Control-based approaches to self-adaptive software-intensive systems (SASs) are hailed for their optimal performance and theoretical guarantees on the reliability of adaptation behavior. However, in practice the guarantees are often threatened by model deviations occurred at runtime. In this paper, we propose a Model-guided Deviation Detector (MoD2) for timely and accurate detection of model deviations. To ensure reliability, a SAS can switch a control-based optimal controller for a mandatory controller once an unsafe model deviation is detected. MoD2 achieves both high timeliness and high accuracy through a deliberate fusion of parameter deviation estimation, uncertainty compensation, and safe region quantification. Empirical evaluation with three exemplar systems validated the efficacy of MoD2 (93.3% shorter detection delay, 39.4% lower FN rate, and 25.2% lower FP rate), as well as the benefits of the adaptation-switching mechanism (abnormal rate dropped by 29.2%).

Context-Aware Systems (a.k.a. CASs) integrate cyber and physical space to provide context-aware adaptive functionalities. Building context-aware systems is challenging due to the uncertainty of the real physical environment. Therefore, input validation for context-aware systems plays a significant role in keeping the systems executing safely. Input validation approaches have been proposed to monitor and guard the executions of context-aware systems. However, few of these works (17%, 2 out of 12) evaluated their approaches with a real context-aware system in a real physical environment. In this paper, we study and compare the effectiveness of input validation approaches for context-aware system in both a simulated and a physical environment. We built a testing platform, RM-Testing, based on DJI RoboMaster S1 robot car. We implemented three up-to-date input validation approaches, and evaluated their effectiveness in improving the success rate of the robot car’s executions. The results show that the selected input validation approaches are effective in guarantee the safe execution of context-aware systems, which improve the success rate by 82% in the simulated environment, and 50% in the physical environment. However, the effectiveness of these approaches does vary in different environment. Thus, we believe that such CASs-based input validation works should be evaluated in the physical environment to better validate their effectiveness and usefulness.

Self-adaptation is a promising approach to enable software systems to address the challenge of uncertainty. Different from traditional reactive adaptation mechanisms that focus on the system’s current environment state only, proactive adaptation mechanisms predict the potential environmental changes and make better adaptation plan accordingly. Proactive Latency-aware Adaptation (PLA for shot) and Control-based Requirements-oriented Adaptation (CobRA for short) are two representative approaches to build proactive self-adaptation mechanisms. Despite their different design and implementation details, PLA and CobRA are reported to have a very similar performance in supporting self-adaptation. In this paper, we conduct an in-depth comparison between these two approaches, trying to explain their effectiveness. We separate a proactive self-adaptation mechanism into three modules, namely system modelling, environment predicting, and uncertainty filtering. We identify the design choices of PLA and CobRA approaches, in terms of these three modules. We performed an ablation study on the three modules of PLA and compared their performance with CobRA. Our study reveals the very important role of uncertainty filtering in supporting self-adaptation, as well as the huge impact of a fluctuant environment on a self-adaptation mechanism. Based on this observation, we briefly discuss a conceptual self-adaptation mechanism, MAPE-U (monitoring, analyzing, planning, executing with uncertainty).

Cyber-physical software delivers context-aware services through continually interacting with its physical environment and adapting to the changing surroundings. However, when the software's assumptions on the environment no longer hold, the interactions can introduce errors for leading to unexpected behaviors and even system failures. One promising solution to this problem is to conduct runtime monitoring of invariants. Violated invariants reflect latent erroneous states (i.e., abnormal states that could lead to failures). In turn, monitoring when program executions violate the invariants can allow the software to take alternative measures to avoid danger. In this article, we present context-based Multiinvariant detection (CoMID), an approach that automatically infers invariants and detects abnormal states for cyber-physical programs. CoMID consists of two novel techniques, namely context-based trace grouping and multiinvariant detection. The former infers contexts to distinguish different effective scopes for CoMID's derived invariants, and the latter conducts ensemble evaluation of multiple invariants to detect abnormal states during runtime monitoring. We evaluate CoMID on real-world cyber-physical software. The results show that CoMID achieves a 5.7-28.2% higher true-positive rate and a 6.8-37.6% lower false-positive rate in detecting abnormal states, as compared with the existing approaches. When deployed in field tests, CoMID's runtime monitoring improves the success rate of cyber-physical software in its task executions by 15.3-31.7%.

Self-adaptive systems (a.k.a. SASs) are useful but error-prone. This stems from the complexity of the interaction between a self-adaptive system and its running environment. Therefore, a testing approach of self-adaptive system has to consider the system's running environment. However, due to their poor controllability and observability, neither the real environment nor the environmental simulators could support SAS-testing effectively and efficiently. In this paper, we propose a novel approach AutoModel to generate environmental models for testing self-adaptive systems effectively. Our key insight is that a self-adaptive system's execution traces naturally encode the behavior of its running environment, especially for the logic of how the environment interacts with the system. Based on the collected execution traces, our AutoModel approach synthesizes an environmental model and learns the model's reaction logic. The derived environmental model is able to imitate the real environment's behavior in program-environment iteration. Our primitive evaluation on real-world self-adaptive systems validates the effectiveness of our AutoModel approach. The average predictive R-squared value of the generated environmental model's prediction results is 55.0%.

Machine learning (ML) programs are being widely used in various human-related applications. However, their testing always remains to be a challenging problem, and one can hardly decide whether and how the existing knowledge extracted from training scenarios suit new scenarios. Existing approaches typically have restricted usages due to their assumptions on the availability of an oracle, comparable implementation, or manual inspection efforts. We solve this problem by proposing a novel program synthesis based approach, SynEva, that can systematically construct an oracle-alike mirror program for similarity measurement, and automatically compare it with the existing knowledge on new scenarios to decide how the knowledge suits the new scenarios. SynEva is lightweight and fully automated. Our experimental evaluation with real-world data sets validates SynEva's effectiveness by strong correlation and little overhead results. We expect that SynEva can apply to, and help evaluate, more ML programs for new scenarios.

Content-based communication is a novel communication paradigm that enables users to communicate with others based on message's content, instead of message's address. Groupware is a kind of software that supports coordination between individual users. An important feature of groupware is that the communication between the specified users is at a high frequency, which is determined by the applied coordination mechanism. Efficient communication in a groupware can support effective coordination between the users. This paper presents a topology-enhanced content-based network, which combines content-based communication with the topology between groupware users, to support content-based communication in groupware. We give a predicate-based method to define cooperation topology, which can effectively describe the topology between groupware users. We also propose a multi-level index structure to support efficient matching of cooperation topology in the forwarding mechanism of the proposed network. We implement the basic feature of our network and evaluate the prototype in a motivating scenario consisting of several coordination tasks. The results show that our method improves the message forwarding efficiency of 1 to 2 magnitude orders.