arXiv subjects

Matthew B. Dwyer

Publications and source records attributed to Matthew B. Dwyer.

26 records · Page 2Linked to original sources

arXiv2023

A Brief Survey on Oracle-based Test Adequacy Metrics

Code coverage is a popular and widespread test adequacy metric that measures the percentage of program codes executed by a test suite. Despite its popularity, code coverage has several limitations. One of the major limitations is that it does not provide any insights into the quality or quantity of test oracles, a core component of testing. Due to this limitation, several studies have suggested that coverage is a poor test adequacy metric; therefore, it should not be used as an indicator of a test suite's fault detection effectiveness. To address this limitation, researchers have proposed extensions to traditional structural code coverage to explicitly consider the quality of test oracles. We refer to these extensions as oracle-based code coverage. This survey paper studies oracle-based coverage techniques published since their inception in 2007. We discuss each metric's definition, methodology, experimental studies, and research findings. Even though oracle-based coverage metrics are proven to be more effective than traditional coverage in detecting faults, they have received little attention in the software engineering community. We present all existing oracle-based adequacy metrics in this paper and compare the critical features against each other. We observe that different oracle-based adequacy metrics operate on different coverage domains and use diverse underlying analysis techniques, enabling a software tester to choose the appropriate metric based on the testing requirements. Our paper provides valuable information regarding the limitations of oracle-based methods, addressing which may help their broader adoption in software testing automation.

cs.SE↗

arXiv2023

PCV: A Point Cloud-Based Network Verifier

3D vision with real-time LiDAR-based point cloud data became a vital part of autonomous system research, especially perception and prediction modules use for object classification, segmentation, and detection. Despite their success, point cloud-based network models are vulnerable to multiple adversarial attacks, where the certain factor of changes in the validation set causes significant performance drop in well-trained networks. Most of the existing verifiers work perfectly on 2D convolution. Due to complex architecture, dimension of hyper-parameter, and 3D convolution, no verifiers can perform the basic layer-wise verification. It is difficult to conclude the robustness of a 3D vision model without performing the verification. Because there will be always corner cases and adversarial input that can compromise the model's effectiveness. In this project, we describe a point cloud-based network verifier that successfully deals state of the art 3D classifier PointNet verifies the robustness by generating adversarial inputs. We have used extracted properties from the trained PointNet and changed certain factors for perturbation input. We calculate the impact on model accuracy versus property factor and can test PointNet network's robustness against a small collection of perturbing input states resulting from adversarial attacks like the suggested hybrid reverse signed attack. The experimental results reveal that the resilience property of PointNet is affected by our hybrid reverse signed perturbation strategy

cs.CV↗

arXiv2022

White-box Testing of NLP models with Mask Neuron Coverage

Recent literature has seen growing interest in using black-box strategies like CheckList for testing the behavior of NLP models. Research on white-box testing has developed a number of methods for evaluating how thoroughly the internal behavior of deep models is tested, but they are not applicable to NLP models. We propose a set of white-box testing methods that are customized for transformer-based NLP models. These include Mask Neuron Coverage (MNCOVER) that measures how thoroughly the attention layers in models are exercised during testing. We show that MNCOVER can refine testing suites generated by CheckList by substantially reduce them in size, for more than 60\% on average, while retaining failing tests -- thereby concentrating the fault detection power of the test suite. Further we show how MNCOVER can be used to guide CheckList input generation, evaluate alternative NLP testing methods, and drive data augmentation to improve accuracy.

cs.CL↗

arXiv2021

DNNV: A Framework for Deep Neural Network Verification

Despite the large number of sophisticated deep neural network (DNN) verification algorithms, DNN verifier developers, users, and researchers still face several challenges. First, verifier developers must contend with the rapidly changing DNN field to support new DNN operations and property types. Second, verifier users have the burden of selecting a verifier input format to specify their problem. Due to the many input formats, this decision can greatly restrict the verifiers that a user may run. Finally, researchers face difficulties in re-using benchmarks to evaluate and compare verifiers, due to the large number of input formats required to run different verifiers. Existing benchmarks are rarely in formats supported by verifiers other than the one for which the benchmark was introduced. In this work we present DNNV, a framework for reducing the burden on DNN verifier researchers, developers, and users. DNNV standardizes input and output formats, includes a simple yet expressive DSL for specifying DNN properties, and provides powerful simplification and reduction operations to facilitate the application, development, and comparison of DNN verifiers. We show how DNNV increases the support of verifiers for existing benchmarks from 30% to 74%.

