SACRE: Supporting contextual requirements' adaptation in modern self-adaptive systems in the presence of uncertainty at runtime

Runtime uncertainty such as unpredictable resource unavailability, changing environmental conditions and user needs, as well as system intrusions or faults represents one of the main current challenges of self-adaptive systems. Moreover, today's systems are increasingly more complex, distributed, decentralized, etc. and therefore have to reason about and cope with more and more unpredictable events. Approaches to deal with such changing requirements in complex today's systems are still missing. This work presents SACRE (Smart Adaptation through Contextual REquirements), our approach leveraging an adaptation feedback loop to detect self-adaptive systems' contextual requirements affected by uncertainty and to integrate machine learning techniques to determine the best operationalization of context based on sensed data at runtime. SACRE is a step forward of our former approach ACon which focus had been on adapting the context in contextual requirements, as well as their basic implementation. SACRE primarily focuses on architectural decisions, addressing self-adaptive systems' engineering challenges. Furthering the work on ACon, in this paper, we perform an evaluation of the entire approach in different uncertainty scenarios in real-time in the extremely demanding domain of smart vehicles. The real-time evaluation is conducted in a simulated environment in which the smart vehicle is implemented through software components. The evaluation results provide empirical evidence about the applicability of SACRE in real and complex software system domains.Comment: 45 pages, journal article, 14 figures, 9 tables, CC-BY-NC-ND 4.0 licens

SACRE: Supporting contextual requirements’ adaptation in modern self-adaptive systems in the presence of uncertainty at runtime

Runtime uncertainty such as unpredictable resource unavailability, changing environmental conditions and user needs, as well as system intrusions or faults represents one of the main current challenges of self-adaptive systems. Moreover, today’s systems are increasingly more complex, distributed, decentralized, etc. and therefore have to reason about and cope with more and more unpredictable events. Approaches to deal with such changing requirements in complex today’s systems are still missing. This work presents SACRE (Smart Adaptation through Contextual REquirements), our approach leveraging an adaptation feedback loop to detect self-adaptive systems’ contextual requirements affected by uncertainty and to integrate machine learning techniques to determine the best operationalization of context based on sensed data at runtime. SACRE is a step forward of our former approach ACon which focus had been on adapting the context in contextual requirements, as well as their basic implementation. SACRE primarily focuses on architectural decisions, addressing selfadaptive systems’ engineering challenges. Furthering the work on ACon, in this paper, we perform an evaluation of the entire approach in different uncertainty scenarios in real-time in the extremely demanding domain of smart vehicles. The real-time evaluation is conducted in a simulated environment in which the smart vehicle is implemented through software components. The evaluation results provide empirical evidence about the applicability of SACRE in real and complex software system domains.Peer ReviewedPostprint (author's final draft

An Ontology-Based Approach To Concern-Specific Dynamic Software Structure Monitoring

Software reliability has not kept pace with computing hardware. Despite the use reliability improvement techniques and methods, faults remain that lead to software errors and failures. Runtime monitoring can improve software reliability by detecting certain errors before failures occur. Monitoring is also useful for online and electronic services, where resource management directly impacts reliability and quality. For example, resource ownership errors can accumulate over time (e. g. , as resource leaks) and result in software aging. Early detection of errors allows more time for corrective action before failures or service outages occur. In addition, the ability to monitor individual software concerns, such as application resource ownership structure, can help support autonomic computing for self-healing, self-adapting and self-optimizing software. This thesis introduces ResOwn - an application resource ownership ontology for interactive session-oriented services. ResOwn provides software monitoring with enriched concepts of application resource ownership borrowed from real-world legal and ownership ontologies. ResOwn is formally defined in OWL-DL (Web Ontology Language Description Logic), verified using an off-the-shelf reasoner, and tested using the call processing software for a small private branch exchange (PBX). The ResOwn Prime Directive states that every object in an operational software system is a resource, an owner, or both simultaneously. Resources produce benefits. Beneficiary owners may receive resource benefits. Nonbeneficiary owners may only manage resources. This approach distinguishes resource ownership use from management and supports the ability to detect when a resource's role-based runtime capacity has been exceeded. This thesis also presents a greybox approach to concern-specific, dynamic software structure monitoring including a monitor architecture, greybox interpreter, and algorithms for deriving monitoring model from a monitored target's formal specifications. The target's requirements and design are assumed to be specified in SDL, a formalism based on communicating extended finite state machines. Greybox abstraction, applicable to both behavior and structure, provides direction on what parts, and how much of the target to instrument, and what types of resource errors to detect. The approach was manually evaluated using a number of resource allocation and ownership scenarios. These scenarios were obtained by collecting actual call traces from an instrumented PBX. The results of an analytical evaluation of ResOwn and the monitoring approach are presented in a discussion of key advantages and known limitations. Conclusions and recommended future work are discussed at the end of the thesis

An Approach to Automatic Detection of Software Failures in Real-Time Systems

Software supervision is an approach to automatic detection of software failures. A software supervisor observes the inputs and outputs of a target system. It uses a model of correct behavior, derived from the target system's requirements specification. Discrepancies between specified and observed behaviors are reported as failures. The tradeoff between the computational complexity of supervision and the latency of failure reporting is discussed in this paper. Supervisor computational complexity can be significantly reduced at the expense of increased failure reporting latency. For applications such as software testing, this is a practical tradeoff. Such a supervisor is called an out-of-time supervisor. This paper describes the data flows, algorithms, operation and evaluation of an out-of-time supervisor for communicating finite state machine based requirements specifications. A prototype supervisor was used to monitor the operation a small telephone exchange control program. For a fail..