Testing Autonomous Cars for Feature Interaction Failures using Many-Objective Search

Complex systems such as autonomous cars are typically built as a composition of features that are independent units of functionality. Features tend to interact and impact one another’s behavior in unknown ways. A challenge is to detect and manage feature interactions, in particular, those that violate system requirements, hence leading to failures. In this paper, we propose a technique to detect feature interaction failures by casting our approach into a search-based test generation problem. We define a set of hybrid test objectives (distance functions) that combine traditional coverage-based heuristics with new heuristics specifically aimed at revealing feature interaction failures. We develop a new search-based test generation algorithm, called FITEST, that is guided by our hybrid test objectives. FITEST extends recently proposed many-objective evolutionary algorithms to reduce the time required to compute fitness values. We evaluate our approach using two versions of an industrial self-driving system. Our results show that our hybrid test objectives are able to identify more than twice as many feature interaction failures as two baseline test objectives used in the software testing literature (i.e., coverage-based and failure-based test objectives). Further, the feedback from domain experts indicates that the detected feature interaction failures represent real faults in their systems that were not previously identified based on analysis of the system features and their requirements

Generation and Rendering of Interactive Ground Vegetation for Real-Time Testing and Validation of Computer Vision Algorithms

During the development process of new algorithms for computer vision applications, testing and evaluation in real outdoor environments is time-consuming and often difficult to realize. Thus, the use of artificial testing environments is a flexible and cost-efficient alternative. As a result, the development of new techniques for simulating natural, dynamic environments is essential for real-time virtual reality applications, which are commonly known as Virtual Testbeds. Since the first basic usage of Virtual Testbeds several years ago, the image quality of virtual environments has almost reached a level close to photorealism even in real-time due to new rendering approaches and increasing processing power of current graphics hardware. Because of that, Virtual Testbeds can recently be applied in application areas like computer vision, that strongly rely on realistic scene representations. The realistic rendering of natural outdoor scenes has become increasingly important in many application areas, but computer simulated scenes often differ considerably from real-world environments, especially regarding interactive ground vegetation. In this article, we introduce a novel ground vegetation rendering approach, that is capable of generating large scenes with realistic appearance and excellent performance. Our approach features wind animation, as well as object-to-grass interaction and delivers realistically appearing grass and shrubs at all distances and from all viewing angles. This greatly improves immersion, as well as acceptance, especially in virtual training applications. Nevertheless, the rendered results also fulfill important requirements for the computer vision aspect, like plausible geometry representation of the vegetation, as well as its consistence during the entire simulation. Feature detection and matching algorithms are applied to our approach in localization scenarios of mobile robots in natural outdoor environments. We will show how the quality of computer vision algorithms is influenced by highly detailed, dynamic environments, like observed in unstructured, real-world outdoor scenes with wind and object-to-vegetation interaction

Effective Testing Of Advanced Driver Assistance Systems Using Evolutionary Algorithms And Machine Learning

