1,720,961 research outputs found
Boundary State Generation for Testing and Improvement of Autonomous Driving Systems
Full text linkRecent advances in Deep Neural Networks (DNNs) and sensor technologies are enabling autonomous driving systems (ADSs) with an ever-increasing level of autonomy. However, assessing their dependability remains a critical concern. State-of-the-art ADS testing approaches modify the controllable attributes of a simulated driving environment until the ADS misbehaves. In such approaches, environment instances in which the ADS is successful are discarded, despite the possibility that they could contain hidden driving conditions in which the ADS may misbehave.
In this paper, we present GENBO (GENerator of BOundary state pairs), a novel test generator for ADS testing. GENBO mutates the driving conditions of the ego vehicle (position, velocity and orientation), collected in a failure-free environment instance, and efficiently generates challenging driving conditions at the behavior boundary (i.e., where the model starts to misbehave) in the same environment instance. We use such boundary conditions to augment the initial training dataset and retrain the DNN model under test. Our evaluation results show that the retrained model has, on average, up to 3x higher success rate on a separate set of evaluation tracks with respect to the original DNN model
Diversity-based web test generation
No full textExisting web test generators derive test paths from a navigational model of the web application, completed with either manually or randomly generated input values. However, manual test data selection is costly, while random generation often results in infeasible input sequences, which are rejected by the application under test. Random and search-based generation can achieve the desired level of model coverage only after a large number of test execution at- tempts, each slowed down by the need to interact with the browser during test execution. In this work, we present a novel web test generation algorithm that pre-selects the most promising candidate test cases based on their diversity from previously generated tests. As such, only the test cases that explore diverse behaviours of the application are considered for in-browser execution. We have implemented our approach in a tool called DIG. Our empirical evaluation on six real-world web applications shows that DIG achieves higher coverage and fault detection rates significantly earlier than crawling-based and search-based web test generators
STILE: A tool for optimizing E2E web test scripts parallelization
No full textWeb applications quality is commonly assessed by executing End-to-End (E2E) test scripts interacting with those systems as a human tester would. To avoid setting up the web application state for each test script, testers usually create test scripts that may depend on others previously executed. However, the presence of dependencies prevents parallelization, a fundamental technique for speedup the execution of large test suites. In this paper, we present STILE, a tool for parallelizing the execution of E2E web test scripts that generates and executes a set of test schedules satisfying two important constraints: (1) every schedule respects existing test dependencies, and (2) all test scripts in the test suite are executed at least once. Moreover, STILE optimizes the execution by running only once the test scripts that are shared among the schedules. We empirically evaluated STILE on eight E2E test suites by comparing the execution time of STILE both with the sequential execution and with the parallel execution based on Selenium Grid. Our results show that STILE can reduce the execution time up to 80% w.r.t. the sequential execution and up to 50% w.r.t. Grid. Moreover, STILE provides a reduction in the CPUs usage (i.e., overall CPU-time) up to 75%
STILE: A Tool for Parallel Execution of E2E Web Test Scripts
No full textAutomated end-to-end (E2E) Web testing relying on frameworks such as Selenium Web Driver is commonly used to assess the quality of web applications. However, the resulting test scripts may require long execution times, due to their interaction with the browser GUI and backend services. To avoid repeated and costly setup of the Web application state, testers tend to build test suites whose test scripts depend on each other (i.e., one test case sets up the application state expected by another test case). In this paper we present Stile, a tool for the parallel execution of Web test scripts that ensures the compliance of all execution schedules with the dependencies among the involved test scripts, while at the same time minimizing the execution time and the computation time required for such parallel execution. Experimental results show that execution times can be approximately halved thanks to Stile
Benchmarking Generative AI Models for Deep Learning Test Input Generation
No full textTest Input Generators (TIGs) are crucial to assess the ability of Deep Learning (DL) image classifiers to provide correct predictions for inputs beyond their training and test sets. Recent advancements in Generative AI(GenAI) models have made them a powerful tool for creating and manipulating synthetic images, although these advancements also imply increased complexity and resource demands for training. In this work, we benchmark and combine different GenAI models with TIGs, assessing their effectiveness, efficiency, and quality of the generated test images, in terms of domain validity and label preservation. We conduct an empirical study involving three different GenAI architectures (VAEs, GANs, Diffusion Models), five classification tasks of increasing complexity, and 364 human evaluations. Our results show that simpler architectures, such as VAEs, are sufficient for less complex datasets like MNIST. However, when dealing with feature-rich datasets, such as ImageNet, more sophisticated architectures like Diffusion Models achieve superior performance by generating a higher number of valid, misclassification-inducing inputs
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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