测试用例优先级相关的研究背景和基本概念,然后对当前的研究工作和进展进行了全面概述,同时比较了各种优先级适用的环境和实验研究结果,最后指出了测试用例优先级中有待解决的问题以及未来的研究方向。     

面向软件错误定位与理解的测试执行离散特征筛选

软件错误定位与错误理解是软件调试过程中的重要步骤,然而调试人员利用基于覆盖分析的软件错误定位获取的可疑度,从高到低静态分析每条程序语句的检查方式,与实际软件调试过程并不相符。为了能够筛选更有助于理解错误根源的测试执行,尤其是致使程序失效的失效执行,帮助调试人员进行动态差异化分析,针对失效执行提出基于高可疑度覆盖率、揭示错误潜力和覆盖语句可疑度离散特征的3种优先级策略,针对成功执行提出加权余弦相似度匹配策略。通过将3种失效执行优先级策略与随机选择在常用错误定位技术中进行实验对比,验证了基于覆盖语句可疑度离散特征的失效执行筛选策略能够对筛选前后的错误理解工作量变化产生更强的积极影响和更弱的消极影响,并能够在相同工作量下理解更多的错误,进而更有助于将错误定位结果应用于错误根源的理解。     

Investigating the penalty reward calculus of software users and its impact on requirements prioritization

ContextThe current requirements engineering techniques for prioritization of software requirements implicitly assume that each user requirement will have an independent and symmetric impact on user satisfaction. For example, it is assumed that implementing a high priority user requirement will positively impact his satisfaction and not implementing a high priority user requirement will negatively impact his satisfaction. Further, the impacts of implementing multiple user requirements on his satisfaction are expected to be additive. But is this always the case?ObjectiveThis paper empirically examines whether the assumptions of symmetric and multiplicative impacts of user requirements on his satisfaction are valid. Further, the study assesses the relative efficacy of 5 methods of requirements prioritization in managing these effects as reflected by the user satisfaction with the prioritized requirement sets.MethodTo test for existence and mitigation of asymmetric effects an adaptation of the widely accepted PRCA (Penalty Reward Contrast Analysis) method was used for 5 requirements prioritization techniques. To test for existence and mitigation of multiplicative effects MHMR (Moderated Hierarchical Multiple Regression) a well-accepted technique for testing interaction effects was used.ResultsBoth asymmetric and multiplicative effects of software requirements on user satisfaction were observed for requirements prioritized using all 5 requirements prioritization methods raising questions about the efficacy of present day requirements prioritization techniques. Further, the results of the experiment led to proposing a new method for requirements prioritization for managing these effects.ConclusionThe study empirically demonstrates the complexities of prioritizing software requirements and calls for a new generation of methods to address them. Understanding and resolving these complexities will enable software providers to conserve resources by enabling them to parsimoniously selecting only those requirements for implementation in the software product that have maximum incremental impact on user satisfaction.     

Test case prioritization: a family of empirical studies

To reduce the cost of regression testing, software testers may prioritize their test cases so that those which are more important, by some measure, are run earlier in the regression testing process. One potential goal of such prioritization is to increase a test suite's rate of fault detection. Previous work reported results of studies that showed that prioritization techniques can significantly improve rate of fault detection. Those studies, however, raised several additional questions: 1) Can prioritization techniques be effective when targeted at specific modified versions; 2) what trade-offs exist between fine granularity and coarse granularity prioritization techniques; 3) can the incorporation of measures of fault proneness into prioritization techniques improve their effectiveness? To address these questions, we have performed several new studies in which we empirically compared prioritization techniques using both controlled experiments and case studies     

基于程序频谱的缺陷定位方法

软件测试是生产可靠软件的重要保障,对测试所发现缺陷的解决可以分为缺陷定位和缺陷修改两个步骤^[1],其中的缺陷定位是最耗时的.通常情况下,测试套件中成功执行的测试用例都占绝大多数,对基于程序频谱的缺陷定位方法,应该具备自主调节成功测试用例覆盖比重的能力,以提高方法的可用性.即,随着语句被成功测试用例覆盖的次数增多,该语句的覆盖次数对怀疑率的贡献度应逐渐减小,成功测试用例数的有效处理能提高缺陷定位方法的效果.基于此,本文提出EPStar（EP*）缺陷定位方法,该方法可以有效调整成功执行用例数的影响,以避免成功用例数量对缺陷定位效果的过度影响,从而提高缺陷定位的准确性,通过实验对比,说明了EP*方法比现有的几种缺陷定位方法具有更高的缺陷定位精度.     

基于条件概率模型的缺陷定位方法

缺陷定位是软件调试的重要阶段,依赖程序频谱信息实现软件缺陷定位,是当前比较行之有效的方法.基于频谱缺陷定位方法应用的前提是,程序频谱和执行结果之间存在的潜在关联.通过经验性分析两者之间的内在关联,借助于统计学的条件概率思想,构建了用以量化分析两者关系强弱的P模型,并基于此提出了基于条件概率的缺陷定位方法.以Siemens套件中的7个程序、Space程序和3个Unix工具程序为基准评测对象,与已有的15种经典缺陷定位方法进行了对比实验.实证研究结果表明,该方法总体上具有更好的缺陷定位效果.     

Experiments with test case prioritization using relevant slices

Software testing and retesting occurs continuously during the software development lifecycle to detect errors as early as possible and to gain confidence that changes to software do not introduce defects. Once developed, test suites are reused and updated frequently, and their sizes grow as software evolves. Due to time and resource constraints, an important goal during regression testing of software is to prioritize the execution of test cases in a suite so as to improve the chances of increasing the rate of fault detection. Prior techniques for test case prioritization are based on the total number of coverage requirements exercised by the test cases. In this paper, we present a new approach to prioritize test cases that takes into account the coverage requirements present in the relevant slices of the outputs of test cases. We have implemented three different heuristics based on our relevant slicing based approach to prioritize test cases and conducted experiments to compare the effectiveness of our techniques with those of the traditional techniques that only account for the total requirement coverage. Our detailed experimental study and results provide interesting insights into the effectiveness of using relevant slices for test case prioritization in terms of ability to achieve high rate of fault detection.