Feature-interaction detection based on feature-based specifications

A Gd3+-coordinated polymerizable analogue of the MRI contrast agent Gd-DOTA was used to prepare amphiphilic block copolymers, with hydrophilic blocks composed entirely of the polymerized contrast agent. The resulting amphiphilic block copolymers assemble into nanoparticles (NPs) of spherical- or fibril-shape, each demonstrating enhanced relaxivity over Gd-DOTA. As an initial examination of their behavior in vivo, intraperitoneal (IP) injection of NPs into live mice was performed, showing long IP residence times, observed by MRI. Extended residence times for particles of well-defined morphology may represent a valuable design paradigm for treatment or diagnosis of peritoneal malignances

Automated analysis of feature models: Quo vadis?

Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía TIC-186

Detecting Feature-Interaction Symptoms in Automotive Software Using Lightweight Analysis

Copyright (c) 2019 IEEE Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Modern automotive software systems are large, com- plex, and feature rich; they can contain over 100 million lines of code, comprising hundreds of features distributed across multiple electronic control units (ECUs), all operating in parallel and communicating over a CAN bus. Because they are safety-critical systems, the problem of possible Feature Interactions (FIs) must be addressed seriously; however, traditional detection approaches using dynamic analyses are unlikely to scale to the size of these systems. We are investigating an approach that detects static source-code patterns that are symptomatic of FIs. The tools report Feature-Interaction warnings, which can be investigated further by engineers to determine if they represent true FIs and if those FIs are problematic. In this paper, we present our preliminary toolchain for FI detection. First, we extract a collection of static “facts” from the source code, such as function calls, variable assignments, and messages between features. Next, we perform relational algebra transformations on this factbase to infer additional “facts” that represent more complicated design information about the code, such as potential information flows and data dependencies; then, the full collection of “facts” is matched against a curated set of patterns for FI symptoms. We present a set of five patterns for FIs in automotive software as well a case study in which we applied our tools to the Autonomoose autonomous-driving software, developed at the University of Waterloo. Our approach identified 1,444 possible FIs in this codebase, of which 10% were classified as being probable interactions worthy of further investigation.General Motors Research Project, GAC 2453 Ontario Research Fund, RE05-04

Understanding Variability-Aware Analysis in Low-Maturity Variant-Rich Systems

Context: Software systems often exist in many variants to support varying stakeholder requirements, such as specific market segments or hardware constraints. Systems with many variants (a.k.a. variant-rich systems) are highly complex due to the variability introduced to support customization. As such, assuring the quality of these systems is also challenging since traditional single-system analysis techniques do not scale when applied. To tackle this complexity, several variability-aware analysis techniques have been conceived in the last two decades to assure the quality of a branch of variant-rich systems called software product lines. Unfortunately, these techniques find little application in practice since many organizations do use product-line engineering techniques, but instead rely on low-maturity \clo~strategies to manage their software variants. For instance, to perform an analysis that checks that all possible variants that can be configured by customers (or vendors) in a car personalization system conform to specified performance requirements, an organization needs to explicitly model system variability. However, in low-maturity variant-rich systems, this and similar kinds of analyses are challenging to perform due to (i) immature architectures that do not systematically account for variability, (ii) redundancy that is not exploited to reduce analysis effort, and (iii) missing essential meta-information, such as relationships between features and their implementation in source code.Objective: The overarching goal of the PhD is to facilitate quality assurance in low-maturity variant-rich systems. Consequently, in the first part of the PhD (comprising this thesis) we focus on gaining a better understanding of quality assurance needs in such systems and of their properties.Method: Our objectives are met by means of (i) knowledge-seeking research through case studies of open-source systems as well as surveys and interviews with practitioners; and (ii) solution-seeking research through the implementation and systematic evaluation of a recommender system that supports recording the information necessary for quality assurance in low-maturity variant-rich systems. With the former, we investigate, among other things, industrial needs and practices for analyzing variant-rich systems; and with the latter, we seek to understand how to obtain information necessary to leverage variability-aware analyses.Results: Four main results emerge from this thesis: first, we present the state-of-practice in assuring the quality of variant-rich systems, second, we present our empirical understanding of features and their characteristics, including information sources for locating them; third, we present our understanding of how best developers\u27 proactive feature location activities can be supported during development; and lastly, we present our understanding of how features are used in the code of non-modular variant-rich systems, taking the case of feature scattering in the Linux kernel.Future work: In the second part of the PhD, we will focus on processes for adapting variability-aware analyses to low-maturity variant-rich systems.Keywords:\ua0Variant-rich Systems, Quality Assurance, Low Maturity Software Systems, Recommender Syste

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