Improving road safety is a major concern for most car manufacturers. In recent years, the development of Advanced Driver Assistance Systems (ADAS) has subsequently seen a tremendous boost. The development of such systems requires complex testing to ensure vehicle’s safety and reliability. Performing road tests tends to be dangerous, time-consuming, and costly. Hence, a large part of testing for ADAS has to be carried out using physics-based simulation platforms, which are able to emulate a wide range of virtual traffic scenarios and road environments. The main difficulties with simulation-based testing of ADAS are: (1) the test input space is large and multidimensional, (2) simulation platforms provide no guidance to engineers as to which scenarios should be selected for testing, and hence, simulation is limited to a small number of scenarios hand-picked by engineers, and (3) test executions are computationally expensive because they often involve executing high-fidelity mathematical models capturing continuous dynamic behaviors of vehicles and their environment. The complexity of testing ADAS is further exacerbated when many ADAS are employed together in a self-driving system. In particular, when self-driving systems include many ADAS (i.e., features), they tend to interact and impact one another’s behavior in an unknown way and may lead to conflicting situations. The main challenge here is to detect and manage feature interactions, in particular, those that violate system safety requirements, hence leading to critical failures. In practice, once feature interaction failures are detected, engineers need to devise resolution strategies to resolve potential conflicts between features. Developing resolution strategies is a complex task and despite the extensive domain expertise, these resolution strategies can be erroneous and are too complex to be manually repaired. In this dissertation, in addition to testing individual ADAS, we focus on testing self-driving systems that include several ADAS. In this dissertation, we propose a set of approaches based on meta-heuristic search and machine learning techniques to automate ADAS testing and to repair feature interaction failures in self-driving systems. The work presented in this dissertation is motivated by ADAS testing needs at IEE, a world-leading part supplier to the automotive industry. In this dissertation, we focus on the problem of design time testing of ADAS in a simulated environment, relying on Simulink models. The main research contributions in this dissertation are: - A testing approach for ADAS that combines multi-objective search with surrogate models to guide testing towards the most critical behaviors of ADAS, and to explore a larger part of the input search space with less computational resources. - An automated testing algorithm that builds on learnable evolution models and uses classification decision trees to guide the generation of new test scenarios within complex and multidimensional input spaces and help engineers interpret test results. - An automated technique that detects feature interaction failures in the context of self-driving systems based on analyzing executable function models typically developed to specify system behaviors at early development stages. - An automated technique that uses a new many-objective search algorithm to localize and repair errors in the feature interaction resolution rules for self-driving systems

Extracting protein-protein interactions from text using rich feature vectors and feature selection

Because of the intrinsic complexity of natural language, automatically extracting accurate information from text remains a challenge. We have applied rich featurevectors derived from dependency graphs to predict protein-protein interactions using machine learning techniques. We present the first extensive analysis of applyingfeature selection in this domain, and show that it can produce more cost-effective models. For the first time, our technique was also evaluated on several large-scalecross-dataset experiments, which offers a more realistic view on model performance. During benchmarking, we encountered several fundamental problems hindering comparability with other methods. We present a set of practical guidelines to set up ameaningful evaluation. Finally, we have analysed the feature sets from our experiments before and after feature selection, and evaluated the contribution of both lexical and syntacticinformation to our method. The gained insight will be useful to develop better performing methods in this domain

Automatic detection of accommodation steps as an indicator of knowledge maturing

Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool that detects accommodation automatically with the help of machine learning algorithms. We applied a software framework for task detection to the automatic identification of accommodation processes within a wiki. To set up the learning algorithms and test its performance, we conducted an empirical study, in which participants had to contribute to a wiki and, at the same time, identify their own tasks. Two domain experts evaluated the participants’ micro-tasks with regard to accommodation. We then applied an ontology-based task detection approach that identified accommodation with a rate of 79.12%. The potential use of our tool for measuring knowledge maturing online is discussed

What use are formal design and analysis methods to telecommunications services?

Have formal methods failed, or will they fail, to help us solve problems of detecting and resolving of feature interactions in telecommunications software? This paper contains SWOT(Strengths, Weaknesses, Opportunities and Threats) analysis of the use of formula design and analysis methods in feature interaction analysis and makes some suggestions for future research

Understanding Learned Models by Identifying Important Features at the Right Resolution

In many application domains, it is important to characterize how complex learned models make their decisions across the distribution of instances. One way to do this is to identify the features and interactions among them that contribute to a model's predictive accuracy. We present a model-agnostic approach to this task that makes the following specific contributions. Our approach (i) tests feature groups, in addition to base features, and tries to determine the level of resolution at which important features can be determined, (ii) uses hypothesis testing to rigorously assess the effect of each feature on the model's loss, (iii) employs a hierarchical approach to control the false discovery rate when testing feature groups and individual base features for importance, and (iv) uses hypothesis testing to identify important interactions among features and feature groups. We evaluate our approach by analyzing random forest and LSTM neural network models learned in two challenging biomedical applications.Comment: First two authors contributed equally to this work, Accepted for presentation at the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19