Abstract
Performing experiments with Internet-of-Things edge devices is not always a trivial task, as large physical testbeds or complex simulators are often needed, leading to low reproducibility and several difficulties in crafting complex scenarios and tweaking parameters. Most available simulators try to simulate as close to reality as possible. While we agree that this kind of high-fidelity simulation might be necessary for some scenarios, we argue that a low-fidelity easy-to-change simulator may be a good solution when rapid prototyping orchestration strategies and algorithms. In this work, we introduce SIMoT, a low-fidelity orchestrator simulator created to achieve shorter feedback loops when testing different orchestration strategies for task allocation in edge devices. We then transferred the simulator-validated algorithms to both physical and virtual testbeds, where it was possible to assert that the simulator results correlate strongly with the observations on those testbeds.

Abstract
This work takes as a starting point a collection of patterns for engineering software for the cloud and tries to find how they are regarded and adopted by professionals. Existing literature assesses the adoption of cloud computing with a focus on business and technological aspects and falls short in grasping a holistic view of the underlying approaches. Other authors delve into how independent patterns can be discovered (mined) and verified, but do not provide insights on their adoption. We investigate (1) the relevance of the patterns for professional software developers, (2) the extent to which product and company characteristics influence their adoption, and (3) how adopting some patterns might correlate with the likelihood of adopting others. For this purpose, we survey practitioners using an online questionnaire (n = 102). Among other findings, we conclude that most companies use these patterns, with the overwhelming majority (97 percent) using at least one. We observe that the mean pattern adoption tends to increase as companies mature, namely when varying the product operation complexity, active monthly users, and company size. Finally, we search for correlations in the adoption of specific patterns and attempt to infer causation, providing further clues on how some practices depend or influence the adoption of others. We conclude that the adoption of some practices correlates with specific company and product characteristics, and find relationships between the patterns that were not covered by the original pattern language and which might deserve further investigation.

Abstract
The current complexity of IoT systems and devices is a barrier to reach a healthy ecosystem, mainly due to technological fragmentation and inherent heterogeneity. Meanwhile, the field has scarcely adopted any engineering practices currently employed in other types of large-scale systems. Although many researchers and practitioners are aware of the current state of affairs and strive to address these problems, compromises have been hard to reach, making them settle for sub-optimal solutions. This paper surveys the current state of the art in designing and constructing IoT systems from the software engineering perspective, without overlooking hardware concerns, revealing current trends and research directions.

Abstract
The widespread use of Internet-of-Things (IoT) across different application domains leads to an increased concern regarding their dependability, especially as the number of potentially mission-critical systems becomes considerable. Fault-tolerance has been used to reduce the impact of faults in systems, and their adoption in IoT is becoming a necessity. This work focuses on how to exercise fault-tolerance mechanisms by deliberately provoking its malfunction. We start by describing a proof-of-concept fault-injection add-on to a commonly used publish/subscribe broker. We then present several experiments mimicking real-world IoT scenarios, focusing on injecting faults in systems with (and without) active self-healing mechanisms and comparing their behavior to the baseline without faults. We observe evidence that fault-injection can be used to (a) exercise in-place fault-tolerance apparatus, and (b) detect when these mechanisms are not performing nominally, providing insights into enhancing in-place fault-tolerance techniques.

Abstract
Designers often use physical hand-drawn mockups to convey their ideas to stakeholders. Unfortunately, these sketches do not depict the exact final look and feel of web pages, and communication errors will often occur, resulting in prototypes that do not reflect the stakeholder's vision. Multiple suggestions exist to tackle this problem, mainly in the translation of visual mockups to prototypes. Some authors propose end-to-end solutions by directly generating the final code from a single (black-box) Deep Neural Network. Others propose the use of object detectors, providing more control over the acquired elements but missing out on the mockup's layout. Our approach provides a real-time solution that explores: (1) how to achieve a large variety of sketches that would look indistinguishable from something a human would draw, (2) a pipeline that clearly separates the different responsibilities of extracting and constructing the hierarchical structure of a web mockup, (3) a methodology to segment and extract containers from mockups, (4) the usage of in-sketch annotations to provide more flexibility and control over the generated artifacts, and (5) an assessment of the synthetic dataset impact in the ability to recognize diagrams actually drawn by humans. We start by presenting an algorithm that is capable of generating synthetic mockups. We trained our model (N=8400, Epochs=400) and subsequently fine-tuned it (N=74, Epochs=100) using real human-made diagrams. We accomplished a mAP of 95.37%, with 90% of the tests taking less than 430ms on modest commodity hardware (approximate to 2.3fps). We further provide an ablation study with well-known object detectors to evaluate the synthetic dataset in isolation, showing that the generator achieves a mAP score of 95%, approximate to 1.5 x higher than training using hand-drawn mockups alone.