802 research outputs found
PARTICIPAÇÃO SOCIAL, CULTURA POLÍTICA E INDICADORES DE ASSOCIATIVISMO: OS DIRIGENTES DE ENTIDADES SOCIAIS
The central idea of the present discussion is that, the struggle for constructing democracy is located within the civil society, not just within the State. It is understood that entities in associative practices make democratic life more dynamic, once such entities are spaces where there is political and civic socialization; they are open communication channels between the society and the State and are also, spaces for formulating and questioning public politics andpolicies. The present paper has as main focus the political culture and the associativism in the local extent. It aims at exploring the emergence of a civic and political culture, favorable to the establishment of citizenship, and to the construction of democratic practices in the Metropolitan Area of Maringá - Paraná. The political behavior, translated in terms of political culture of leaders of civil society associations, will enable to measure the relations and attitudes concerning the political system
Boundary State Generation for Testing and Improvement of Autonomous Driving Systems
Recent 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. Such approaches have
two main drawbacks: (1) modifications to the simulated environment might not be
easily transferable to the in-field test setting (e.g., changing the road
shape); (2) 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. 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 up to 16 higher success rate on a separate
set of evaluation tracks with respect to the original DNN model
Testing of Deep Reinforcement Learning Agents with Surrogate Models
Deep Reinforcement Learning (DRL) has received a lot of attention from the
research community in recent years. As the technology moves away from game
playing to practical contexts, such as autonomous vehicles and robotics, it is
crucial to evaluate the quality of DRL agents. In this paper, we propose a
search-based approach to test such agents. Our approach, implemented in a tool
called Indago, trains a classifier on failure and non-failure environment
(i.e., pass) configurations resulting from the DRL training process. The
classifier is used at testing time as a surrogate model for the DRL agent
execution in the environment, predicting the extent to which a given
environment configuration induces a failure of the DRL agent under test. The
failure prediction acts as a fitness function, guiding the generation towards
failure environment configurations, while saving computation time by deferring
the execution of the DRL agent in the environment to those configurations that
are more likely to expose failures. Experimental results show that our
search-based approach finds 50% more failures of the DRL agent than
state-of-the-art techniques. Moreover, such failures are, on average, 78% more
diverse; similarly, the behaviors of the DRL agent induced by failure
configurations are 74% more diverse
Assessment of Source Code Obfuscation Techniques
Obfuscation techniques are a general category of software protections widely
adopted to prevent malicious tampering of the code by making applications more
difficult to understand and thus harder to modify. Obfuscation techniques are
divided in code and data obfuscation, depending on the protected asset. While
preliminary empirical studies have been conducted to determine the impact of
code obfuscation, our work aims at assessing the effectiveness and efficiency
in preventing attacks of a specific data obfuscation technique - VarMerge. We
conducted an experiment with student participants performing two attack tasks
on clear and obfuscated versions of two applications written in C. The
experiment showed a significant effect of data obfuscation on both the time
required to complete and the successful attack efficiency. An application with
VarMerge reduces by six times the number of successful attacks per unit of
time. This outcome provides a practical clue that can be used when applying
software protections based on data obfuscation.Comment: Post-print, SCAM 201
Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing
Deep Neural Networks (DNNs) are becoming a crucial component of modern
software systems, but they are prone to fail under conditions that are
different from the ones observed during training (out-of-distribution inputs)
or on inputs that are truly ambiguous, i.e., inputs that admit multiple classes
with nonzero probability in their labels. Recent work proposed DNN supervisors
to detect high-uncertainty inputs before their possible misclassification leads
to any harm. To test and compare the capabilities of DNN supervisors,
researchers proposed test generation techniques, to focus the testing effort on
high-uncertainty inputs that should be recognized as anomalous by supervisors.
However, existing test generators aim to produce out-of-distribution inputs. No
existing model- and supervisor independent technique targets the generation of
truly ambiguous test inputs, i.e., inputs that admit multiple classes according
to expert human judgment.
In this paper, we propose a novel way to generate ambiguous inputs to test
DNN supervisors and used it to empirically compare several existing supervisor
techniques. In particular, we propose AmbiGuess to generate ambiguous samples
for image classification problems. AmbiGuess is based on gradient-guided
sampling in the latent space of a regularized adversarial autoencoder.
