Subdomain-based test data generation

Abstract Considerable effort is required to test software thoroughly. Even with automated test data generation tools, it is still necessary to evaluate the output of each test case and identify unexpected results. Manual effort can be reduced by restricting the range of inputs testers need to consider to regions that are more likely to reveal faults, thus reducing the number of test cases overall, and therefore reducing the effort needed to create oracles. This article describes and evaluates search-based techniques, using evolution strategies and subset selection, for identifying regions of the input domain (known as subdomains) such that test cases sampled at random from within these regions can be used efficiently to find faults. The fault finding capability of each subdomain is evaluated using mutation analysis, a technique that is based on faults programmers are likely to make. The resulting subdomains kill more mutants than random testing (up to six times as many in one case) with the same number or fewer test cases. Optimised subdomains can be used as a starting point for program analysis and regression testing. They can easily be comprehended by a human test engineer, so may be used to provide information about the software under test and design further highly efficient test suites

Avoiding coincidental correctness in boundary value analysis

In partition analysis we divide the input domain to form subdomains on which the system's behaviour should be uniform. Boundary value analysis produces test inputs near each subdomain's boundaries to find failures caused by incorrect implementation of the boundaries. However, boundary value analysis can be adversely affected by coincidental correctness---the system produces the expected output, but for the wrong reason. This article shows how boundary value analysis can be adapted in order to reduce the likelihood of coincidental correctness. The main contribution is to cases of automated test data generation in which we cannot rely on the expertise of a tester

An error indicator-based adaptive reduced order model for nonlinear structural mechanics -- application to high-pressure turbine blades

The industrial application motivating this work is the fatigue computation of aircraft engines' high-pressure turbine blades. The material model involves nonlinear elastoviscoplastic behavior laws, for which the parameters depend on the temperature. For this application, the temperature loading is not accurately known and can reach values relatively close to the creep temperature: important nonlinear effects occur and the solution strongly depends on the used thermal loading. We consider a nonlinear reduced order model able to compute, in the exploitation phase, the behavior of the blade for a new temperature field loading. The sensitivity of the solution to the temperature makes {the classical unenriched proper orthogonal decomposition method} fail. In this work, we propose a new error indicator, quantifying the error made by the reduced order model in computational complexity independent of the size of the high-fidelity reference model. In our framework, when the {error indicator} becomes larger than a given tolerance, the reduced order model is updated using one time step solution of the high-fidelity reference model. The approach is illustrated on a series of academic test cases and applied on a setting of industrial complexity involving 5 million degrees of freedom, where the whole procedure is computed in parallel with distributed memory

Learning Compositional Visual Concepts with Mutual Consistency

Compositionality of semantic concepts in image synthesis and analysis is appealing as it can help in decomposing known and generatively recomposing unknown data. For instance, we may learn concepts of changing illumination, geometry or albedo of a scene, and try to recombine them to generate physically meaningful, but unseen data for training and testing. In practice however we often do not have samples from the joint concept space available: We may have data on illumination change in one data set and on geometric change in another one without complete overlap. We pose the following question: How can we learn two or more concepts jointly from different data sets with mutual consistency where we do not have samples from the full joint space? We present a novel answer in this paper based on cyclic consistency over multiple concepts, represented individually by generative adversarial networks (GANs). Our method, ConceptGAN, can be understood as a drop in for data augmentation to improve resilience for real world applications. Qualitative and quantitative evaluations demonstrate its efficacy in generating semantically meaningful images, as well as one shot face verification as an example application.Comment: 10 pages, 8 figures, 4 tables, CVPR 201

Expanding an extended finite state machine to aid testability

The problem of testing from an extended finite state machine (EFSM) is complicated by the presence of infeasible paths. This paper considers the problem of expanding an EFSM in order to bypass the infeasible path problem. The approach is developed for the specification language SDL but, in order to aid generality, the rewriting process is broken down into two phases: producing a normal form EFSM (NF-EFSM) from an SDL specification and then expanding this NF-EFSM

Model Reduction for Multiscale Lithium-Ion Battery Simulation

In this contribution we are concerned with efficient model reduction for multiscale problems arising in lithium-ion battery modeling with spatially resolved porous electrodes. We present new results on the application of the reduced basis method to the resulting instationary 3D battery model that involves strong non-linearities due to Buttler-Volmer kinetics. Empirical operator interpolation is used to efficiently deal with this issue. Furthermore, we present the localized reduced basis multiscale method for parabolic problems applied to a thermal model of batteries with resolved porous electrodes. Numerical experiments are given that demonstrate the reduction capabilities of the presented approaches for these real world applications