Formal analysis techniques for gossiping protocols

We give a survey of formal verification techniques that can be used to corroborate existing experimental results for gossiping protocols in a rigorous manner. We present properties of interest for gossiping protocols and discuss how various formal evaluation techniques can be employed to predict them

Improved techniques for software testing based on Markov chain usage models

In statistical testing of software all possible uses of the software, at some level of abstraction, are represented by a statistical model wherein each possible use of the software has an associated probability of occurrence [16]. Test cases are drawn from the sample population of possible uses according to the sample distribution and run against the software under test. Various statistics of interest, such as the estimated failure rate and mean time to failure of the software are computed. The testing performed is evaluated relative to the population of uses to determine whether or not to stop testing. The model used to represent use of the software in this work is a finite state, time homogeneous, discrete parameter, irreducible Markov chain [10]. In a Markov chain usage model the states of use of the software are represented as states in the Markov chain [1,18, 21,22]. User actions are represented as state transitions in the Markov chain. The probability of a user performing a certain action given that the software is in a particular state of use is represented by the associated transition probability in the Markov chain. The usage model always contains two special states, the (Invoke) state and (Terminate) state. The (Invoke) state represents the software prior to invocation and the (Terminate) state represents the software after execution has ceased. All test cases start from the (Invoke,) state and end in the (Terminate) state. Given a Markov chain based usage model it is possible to analytically compute a number of statistics useful for validation of the model, test planning, test monitoring, and evaluation of the software under test. Example statistics include the expected test case length and associated variance, the probability of a state or arc appearing in a test case, the long run probability of the software being in a certain state of use [22]. Test cases are randomly generated from the usage model, i.e., randomly sampled based on the use distribution. These test cases are run against the software and estimates of reliability are computed

A Review of Software Reliability Testing Techniques

In the era of intelligent systems, the safety and reliability of software have received more attention. Software reliability testing is a significant method to ensure reliability, safety and quality of software. The intelligent software technology has not only offered new opportunities but also posed challenges to software reliability technology. The focus of this paper is to explore the software reliability testing technology under the impact of intelligent software technology. In this study, the basic theories of traditional software and intelligent software reliability testing were investigated via related previous works, and a general software reliability testing framework was established. Then, the technologies of software reliability testing were analyzed, including reliability modeling, test case generation, reliability evaluation, testing criteria and testing methods. Finally, the challenges and opportunities of software reliability testing technology were discussed at the end of this paper

Tool support for statistical testing of software components

We describe the "STSC" prototype tool that supports the statistical testing of software components. The tool supports a wide range of operational profiles and test oracles for test case generation and output evaluation. The tool also generates appropriate values for different types of input parameters of operations. STSC automatically generates a test driver from an operational profile. This test driver invokes a test oracle that is implemented as a behaviour-checking version of the implementation. To evaluate the flexibility and usability of the tool, it has been applied to several case studies using different types of operational profiles and test oracles

Threshold Regression for Survival Analysis: Modeling Event Times by a Stochastic Process Reaching a Boundary

Many researchers have investigated first hitting times as models for survival data. First hitting times arise naturally in many types of stochastic processes, ranging from Wiener processes to Markov chains. In a survival context, the state of the underlying process represents the strength of an item or the health of an individual. The item fails or the individual experiences a clinical endpoint when the process reaches an adverse threshold state for the first time. The time scale can be calendar time or some other operational measure of degradation or disease progression. In many applications, the process is latent (i.e., unobservable). Threshold regression refers to first-hitting-time models with regression structures that accommodate covariate data. The parameters of the process, threshold state and time scale may depend on the covariates. This paper reviews aspects of this topic and discusses fruitful avenues for future research.Comment: Published at http://dx.doi.org/10.1214/088342306000000330 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Robust Dynamic Selection of Tested Modules in Software Testing for Maximizing Delivered Reliability

Software testing is aimed to improve the delivered reliability of the users. Delivered reliability is the reliability of using the software after it is delivered to the users. Usually the software consists of many modules. Thus, the delivered reliability is dependent on the operational profile which specifies how the users will use these modules as well as the defect number remaining in each module. Therefore, a good testing policy should take the operational profile into account and dynamically select tested modules according to the current state of the software during the testing process. This paper discusses how to dynamically select tested modules in order to maximize delivered reliability by formulating the selection problem as a dynamic programming problem. As the testing process is performed only once, risk must be considered during the testing process, which is described by the tester's utility function in this paper. Besides, since usually the tester has no accurate estimate of the operational profile, by employing robust optimization technique, we analysis the selection problem in the worst case, given the uncertainty set of operational profile. By numerical examples, we show the necessity of maximizing delivered reliability directly and using robust optimization technique when the tester has no clear idea of the operational profile. Moreover, it is shown that the risk averse behavior of the tester has a major influence on the delivered reliability.Comment: 19 pages, 4 figure