cs.LG↗

arXiv2021

Distribution-Aware Testing of Neural Networks Using Generative Models

The reliability of software that has a Deep Neural Network (DNN) as a component is urgently important today given the increasing number of critical applications being deployed with DNNs. The need for reliability raises a need for rigorous testing of the safety and trustworthiness of these systems. In the last few years, there have been a number of research efforts focused on testing DNNs. However the test generation techniques proposed so far lack a check to determine whether the test inputs they are generating are valid, and thus invalid inputs are produced. To illustrate this situation, we explored three recent DNN testing techniques. Using deep generative model based input validation, we show that all the three techniques generate significant number of invalid test inputs. We further analyzed the test coverage achieved by the test inputs generated by the DNN testing techniques and showed how invalid test inputs can falsely inflate test coverage metrics. To overcome the inclusion of invalid inputs in testing, we propose a technique to incorporate the valid input space of the DNN model under test in the test generation process. Our technique uses a deep generative model-based algorithm to generate only valid inputs. Results of our empirical studies show that our technique is effective in eliminating invalid tests and boosting the number of valid test inputs generated.

cs.SE↗

arXiv2020

Optimal Runtime Verification of Finite State Properties over Lossy Event Streams

Monitoring programs for finite state properties is challenging due to high memory and execution time overheads it incurs. Some events if skipped or lost naturally can reduce both overheads, but lead to uncertainty about the current monitor state. In this work, we present a theoretical framework to model these lossy event streams and provide a construction for a monitor which observes them without producing false positives. The constructed monitor is optimally sound among all complete monitors. We model several loss types of practical relevance using our framework and provide construction of smaller approximate monitors for properties with a large number of states.

cs.LO↗

arXiv2019

Refactoring Neural Networks for Verification

Deep neural networks (DNN) are growing in capability and applicability. Their effectiveness has led to their use in safety critical and autonomous systems, yet there is a dearth of cost-effective methods available for reasoning about the behavior of a DNN. In this paper, we seek to expand the applicability and scalability of existing DNN verification techniques through DNN refactoring. A DNN refactoring defines (a) the transformation of the DNN's architecture, i.e., the number and size of its layers, and (b) the distillation of the learned relationships between the input features and function outputs of the original to train the transformed network. Unlike with traditional code refactoring, DNN refactoring does not guarantee functional equivalence of the two networks, but rather it aims to preserve the accuracy of the original network while producing a simpler network that is amenable to more efficient property verification. We present an automated framework for DNN refactoring, and demonstrate its potential effectiveness through three case studies on networks used in autonomous systems.

cs.NE↗

arXiv2019

SymInfer: Inferring Program Invariants using Symbolic States

We introduce a new technique for inferring program invariants that uses symbolic states generated by symbolic execution. Symbolic states, which consist of path conditions and constraints on local variables, are a compact description of sets of concrete program states and they can be used for both invariant inference and invariant verification. Our technique uses a counterexample-based algorithm that creates concrete states from symbolic states, infers candidate invariants from concrete states, and then verifies or refutes candidate invariants using symbolic states. The refutation case produces concrete counterexamples that prevent spurious results and allow the technique to obtain more precise invariants. This process stops when the algorithm reaches a stable set of invariants. We present SymInfer, a tool that implements these ideas to automatically generate invariants at arbitrary locations in a Java program. The tool obtains symbolic states from Symbolic PathFinder and uses existing algorithms to infer complex (potentially nonlinear) numerical invariants. Our preliminary results show that SymInfer is effective in using symbolic states to generate precise and useful invariants for proving program safety and analyzing program runtime complexity. We also show that SymInfer outperforms existing invariant generation systems.

cs.SE↗