Moreover, we conducted what is -- to the best of our knowledge -- the most
extensive comparative study of DNN supervisors, considering their capabilities
to detect 4 distinct types of high-uncertainty inputs, including truly
ambiguous ones. We find that the tested supervisors' capabilities are
complementary: Those best suited to detect true ambiguity perform worse on
invalid, out-of-distribution and adversarial inputs and vice-versa.Comment: Accepted for publication at Springers "Empirical Software
Engineering" (EMSE
Search based path and input data generation for web application testing
Test case generation for web applications aims at ensuring full coverage of the navigation structure. Existing approaches resort to crawling and manual/random input generation, with or without a preliminary construction of the navigation model. However, crawlers might be unable to reach some parts of the web application and random input generation might not receive enough guidance to produce the inputs needed to cover a given path. In this paper, we take advantage of the navigation structure implicitly specified by developers when they write the page objects used for web testing and we define a novel set of genetic operators that support the joint generation of test inputs and feasible navigation paths. On a case study, our tool Subweb was able to achieve higher coverage of the navigation model than crawling based approaches, thanks to its intrinsic ability of generating inputs for feasible paths and of discarding likely infeasible paths
Test oracle assessment and improvement
We introduce a technique for assessing and improving test oracles by reducing the incidence of both false positives and false negatives. We prove that our approach can always result in an increase in the mutual information between the actual and perfect oracles. Our technique combines test case generation to reveal false positives and mutation testing to reveal false negatives. We applied the decision support tool that implements our oracle improvement technique to five real-world subjects. The experimental results show that the fault detection rate of the oracles after improvement increases, on average, by 48.6% (86% over the implicit oracle). Three actual, exposed faults in the studied systems were subsequently confirmed and fixed by the developers
A New Method for Structural Simulation
In this paper structural change is defined and a tool to simulate structural changes is introduced which consists of a new simulation language which allows to deal separately with quantitative changes and structural qualitative changes. Two strategies of structural simulation are described. In the first one, the user defines the possible structures and conditions of change. In this case, the simulation process finds the structural paths through successive structures. In the second strategy, the structures are generated by the simulation process based on the model of creative thinking proposed by Poincare and Hadamard. AI and genetic programming techniques are used to implement the model. A simple example is given to illustrate the method of the second strategy
Using multi-locators to increase the robustness of web test cases
The main reason for the fragility of web test cases is the inability of web element locators to work correctly when the web page DOM evolves. Web elements locators are used in web test cases to identify all the GUI objects to operate upon and eventually to retrieve web page content that is compared against some oracle in order to decide whether the test case has passed or not. Hence, web element locators play an extremely important role in web testing and when a web element locator gets broken developers have to spend substantial time and effort to repair it. While algorithms exist to produce robust web element locators to be used in web test scripts, no algorithm is perfect and different algorithms are exposed to different fragilities when the software evolves. Based on such observation, we propose a new type of locator, named multi-locator, which selects the best locator among a candidate set of locators produced by different algorithms. Such selection is based on a voting procedure that assigns different voting weights to different locator generation algorithms. Experimental results obtained on six web applications, for which a subsequent release was available, show that the multi-locator is more robust than the single locators (about -30% of broken locators w.r.t. the most robust kind of single locator) and that the execution overhead required by the multiple queries done with different locators is negligible (2-3% at most)
Diversifying focused testing for unit testing
Software changes constantly because developers add new features or modifications. This directly affects the effectiveness of the testsuite associated with that software, especially when these new modifications are in a specific area that no test case covers. This paper tackles the problem of generating a high quality test suite to cover repeatedly a given point in a program, with the ultimate goal of exposing faults possibly affecting the given program point. Both search based software testing and constraint solving offer ready, but low quality, solutions to this: ideally a maximally diverse covering test set is required whereas search and constraint solving tend to generate test sets with biased distributions. Our approach, Diversified Focused Testing (DFT), uses a search strategy inspired by GödelTest. We artificially inject parameters into the code branching conditions and use a bi-objective search algorithm to find diverse inputs by perturbing the injected parameters, while keeping the path conditions still satisfiable. Our results demonstrate that our technique, DFT, is able to cover a desired point in the code at least 90% of the time. Moreover, adding diversity improves the bug detection and the mutation killing abilities of the test suites. We show that DFT achieves better results than focused testing, symbolic execution and random testing by achieving from 3% to 70% improvement in mutation score and up to 100% improvement in fault detection across 105 software subjects
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