Abstract: The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.

Abstract: In previous research, we have developed a theoretical framework to help software architects make better decisions when planning software evolution. Our approach is based on representation and analysis of candidate evolution paths—sequences of transitional architectures leading from the current system to a desired target architecture. One problem with this kind of approach is that it imposes a heavy burden on the software architect, who must explicitly define and model these candidate paths. In this paper, we show how automated planning techniques can be used to support automatic generation of evolution paths, relieving this burden on the architect. We illustrate our approach by applying it to a data migration scenario, showing how this architecture evolution problem can be translated into a planning problem and solved using existing automated planning tools.

Abstract: Since conventional software security approaches are often manually developed and statically deployed, they are no longer sufficient against today's sophisticated and evolving cyber security threats. This has motivated the development of self-protecting software that is capable of detecting security threats and mitigating them through runtime adaptation techniques. In this paper, we argue for an architecture-based self- protection (ABSP) approach to address this challenge. In ABSP, detection and mitigation of security threats are informed by an architectural representation of the running system, maintained at runtime. With this approach, it is possible to reason about the impact of a potential security breach on the system, assess the overall security posture of the system, and achieve defense in depth. To illustrate the effectiveness of this approach, we present several architecture adaptation patterns that provide reusable detection and mitigation strategies against well-known web application security threats. Finally, we describe our ongoing work in realizing these patterns on top of Rainbow, an existing architecture-based adaptation framework.

Abstract: Many domains such as scientific computing and neuroscience require end users to compose heterogeneous computational entities to automate their professional tasks. However, an issue that frequently hampers such composition is data-mismatches between computational entities. Although, many composition frameworks today provide support for data mismatch resolution through specialpurpose data converters, end users still have to put significant effort in dealing with data mismatches, e. g., identifying the available converters and determining which of them meet their QoS expectations. In this paper we present an approach that eliminates this effort by automating the detection and resolution of data mismatches. Specifically, it uses architectural abstractions to automatically detect different types of data mismatches, model-generation techniques to fix those mismatches, and utility theory to decide the best fix based on QoS constraints. We illustrate our approach in the neuroscience domain where data-mismatches can be fixed in an efficient manner on the order of few seconds.

Abstract: Recent research has developed a theoretical basis for providing software architects with better support for planning and carrying out major evolutions of software systems. However, these theoretical models have yet to be implemented in a complete, integrated tool. In this paper, we consider some of the challenges involved in developing such a tool as a plug-in to an existing architecture modeling framework. We present an experience report based on the development of a prototype architecture evolution tool as a plug-in to MagicDraw, a commercial UML tool. We find that there are many practical obstacles to developing tools for architecture evolution modeling as plug-ins, and we discuss some of the key factors that plug-in developers should weigh when considering frameworks.

Abstract: Although architecture-based self-adaptation has been widely used, there is still little understanding about the validity and tradeoffs of incorporating it into real-world software-intensive systems which already feature built-in adaptation mechanisms. In this paper, we report on our experience in integrating Rainbow, a platform for architecture-based self-adaptation, and an industrial middleware employed to monitor and manage highly populated networks of devices. Concretely, we reflect on aspects such as the effort required for framework customization and legacy code refactoring, performance improvement, and the impact of architecture-based self-adaptation on system evolution.

Abstract: Many software systems eventually undergo changes to their basic architectural structure. Such changes may be prompted by new feature requests, new quality attribute requirements, changing technology, or other reasons. Whatever the causes, architecture evolution is commonplace in real-world software projects. Today’s software architects, however, have few techniques to help them plan such evolution. In particular, they have little assistance in planning alternatives, making trade-offs among these different alternatives, or applying best practices for particular domains. To address this, we have developed an approach for assisting architects in planning and reasoning about software architecture evolution. Our approach is based on modeling and analyzing potential evolution paths that represent different ways of evolving the system. We represent an evolution path as a sequence of transitional architectural states leading from the initial architecture to the target architecture, along with evolution operators that characterize the transitions among these states. We support analysis of evolution paths through the definition and application of constraints that express rules governing the evolution of the systemand evaluation functions that assess path quality. Finally, a set of these modeling elements may be grouped together into an evolution style that encapsulates a body of knowledge relevant to a particular domain of architecture evolution. We evaluate this approach in three ways. First, we evaluate its applicability to real-world architecture evolution projects. This is accomplished through case studies of two very different software organizations. Second, we undertake a formal evaluation of the computational complexity of verifying evolution constraints. Finally, we evaluate the implementability of the approach based on our experiences developing prototype tools for software architecture evolution.

Abstract: An increasingly essential aspect of many critical software systems is the ability to quickly diagnose and locate faults so that appropriate corrective measures can be taken. Large, complex software systems fail unpredictably and pinpointing the source of the failure is a challenging task. In this paper we explore how our recently developed technique for automatic diagnosis performs in the automatic detection of failures and fault localization in a critical manufacturing control system of Samsung Electronics, where failures can result in large financial and schedule losses. We show how our approach can scale to such systems to diagnose intermittent faults, connectivity problems, protocol violations, and timing failures. We propose a set of measures of accuracy and performance that can be used to evaluate run-time diagnosis. We present lessons learned from this work including how instrumentation limitations may impair diagnosis accuracy: without overcoming these, there is a limit to the kinds of faults that can be detected.

Abstract: Many software systems eventually undergo changes to their basic architectural structure. As systems age, they often require redesign in order to accommodate new requirements, support new technologies, or respond to changing market conditions. At present, however, software architects lack tools to assist them in developing plans for carrying out such evolution.

In previous research, we have developed an approach to support architects in reasoning about evolution. Our approach is based on formally modeling and reasoning about possible evolution paths—sequences of transitional architectures leading from the current state to some desired target architecture.

To date, much of this work has been theoretical in character. We have focused our efforts on the definition and elaboration of a theoretical framework for architecture evolution, and have done relatively little work on applying this theoretical research in practical contexts.

This report presents a case study examining architecture evolution in depth in a real-world software organization: the IT division of a major global retailer. Based on content analysis of interview data and architectural documentation, the case study examines how practicing architects plan and reason about evolution, what challenges they face in doing so, and whether our modeling approach is adequate for capturing the concerns that arise in a real-world evolution. It finds that architects face a variety of pressing challenges in planning evolutions of major software systems, and use a variety of techniques to manage these challenges. It further finds that most of the major considerations that architects described in connection within a specific evolution can be successfully modeled using our approach, suggesting that the conceptual framework and specification languages that we have developed are expressive enough to be applicable to a real-world architecture evolution.

Abstract: This report examined the technical aspects of a future ecosystem-ready software platform for the smart building domain. We analyzed the type of contributors that may exist in a smart building ecosystem, the quality attributes that those roles are concerned with, and the key functional attributes that the software platforms of future smart interviews, use-cases, and architectural scenarios, we identified a collection of key functional capabilities and several architectural trade-offs that need to be evaluated and carefully considered.