Feature interaction problem

Abstract: It is argued that the goal of the intelligent network (IN) is to accelerate the introduction of new telecommunications features in a multisupplier competitive environment. One major roadblock to fulfilling such requirements is the feature interaction problem-a new feature may interact with existing features in some undesirable ways, resulting in adverse behavior. Feature interaction examples are categorized by their causes, since problems arising from the same cause may have the same solution. These examples and taxonomies are developed to improve understanding of the problem's scope and to provide a benchmark for analyzing the coverage of a proposed approach to solving the problem. Informal definitions of the concepts given, feature, service, and feature interaction, are presented. Prototypical examples of feature interactions and their categorization by causes are also presented. Possible approaches to the problem are discussed. >

Abstract: Distributed Feature Composition (DFC) is a new technology for feature specification and composition, based on a virtual architecture offering benefits analogous to those of a pipe-and-filter architecture. In the DFC architecture, customer calls are processed by dynamically assembled configurations of filter-like components: each component implements an applicable feature, and communicates with its neighbors by featureless internal calls that are connected by the underlying architectural substrate.

Abstract: The problems of defining features, services, and feature interactions in telecommunication systems are discussed. Several ways of classifying feature interactions are surveyed. Existing approaches for solving the feature-interaction problem are reviewed. It is seen that no full approach exists; they are all partial solutions. The approaches are divided into three classes: avoidance, detection, and resolution. Avoidance looks at ways to prevent undesired feature interactions. Detection assumes that feature interactions will be present, and determines methods for identifying and locating them. Resolution assumes that feature interactions will be present and detected, and looks at mechanisms for minimizing their potential adverse effects. >

Abstract: 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 this problem 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.