Adopting genetic algorithm to enhance state-sensitivity partitioning

Software testing requires executing software under test with the intention of finding defects as much as possible. Test case generation remains the most dominant research in software testing.The technique used in generating test cases may lead to effective and efficient software testing process.Many techniques have been proposed to generate test cases.One of them is State Sensitivity Partitioning (SSP) technique.The objective of SSP is to avoid exhaustive testing of the entire data states of a module. In SSP,test cases are represented in the form of sequence of events. Even recognizing the finite limits on the size of the queue, there is an infinite set of these sequences and with no upper bound on the length of such a sequence.Thus, a lengthy test sequence might consist of redundant data states. The existence of the redundant data state will increase the size of test suite and consequently the process of testing will be ineffective. Therefore, there is a need to optimize those test cases generated by the SSP in enhancing its effectiveness in detecting faults. Genetic algorithm (GA) has been identified as the most common potential technique among several optimization techniques.Thus, GA is investigated for the integrating with the existing SSP. This paper addresses the issue on how to represent the states produced by SSP sequences of events in order to be accepted by GA.System ID were used for representing the combination of states variables uniquely and generate the GA initial population

Assessing the Risk due to Software Faults: Estimates of Failure Rate versus Evidence of Perfection.

In the debate over the assessment of software reliability (or safety), as applied to critical software, two extreme positions can be discerned: the ‘statistical’ position, which requires that the claims of reliability be supported by statistical inference from realistic testing or operation, and the ‘perfectionist’ position, which requires convincing indications that the software is free from defects. These two positions naturally lead to requiring different kinds of supporting evidence, and actually to stating the dependability requirements in different ways, not allowing any direct comparison. There is often confusion about the relationship between statements about software failure rates and about software correctness, and about which evidence can support either kind of statement. This note clarifies the meaning of the two kinds of statement and how they relate to the probability of failure-free operation, and discusses their practical merits, especially for high required reliability or safety

Design diversity: an update from research on reliability modelling

Diversity between redundant subsystems is, in various forms, a common design approach for improving system dependability. Its value in the case of software-based systems is still controversial. This paper gives an overview of reliability modelling work we carried out in recent projects on design diversity, presented in the context of previous knowledge and practice. These results provide additional insight for decisions in applying diversity and in assessing diverseredundant systems. A general observation is that, just as diversity is a very general design approach, the models of diversity can help conceptual understanding of a range of different situations. We summarise results in the general modelling of common-mode failure, in inference from observed failure data, and in decision-making for diversity in development.

Acceptance Criteria for Critical Software Based on Testability Estimates and Test Results

Testability is defined as the probability that a program will fail a test, conditional on the program containing some fault. In this paper, we show that statements about the testability of a program can be more simply described in terms of assumptions on the probability distribution of the failure intensity of the program. We can thus state general acceptance conditions in clear mathematical terms using Bayesian inference. We develop two scenarios, one for software for which the reliability requirements are that the software must be completely fault-free, and another for requirements stated as an upper bound on the acceptable failure probability

Comparing test sets and criteria in the presence of test hypotheses and fault domains

A number of authors have considered the problem of comparing test sets and criteria. Ideally test sets are compared using a preorder with the property that test set T1 is at least as strong as T2 if whenever T2 determines that an implementation p is faulty, T1 will also determine that p is faulty. This notion can be extended to test criteria. However, it has been noted that very few test sets and criteria are comparable under such an ordering; instead orderings are based on weaker properties such as subsumes. This paper explores an alternative approach, in which comparisons are made in the presence of a test hypothesis or fault domain. This approach allows strong statements about fault detecting ability to be made and yet for a number of test sets and criteria to be comparable. It may also drive incremental test generation

Will My Tests Tell Me If I Break This Code?

Automated tests play an important role in software evolution because they can rapidly detect faults introduced during changes. In practice, code-coverage metrics are often used as criteria to evaluate the effectiveness of test suites with focus on regression faults. However, code coverage only expresses which portion of a system has been executed by tests, but not how effective the tests actually are in detecting regression faults. Our goal was to evaluate the validity of code coverage as a measure for test effectiveness. To do so, we conducted an empirical study in which we applied an extreme mutation testing approach to analyze the tests of open-source projects written in Java. We assessed the ratio of pseudo-tested methods (those tested in a way such that faults would not be detected) to all covered methods and judged their impact on the software project. The results show that the ratio of pseudo-tested methods is acceptable for unit tests but not for system tests (that execute large portions of the whole system). Therefore, we conclude that the coverage metric is only a valid effectiveness indicator for unit tests.Comment: 7 pages, 3 figure

Optimal discrete stopping times for reliability growth tests

Often, the duration of a reliability growth development test is specified in advance and the decision to terminate or continue testing is conducted at discrete time intervals. These features are normally not captured by reliability growth models. This paper adapts a standard reliability growth model to determine the optimal time for which to plan to terminate testing. The underlying stochastic process is developed from an Order Statistic argument with Bayesian inference used to estimate the number of faults within the design and classical inference procedures used to assess the rate of fault detection. Inference procedures within this framework are explored where it is shown the Maximum Likelihood Estimators possess a small bias and converges to the Minimum Variance Unbiased Estimator after few tests for designs with moderate number of faults. It is shown that the Likelihood function can be bimodal when there is conflict between the observed rate of fault detection and the prior distribution describing the number of faults in the design. An illustrative example is provided

Verdict functions in testing with a fault domain or test hypotheses

In state based testing it is common to include verdicts within test cases, the result of the test case being the verdict reached by the test run. In addition, approaches that reason about test effectiveness or produce tests that are guaranteed to find certain classes of faults are often based on either a fault domain or a set of test hypotheses. This paper considers how the presence of a fault domain or test hypotheses affects our notion of a test verdict. The analysis reveals the need for new verdicts that provide more information than the current verdicts and for verdict functions that return a verdict based on a set of test runs rather than a single test run. The concepts are illustrated in the contexts of testing from a non-deterministic finite state machine and the testing of a datatype specified using an algebraic specification language but are potentially relevant whenever fault domains or test hypotheses are used

Squeeziness: An information theoretic measure for avoiding fault masking

Copyright @ 2012 ElsevierFault masking can reduce the effectiveness of a test suite. We propose an information theoretic measure, Squeeziness, as the theoretical basis for avoiding fault masking. We begin by explaining fault masking and the relationship between collisions and fault masking. We then define Squeeziness and demonstrate by experiment that there is a strong correlation between Squeeziness and the likelihood of collisions. We conclude with comments on how Squeeziness could be the foundation for generating test suites that minimise the likelihood of fault masking