Detection of Feature Interactions in Automotive Active Safety Features

With the introduction of software into cars, many functions are now realized with reduced cost, weight and energy. The development of these software systems is done in a distributed manner independently by suppliers, following the traditional approach of the automotive industry, while the car maker takes care of the integration. However, the integration can lead to unexpected and unintended interactions among software systems, a phenomena regarded as feature interaction. This dissertation addresses the problem of the automatic detection of feature interactions for automotive active safety features. Active safety features control the vehicle's motion control systems independently from the driver's request, with the intention of increasing passengers' safety (e.g., by applying hard braking in the case of an identified imminent collision), but their unintended interactions could instead endanger the passengers (e.g., simultaneous throttle increase and sharp narrow steering, causing the vehicle to roll over). My method decomposes the problem into three parts: (I) creation of a definition of feature interactions based on the set of actuators and domain expert knowledge; (II) translation of automotive active safety features designed using a subset of Matlab's Stateflow into the input language of the model checker SMV; (III) analysis using model checking at design time to detect a representation of all feature interactions based on partitioning the counterexamples into equivalence classes. The key novel characteristic of my work is exploiting domain-specific information about the feature interaction problem and the structure of the model to produce a method that finds a representation of all different feature interactions for automotive active safety features at design time. My method is validated by a case study with the set of non-proprietary automotive feature design models I created. The method generates a set of counterexamples that represent the whole set of feature interactions in the case study.By showing only a set of representative feature interaction cases, the information is concise and useful for feature designers. Moreover, by generating these results from feature models designed in Matlab's Stateflow translated into SMV models, the feature designers can trace the counterexamples generated by SMV and understand the results in terms of the Stateflow model. I believe that my results and techniques will have relevance to the solution of the feature interaction problem in other cyber-physical systems, and have a direct impact in assessing the safety of automotive systems

Feature-Aware Verification

A software product line is a set of software products that are distinguished in terms of features (i.e., end-user--visible units of behavior). Feature interactions ---situations in which the combination of features leads to emergent and possibly critical behavior--- are a major source of failures in software product lines. We explore how feature-aware verification can improve the automatic detection of feature interactions in software product lines. Feature-aware verification uses product-line verification techniques and supports the specification of feature properties along with the features in separate and composable units. It integrates the technique of variability encoding to verify a product line without generating and checking a possibly exponential number of feature combinations. We developed the tool suite SPLverifier for feature-aware verification, which is based on standard model-checking technology. We applied it to an e-mail system that incorporates domain knowledge of AT&T. We found that feature interactions can be detected automatically based on specifications that have only feature-local knowledge, and that variability encoding significantly improves the verification performance when proving the absence of interactions.Comment: 12 pages, 9 figures, 1 tabl

A template-based approach for the generation of abstractable and reducible models of featured networks

We investigate the relationship between symmetry reduction and inductive reasoning when applied to model checking networks of featured components. Popular reduction techniques for combatting state space explosion in model checking, like abstraction and symmetry reduction, can only be applied effectively when the natural symmetry of a system is not destroyed during specification. We introduce a property which ensures this is preserved, open symmetry. We describe a template-based approach for the construction of open symmetric Promela specifications of featured systems. For certain systems (safely featured parameterised systems) our generated specifications are suitable for conversion to abstract specifications representing any size of network. This enables feature interaction analysis to be carried out, via model checking and induction, for systems of any number of featured components. In addition, we show how, for any balanced network of components, by using a graphical representation of the features and the process communication structure, a group of permutations of the underlying state space of the generated specification can be determined easily. Due to the open symmetry of our Promela specifications, this group of permutations can be used directly for symmetry reduced model checking. The main contributions of this paper are an automatic method for developing open symmetric specifications which can be used for generic feature interaction analysis, and the novel application of symmetry detection and reduction in the context of model checking featured networks. We apply our techniques to a well known example of a featured network – an email system

Generalising feature interactions in email

We report on a property-based approach to feature interaction analysis for a client-server email system. The model is based upon Hall's email model presented at FIW'00, but the implementation is at a lower level of abstraction, employing non-determinism and asynchronous communication; it is a challenge to avoid deadlock and race conditions. The analysis is more extensive in two ways: interaction analysis is fully automated, based on model-checking the entire state-space, and results are scalable, that is they generalise to email systems consisting of any number of email clients. Abstraction techniques are used to prove general results. The key idea is to model-check a system consisting of a constant number (m) of client processes, in parallel with a mailer process and an ``abstract'' process which represents the product of any number of other (unfeatured, isomorphic) client processes. We give a lower bound for the value of m. All of the models -- for any specified set of client processes and selected features -- are generated automatically using Perl scripts

Symbolic Model Checking of Product-Line Requirements Using SAT-Based Methods

Product line (PL) engineering promotes the de- velopment of families of related products, where individual products are differentiated by which optional features they include. Modelling and analyzing requirements models of PLs allows for early detection and correction of requirements errors – including unintended feature interactions, which are a serious problem in feature-rich systems. A key challenge in analyzing PL requirements is the efficient verification of the product family, given that the number of products is too large to be verified one at a time. Recently, it has been shown how the high-level design of an entire PL, that includes all possible products, can be compactly represented as a single model in the SMV language, and model checked using the NuSMV tool. The implementation in NuSMV uses BDDs, a method that has been outperformed by SAT-based algorithms. In this paper we develop PL model checking using two leading SAT-based symbolic model checking algorithms: IMC and IC3. We describe the algorithms, prove their correctness, and report on our implementation. Evaluating our methods on three PL models from the literature, we demonstrate an improvement of up to 3 orders of magnitude over the existing BDD-based method.NSERC Discovery Grant, 155243-12 || NSERC / Automotive Partnership Canada, APCPJ 386797 - 09 || Ontario Research Fund, RE05-04

False News On Social Media: A Data-Driven Survey

In the past few years, the research community has dedicated growing interest to the issue of false news circulating on social networks. The widespread attention on detecting and characterizing false news has been motivated by considerable backlashes of this threat against the real world. As a matter of fact, social media platforms exhibit peculiar characteristics, with respect to traditional news outlets, which have been particularly favorable to the proliferation of deceptive information. They also present unique challenges for all kind of potential interventions on the subject. As this issue becomes of global concern, it is also gaining more attention in academia. The aim of this survey is to offer a comprehensive study on the recent advances in terms of detection, characterization and mitigation of false news that propagate on social media, as well as the challenges and the open questions that await future research on the field. We use a data-driven approach, focusing on a classification of the features that are used in each study to characterize false information and on the datasets used for instructing classification methods. At the end of the survey, we highlight emerging approaches that look most promising for addressing false news

Loupe: Verifying Publish-Subscribe Architectures with a Magnifying Lens

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