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1. Automated Optimization of Weighted Non-functional Objectives in Self-adaptive Systems

   Bowers, Kate M. ; Fredericks, Erik M. ; Cheng, Betty H. ( January 2018 , Proceedings of the 10th Symposium on Search-Based Software Engineering (SSBSE))

   A self-adaptive system (SAS) can reconfigure at run time in response to adverse combinations of system and environmental conditions in order to continuously satisfy its requirements. Moreover, SASs are subject to cross-cutting non-functional requirements (NFRs), such as performance, security, and usability, that collectively characterize how functional requirements (FRs) are to be satisfied. In many cases, the trigger for adapting an SAS may be due to a violation of one or more NFRs. For a given NFR, different combinations of hierarchically-organized FRs may yield varying degrees of satisfaction (i.e., satisficement). This paper presents Providentia, a search-based technique to optimize NFR satisficement when subjected to various sources of uncertainty (e.g., environment, interactions between system elements, etc.). Providentia searches for optimal combinations of FRs that, when considered with different subgoal decompositions and/or differential weights, provide optimal satisficement of NFR objectives. Experimental results suggest that using an SAS goal model enhanced with search-based optimization significantly improves system performance when compared with manually and randomly-generated weights and subgoals. 

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2. An infrastructure approach to improving effectiveness of Android UI testing tools

   https://doi.org/10.1145/3460319.3464828  

   Wang, Wenyu ; Lam, Wing ; Xie, Tao ( July 2021 , 30th ACM SIGSOFT International Symposium on Software Testing and Analysis)

   null (Ed.)

   Due to the importance of Android app quality assurance, many Android UI testing tools have been developed by researchers over the years. However, recent studies show that these tools typically achieve low code coverage on popular industrial apps. In fact, given a reasonable amount of run time, most state-of-the-art tools cannot even outperform a simple tool, Monkey, on popular industrial apps with large codebases and sophisticated functionalities. Our motivating study finds that these tools perform two types of operations, UI Hierarchy Capturing (capturing information about the contents on the screen) and UI Event Execution (executing UI events, such as clicks), often inefficiently using UIAutomator, a component of the Android framework. In total, these two types of operations use on average 70% of the given test time. Based on this finding, to improve the effectiveness of Android testing tools, we propose TOLLER, a tool consisting of infrastructure enhancements to the Android operating system. TOLLER injects itself into the same virtual machine as the app under test, giving TOLLER direct access to the app’s runtime memory. TOLLER is thus able to directly (1) access UI data structures, and thus capture contents on the screen without the overhead of invoking the Android framework services or remote procedure calls (RPCs), and (2) invoke UI event handlers without needing to execute the UI events. Compared with the often-used UIAutomator, TOLLER reduces average time usage of UI Hierarchy Capturing and UI Event Execution operations by up to 97% and 95%, respectively. We integrate TOLLER with existing state-of-the-art/practice Android UI testing tools and achieve the range of 11.8% to 70.1% relative code coverage improvement on average. We also find that TOLLER-enhanced tools are able to trigger 1.4x to 3.6x distinct crashes compared with their original versions without TOLLER enhancement. These improvements are so substantial that they also change the relative competitiveness of the tools under empirical comparison. Our findings highlight the practicality of TOLLER as well as raising the community awareness of infrastructure support’s significance beyond the community’s existing heavy focus on algorithms. 

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3. Efficient and Adaptive Decentralized Sparse Gaussian Process Regression for Environmental Sampling Using Autonomous Vehicles

   Norton, Tanner A. ( June 2022 , BYU Scholars Archive)

