A high abstraction level approach for detecting feature interactions between telecommunication services

AbstractWhen several telecommunication services are running at the same time, undesirable behaviors may arise, which are commonly called feature interactions. Several methods have been developed for detecting and resolving feature interactions. However, most of these methods are based on detailed models of services, which make them suffer from state space explosion. Moreover, different telecommunication operators cannot cooperate to manage feature interactions by exchanging detailed service models because this violates the confidentiality principle. Our work is a part of the few attempts to develop feature interaction detection methods targeting to avoid or reduce significantly state space explosion. In order to reach this objective, we first develop a so called Cause–Restrict language to model subscribers of telecommunication services at a very high abstraction level. A Cause–Restrict model of a subscriber provides information such as: what is the cause of what, and what restricts (or forbids) what, and specifies coarsely the frequency of each operation “cause” or “restrict” by “always” or “sometimes”. Then, we develop a method that detects feature interactions between telecommunication services modeled in the Cause–Restrict language. We demonstrate the applicability of our approach by modeling several services and detecting several feature interactions between them. New feature interactions have been detected by our approach

A Feature-Oriented Modelling Language and a Feature-Interaction Taxonomy for Product-Line Requirements

Many organizations specialize in the development of families of software systems, called software product lines (SPLs), for one or more domains (e.g., automotive, telephony, health care). SPLs are commonly developed as a shared set of assets representing the common and variable aspects of an SPL, and individual products are constructed by assembling the right combinations of assets. The feature-oriented software development (FOSD) paradigm advocates the use of system features as the primary unit of commonality and variability among the products of an SPL. A feature represents a coherent and identifiable bundle of system functionality, such as call waiting in telephony and cruise control in an automobile. Furthermore, FOSD aims at feature-oriented artifacts (FOAs); that is, software-development artifacts that explicate features, so that a clear mapping is established between a feature and its representation in different artifacts. The thesis first identifies the problem of developing a suitable language for expressing feature-oriented models of the functional requirements of an SPL, and then presents the feature-oriented requirements modelling language (FORML) as a solution to this problem. FORML's notation is based on standard software-engineering notations (e.g., UML class and state-machine models, feature models) to ease adoption by practitioners, and has a precise syntax and semantics to enable analysis. The novelty of FORML is in adding feature-orientation to state-of-the-art requirements modelling approaches (e.g., KAOS), and in the systematic treatment of modelling evolutions of an SPL via enhancements to existing features. An existing feature can be enhanced by extending or modifying its requirements. Enhancements that modify a feature's requirements are called intended feature interactions. For example, the call waiting feature in telephony intentionally overrides the basic call service feature's treatment of incoming calls when the subscriber is already involved in a call. FORML prescribes different constructs for specifying different types of enhancements in state-machine models of requirements. Furthermore, unlike some prominent approaches (e.g., AHEAD, DFC), FORML's constructs for modelling intended feature interactions do not depend on the order in which features are composed; this can lead to savings in analysis costs, since only one rather than (possibly) multiple composition orders need to be analyzed. A well-known challenge in FOSD is managing feature interactions, which, informally defined, are ways in which different features can influence one another in defining the overall properties and behaviours of their combination. Some feature interactions are intended, as described above, while other feature interactions are unintended: for example, the cruise control and anti-lock braking system features of an automobile may have incompatible affects on the automobile's acceleration, which would make their combination inconsistent. Unintended feature interactions should be detected and resolved. To detect unintended interactions in models of feature behaviour, we must first define a taxonomy of feature interactions for the modelling language: that is, we must understand the different ways that feature interactions can manifest among features expressed in the language. The thesis presents a taxonomy of feature interactions for FORML that is an adaptation of existing taxonomies for operational models of feature behaviour. The novelty of the proposed taxonomy is that it presents a definition of behaviour modification that generalizes special cases found in the literature; and it enables feature-interaction analyses that report only unintended interactions, by excluding interactions caused by FORML's constructs for modelling intended feature interactions

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