Mirror adaptive random testing

Recently, adaptive random testing (ART) has been introduced to improve the fault-detection effectiveness of random testing for non-point types of failure patterns. However, ART requires additional computations to ensure an even spread of test cases, which may render ART less cost-effective than random testing. This paper presents a new technique, namely mirror ART, to reduce these computations. It is an integration of the technique of mirroring and ART. Our simulation results clearly show that mirror ART does improve the cost-effectiveness of ART.     

Distributing test cases more evenly in adaptive random testing

Adaptive random testing (ART) has recently been proposed to enhance the failure-detection capability of random testing. In ART, test cases are not only randomly generated, but also evenly spread over the input domain. Various ART algorithms have been developed to evenly spread test cases in different ways. Previous studies have shown that some ART algorithms prefer to select test cases from the edge part of the input domain rather than from the centre part, that is, inputs do not have equal chance to be selected as test cases. Since we do not know where the failure-causing inputs are prior to testing, it is not desirable for inputs to have different chances of being selected as test cases. Therefore, in this paper, we investigate how to enhance some ART algorithms by offsetting the edge preference, and propose a new family of ART algorithms. A series of simulations have been conducted and it is shown that these new algorithms not only select test cases more evenly, but also have better failure detection capabilities.     

Adaptive random testing based on distribution metrics

Random testing (RT) is a fundamental software testing technique. Adaptive random testing (ART), an enhancement of RT, generally uses fewer test cases than RT to detect the first failure. ART generates test cases in a random manner, together with additional test case selection criteria to enforce that the executed test cases are evenly spread over the input domain. Some studies have been conducted to measure how evenly an ART algorithm can spread its test cases with respect to some distribution metrics. These studies observed that there exists a correlation between the failure detection capability and the evenness of test case distribution. Inspired by this observation, we aim to study whether failure detection capability of ART can be enhanced by using distribution metrics as criteria for the test case selection process. Our simulations and empirical results show that the newly proposed algorithms not only improve the evenness of test case distribution, but also enhance the failure detection capability of ART.     

An experimental study of adaptive testing for software reliability assessment

Adaptive testing is a new form of software testing that is based on the feedback and adaptive control principle and can be treated as the software testing counterpart of adaptive control. Our previous work has shown that adaptive testing can be formulated and guided in theory to minimize the variance of an unbiased software reliability estimator and to achieve optimal software reliability assessment. In this paper, we present an experimental study of adaptive testing for software reliability assessment, where the adaptive testing strategy, the random testing strategy and the operational profile based testing strategy were applied to the Space program in four experiments. The experimental results demonstrate that the adaptive testing strategy can really work in practice and may noticeably outperform the other two. Therefore, the adaptive testing strategy can serve as a preferable alternative to the random testing strategy and the operational profile based testing strategy if high confidence in the reliability estimates is required or the real-world operational profile of the software under test cannot be accurately identified.     

Optimal and adaptive testing for software reliability assessment

Optimal software testing is concerned with how to test software such that the underlying testing goal is achieved in an optimal manner. Our previous work shows that the optimal testing problem for software reliability growth can be treated as closed-loop or feedback control problem, where the software under test serves as a controlled object and the software testing strategy serves as the corresponding controller. More specifically, the software under test is modeled as controlled Markov chains (CMCs) and the control theory of Markov chains is used to synthesize the required optimal testing strategy. In this paper, we show that software reliability assessment can be treated as a feedback control problem and the CMC approach is also applicable to dealing with the optimal testing problem for software reliability assessment. In this problem, the code of the software under test is frozen and the software testing process is optimized in the sense that the variance of the software reliability estimator is minimized. An adaptive software testing strategy is proposed that uses the testing data collected on-line to estimate the required parameters and selects next test cases. Simulation results show that the proposed adaptive software testing strategy can really work in the sense that the resulting variance of the software reliability estimate is much smaller than that resulting from the random testing strategies. The work presented in this paper is a contribution to the new area of software cybernetics that explores the interplay between software and control.     

A robust adaptive tracking control method for a rehabilitative walker using random parameters

The present paper investigates stochastic modelling and a new nonlinear reliable tracking control method for a rehabilitative training walker. The stochastic model is constructed by considering random parameters. A new nonlinear tracking method against actuator fault is proposed based on redundant degree of freedom and a state feedback controller is designed by exploiting an adaptive control technique. It is proved that the mean square of the trajectory tracking error can be made arbitrarily small by choosing appropriate design parameters. As an application, simulation results confirm the effectiveness of the proposed method and verify that the walker with random parameters can provide safe sequential motion when one wheel actuator is at fault.     

Path testing of computer programs with loops using a tool for simple loop patterns

The purpose of this paper is to present a method for testing computer programs with iteration loops. Given such programs, we have shown that for classes of program paths, identified as sequences of simple loop paths, there is a characterizing function called a simple loop pattern. The key idea of simple loop patterns is that these special functions form a base set which can represent any path computation in the given program. A software tool called SILOP has been developed to automatically generate these simple loop patterns, and each corresponding sequence of simple loop paths can be considered as a test case. The tester uses each test case, and with knowledge of the application program, can generate corresponding test data. This paper also presents a method for selecting the specific paths and test data to determine the simple loop pattern reliably. The tester can use this selection method to predict the number of tests required. In order to apply this selection method, the given program must be a linear computer program. The SILOP tool and this test selection method have been applied to commercial software; in this paper, this computational experience is reported and several examples are given to demonstrate the approach.     

Adaptive ILC for a class of discrete-time systems with iteration-varying trajectory and random initial condition

In this work we present a discrete-time adaptive iterative learning control (AILC) scheme to deal with systems with time-varying parametric uncertainties. Using the analogy between the discrete-time axis and the iterative learning axis, the new adaptive ILC can incorporate a Recursive Least Squares (RLS) algorithm, hence the learning gain can be tuned iteratively along the learning axis and pointwisely along the time axis. When the initial states are random and the reference trajectory is iteration-varying, the new AILC can achieve the pointwise convergence over a finite time interval asymptotically along the iterative learning axis.     

Output tracking control for an omnidirectional rehabilitative training walker with incomplete measurements and random parameters

In this study, we propose a model and an output feedback tracking control for an omnidirectional rehabilitative training walker (ODW) with unmeasurable speed, incomplete measurements of position output, and random structural parameters. A stochastic model and an incomplete measurement model were proposed to describe the motion of an ODW subject to random structural parameters and to account for any incomplete data transmission phenomenon caused by possible sensor ageing or failures. A speed observer and a state observer were designed to estimate the unmeasurable speed and the incomplete measurements of position output. Moreover, a dynamic output feedback controller was constructed to ensure the exponential stability in mean square of the tracking error system. Furthermore, the results verify that the choice of appropriate design parameters can result in the mean square of the tracking error becoming arbitrarily small. A simulation example was provided to illustrate the effectiveness of the proposed design procedures.