72,777 research outputs found
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
Deep learning (DL) defines a new data-driven programming paradigm that
constructs the internal system logic of a crafted neuron network through a set
of training data. We have seen wide adoption of DL in many safety-critical
scenarios. However, a plethora of studies have shown that the state-of-the-art
DL systems suffer from various vulnerabilities which can lead to severe
consequences when applied to real-world applications. Currently, the testing
adequacy of a DL system is usually measured by the accuracy of test data.
Considering the limitation of accessible high quality test data, good accuracy
performance on test data can hardly provide confidence to the testing adequacy
and generality of DL systems. Unlike traditional software systems that have
clear and controllable logic and functionality, the lack of interpretability in
a DL system makes system analysis and defect detection difficult, which could
potentially hinder its real-world deployment. In this paper, we propose
DeepGauge, a set of multi-granularity testing criteria for DL systems, which
aims at rendering a multi-faceted portrayal of the testbed. The in-depth
evaluation of our proposed testing criteria is demonstrated on two well-known
datasets, five DL systems, and with four state-of-the-art adversarial attack
techniques against DL. The potential usefulness of DeepGauge sheds light on the
construction of more generic and robust DL systems.Comment: The 33rd IEEE/ACM International Conference on Automated Software
Engineering (ASE 2018
Recommended from our members
Using formal methods to support testing
Formal methods and testing are two important approaches that assist in the development of high quality software. While traditionally these approaches have been seen as rivals, in recent
years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing
Metamodel Instance Generation: A systematic literature review
Modelling and thus metamodelling have become increasingly important in
Software Engineering through the use of Model Driven Engineering. In this paper
we present a systematic literature review of instance generation techniques for
metamodels, i.e. the process of automatically generating models from a given
metamodel. We start by presenting a set of research questions that our review
is intended to answer. We then identify the main topics that are related to
metamodel instance generation techniques, and use these to initiate our
literature search. This search resulted in the identification of 34 key papers
in the area, and each of these is reviewed here and discussed in detail. The
outcome is that we are able to identify a knowledge gap in this field, and we
offer suggestions as to some potential directions for future research.Comment: 25 page
Functional Requirements-Based Automated Testing for Avionics
We propose and demonstrate a method for the reduction of testing effort in
safety-critical software development using DO-178 guidance. We achieve this
through the application of Bounded Model Checking (BMC) to formal low-level
requirements, in order to generate tests automatically that are good enough to
replace existing labor-intensive test writing procedures while maintaining
independence from implementation artefacts. Given that existing manual
processes are often empirical and subjective, we begin by formally defining a
metric, which extends recognized best practice from code coverage analysis
strategies to generate tests that adequately cover the requirements. We then
formulate the automated test generation procedure and apply its prototype in
case studies with industrial partners. In review, the method developed here is
demonstrated to significantly reduce the human effort for the qualification of
software products under DO-178 guidance
Practical Application Of Uml Activity Diagrams For The Generation Of Test Cases
Software testing and debugging represents around one third of total effort in development projects. Different factors which have influence on poor practices of testing have been identified through specific surveys. Amongst several, one of the most important is the lack of efficient methods to
exploit development models for generating test cases. This paper presents a new method for automatically generating a complete set of functional test cases from UML activity diagrams complementing specification of use cases. Test cases are prioritized according to software risk
information. Results from experiences with more than 70 software professionals/experts validate benefits of the method. Participants also confirm its interest and effectiveness for testing needs of industry
QCBA: Postoptimization of Quantitative Attributes in Classifiers based on Association Rules
The need to prediscretize numeric attributes before they can be used in
association rule learning is a source of inefficiencies in the resulting
classifier. This paper describes several new rule tuning steps aiming to
recover information lost in the discretization of numeric (quantitative)
attributes, and a new rule pruning strategy, which further reduces the size of
the classification models. We demonstrate the effectiveness of the proposed
methods on postoptimization of models generated by three state-of-the-art
association rule classification algorithms: Classification based on
Associations (Liu, 1998), Interpretable Decision Sets (Lakkaraju et al, 2016),
and Scalable Bayesian Rule Lists (Yang, 2017). Benchmarks on 22 datasets from
the UCI repository show that the postoptimized models are consistently smaller
-- typically by about 50% -- and have better classification performance on most
datasets
The development of a program analysis environment for Ada
A unit level, Ada software module testing system, called Query Utility Environment for Software Testing of Ada (QUEST/Ada), is described. The project calls for the design and development of a prototype system. QUEST/Ada design began with a definition of the overall system structure and a description of component dependencies. The project team was divided into three groups to resolve the preliminary designs of the parser/scanner: the test data generator, and the test coverage analyzer. The Phase 1 report is a working document from which the system documentation will evolve. It provides history, a guide to report sections, a literature review, the definition of the system structure and high level interfaces, descriptions of the prototype scope, the three major components, and the plan for the remainder of the project. The appendices include specifications, statistics, two papers derived from the current research, a preliminary users' manual, and the proposal and work plan for Phase 2
- …