   In this thesis, I present a decentralized sparse Gaussian process regression (DSGPR) model with event-triggered, adaptive inducing points. This DSGPR model brings the advantages of sparse Gaussian process regression to a decentralized implementation. Being decentralized and sparse provides advantages that are ideal for multi-agent systems (MASs) performing environmental modeling. In this case, MASs need to model large amounts of information while having potential intermittent communication connections. Additionally, the model needs to correctly perform uncertainty propagation between autonomous agents and ensure high accuracy on the prediction. For the model to meet these requirements, a bounded and efficient real-time sparse Gaussian process regression (SGPR) model is needed. I improve real-time SGPR models in these regards by introducing an adaptation of the mean shift and fixed-width clustering algorithms called radial clustering. Radial clustering enables real-time SGPR models to have an adaptive number of inducing points through an efficient inducing point selection process. I show how this clustering approach scales better than other seminal Gaussian process regression (GPR) and SGPR models for real-time purposes while attaining similar prediction accuracy and uncertainty reduction performance. Furthermore, this thesis addresses common issues inherent in decentralized frameworks such as high computation costs, inter-agent message bandwidth restrictions, and data fusion integrity. These challenges are addressed in part through performing maximum consensus between local agent models which enables the MAS to gain the advantages of decentralization while keeping data fusion integrity. The inter-agent communication restrictions are addressed through the contribution of two message passing heuristics called the covariance reduction heuristic and the Bhattacharyya distance heuristic. These heuristics enable user to reduce message passing frequency and message size through the Bhattacharyya distance and properties of spatial kernels. The entire DSGPR framework is evaluated on multiple simulated random vector fields. The results show that this framework effectively estimates vector fields using multiple autonomous agents. This vector field is assumed to be a wind field; however, this framework may be applied to the estimation of other scalar or vector fields (e.g., fluids, magnetic fields, electricity, etc.). Keywords: Sparse Gaussian process regression, clustering, event-triggered, decentralized, sensor fusion, uncertainty propagation, inducing points 

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4. Scalable Robust Adaptive Control from the System Level Perspective

   https://doi.org/10.23919/ACC.2019.8814896  

   Ho, Dimitar ; Doyle, John C. ( July 2019 , 2019 American Control Conference (ACC))

   We will present a new general framework for robust and adaptive control that allows for distributed and scalable learning and control of large systems of interconnected linear subsystems. The control method is demonstrated for a linear time-invariant system with bounded parameter uncertainties, disturbances and noise. The presented scheme continuously collects measurements to reduce the uncertainty about the system parameters and adapts dynamic robust controllers online in a stable and performance-improving way. A key enabler for our approach is choosing a time-varying dynamic controller implementation, inspired by recent work on System Level Synthesis [1]. We leverage a new robustness result for this implementation to propose a general robust adaptive control algorithm. In particular, the algorithm allows us to impose communication and delay constraints on the controller implementation and is formulated as a sequence of robust optimization problems that can be solved in a distributed manner. The proposed control methodology performs particularly well when the interconnection between systems is sparse and the dynamics of local regions of subsystems depend only on a small number of parameters. As we will show on a five-dimensional exemplary chain-system, the algorithm can utilize system structure to efficiently learn and control the entire system while respecting communication and implementation constraints. Moreover, although current theoretical results require the assumption of small initial uncertainties to guarantee robustness, we will present simulations that show good closed-loop performance even in the case of large uncertainties, which suggests that this assumption is not critical for the presented technique and future work will focus on providing less conservative guarantees. 

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5. A Flexible Processing-in-Memory Accelerator for Dynamic Channel-Adaptive Deep Neural Networks

   https://doi.org/10.1109/ASP-DAC47756.2020.9045166  

   Yang, Li ; Angizi, Shaahin ; Fan, Deliang ( January 2020 , 2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC))

   With the success of deep neural networks (DNN), many recent works have been focusing on developing hardware accelerator for power and resource-limited embedded system via model compression techniques, such as quantization, pruning, low-rank approximation, etc. However, almost all existing DNN structure is fixed after deployment, which lacks runtime adaptive DNN structure to adapt to its dynamic hardware resource, power budget, throughput requirement, as well as dynamic workload. Correspondingly, there is no runtime adaptive hardware platform to support dynamic DNN structure. To address this problem, we first propose a dynamic channel-adaptive deep neural network (CA-DNN) which can adjust the involved convolution channel (i.e. model size, computing load) at run-time (i.e. at inference stage without retraining) to dynamically trade off between power, speed, computing load and accuracy. Further, we utilize knowledge distillation method to optimize the model and quantize the model to 8-bits and 16-bits, respectively, for hardware friendly mapping. We test the proposed model on CIFAR-10 and ImageNet dataset by using ResNet. Comparing with the same model size of individual model, our CA-DNN achieves better accuracy. Moreover, as far as we know, we are the first to propose a Processing-in-Memory accelerator for such adaptive neural networks structure based on Spin Orbit Torque Magnetic Random Access Memory(SOT-MRAM) computational adaptive sub-arrays. Then, we comprehensively analyze the trade-off of the model with different channel-width between the accuracy and the hardware parameters, eg., energy, memory, and area overhead. 

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