Towards a holistic software systems engineering approach for dependable autonomous systems

Abstract

Autonomous systems are gaining momentum in various application domains, such as autonomous vehicles, autonomous transport robotics and self-adaptation in smart homes. Product liability regulations impose high standards on manufacturers of such systems with respect to dependability (safety, security and privacy). Today's conventional engineering methods are not adequate for providing guarantees with respect to dependability requirements in a costefficient manner, e.g. road tests in the automotive industry sum up millions of miles before a system can be considered sufficiently safe. System engineers will no longer be able to test and respectively formally verify autonomous systems during development time in order to guarantee the dependability requirements in advance. In this vision paper, we introduce a new holistic software systems engineering approach for autonomous systems, which integrates development time methods as well as operation time techniques. With this approach, we aim to give the users a transparent view of the confidence level of the autonomous system under use with respect to the dependability requirements. We present already obtained results and point out research goals to be addressed in the future.

Testing machine learning based systems: a systematic mapping

“…Six of them leverage anomaly detection techniques to identify unexpected execution contexts during the operation of MLSs (Henriksson et al 2019;Patel et al 2018;Aniculaesei et al 2018;Bolte et al 2019;Zhang et al 2018b), whereas two papers are related to online risk assessment and failure probability estimation for MLSs (Strickland et al 2018;Uesato et al 2019).…”

“…Nearly half of the analysed papers (56%) propose and evaluate a technique which is domain-agnostic, Fig. 7 Testing Levels i.e., in principle it may be applicable to any MLS (Aniculaesei et al 2018;Byun et al 2019;Cheng et al 2018a, b;Du et al 2019;Eniser et al 2019;Guo et al 2018;Henriksson et al 2019;Kim et al 2019;Li et al 2018;Ma et al 2018bMa et al , c, d, 2019Murphy et al 2007aMurphy et al , b, 2008Murphy et al , b, 2009Nakajima and Bui 2016, 2019Odena et al 2019;Pei et al 2017;Saha and Kanewala 2019;Sekhon and Fleming 2019;Shen et al 2018;Shi et al 2019;Sun et al 2018a, b;Tian et al 2018;Udeshi and Chattopadhyay 2019;Uesato et al 2019;Xie et al 2018Xie et al , 2019Xie et al , 2011Zhang et al 2018aZhang et al , 2019Zhao and Gao 2018). Around 30% proposed approaches are designed for autonomous systems (Abeysirigoonawardena et al 2019;Beglerovic et al 2017;Bühler and Wegener 2004;Klueck et al 2018;Li et al 2016;Mullins et al 2018;de Oliveira Neves et al 2016;Patel et al 2018;Strickland et al 2018;…”

On Challenges in Engineering IoT Software Systems

“…Another related work is from Aniculaesei et al, which argues that conventional engineering methods are not adequate for providing guarantees to some of the challenges specific of autonomous systems, such as dependability, the focus of their work (Aniculaesei et al 2018). Some of the main points discussed is the possibility of adaptive behavior present in IoT, as they adapt their behavior to better interact with other systems and people or to solve problems more effectively, and variations in the context, the formerly closed and valid development artifacts may not capture the changes and be inadequate since the environment and the system behavior can no longer be fully predicted or described in advance (Aniculaesei et al 2018). In response to these challenges and gaps, the authors propose an approach based on the notion of Dependability Cages.…”

“…At the moment of preparing this manuscript, we observed a lack of more concrete proposals for the materialization of the IoT paradigm. We aim to address the challenges presented in (Alegre et al 2016) and (Sánchez Guinea et al 2016), filling the gaps from (Patel and Cassou 2015), (Costa et al 2017) and (Aniculaesei et al 2018) focusing on the issue of multidisciplinarity and providing support to decision-making in the initial development phase of problem understanding. Figure 1 presents our investigation strategy.…”

“…Our motivation to investigate and contribute to the IoT paradigm is therefore supported by its relevance (CNI 2016;Lu 2017) and the need for a holistic approach and multidisciplinary view for the development of new software solutions (Bauer and Dey 2016;Aniculaesei et al 2018). This reflects in a demand for technical competencies and skills detained by different practitioners to engineer such software systems (Desolda et al 2017;de Farias et al 2017) and the lack of specific software engineering methodologies to support IoT (Zambonelli 2016;Larrucea et al 2017;Jacobson et al 2017).…”