Supporting the migration towards model-driven robotic systems

Robots are increasingly deployed to perform every-day tasks. It is crucial to implement reliable and reusable systems to reduce development effort. The complexity of robotic systems requires the collaboration of experts from different backgrounds. Therefore, clear and communicatable abstraction of components is essential for successful development process. There has been a demand in the community for increased adoption of software engineering approaches to support better robotic systems. Adopting model-driven approaches has been proved successful in supporting this movement. We aim to support the adaptation of model-driven approaches in robotic domain in three interest areas: behavior models, structural models and guaranteeing confidence in system behavior.The overall goal is to support the creation of reusable, verifiable and easy to communicate robotic missions and systems. To achieve that, we conducted a mix of knowledge-seeking and solution-seeking studies. We started with behavior models. We wanted to build knowledge about used behavior models in practice. We investigated the state-of-practice of an emerging behavior model, behavior trees, in comparison to two standardized UML models and a traditional roboticists choice. Moving to the second interest area, we wanted to support the creation of light-weight tools for building an understanding of system structure using feature models. We conducted a pilot evaluation of an already light-weight tool, called FeatureVista. The final interest area was guaranteeing confidence in system behavior. The usual engineering process of self-adaptive controllers in robotic involves different model-based approaches. We wanted to investigate an approach that reaffirm, at code-level, control properties while keeping the usual engineering process. We investigated an approach for mapping control properties to software ones using an appropriate input format for software model-based checking.Our investigations in the different interest areas have built knowledge and shed light on opportunities. We provided characteristics of behavior models, behavior trees and state machines, in popular robotic implementations and highlighted opportunities for improvements. We also provided usage trend for studied implementations in open-source projects. In addition, we provided corestructural characteristic and code-reuse patterns for studied behavior models in open-source projects. For feature models, our results showed promising results for using an interactive tool that provides an easy and initiative navigation between feature models and software components. Improvement aspects were also highlighted for developing similar tools. Finally, our work for the confidence of system behavior showed promising results in reaffirming the correctness of a control property at code-level using appropriate software notation, specification patterns. Also, our approach allowed keeping the current practices of using model-based approaches in self-adaptive robotic systems

Identifying Related Clinical Trials by Using Graph Database and Clustering

In this work we investigate two methods to find similar clinical trials; creating a graph database to migrate clinical trials meta-data from relational database, and clustering clinical trials. We succeeded in identifying similar groups of clinical trials using the clustering method. However, we were not able to evaluate migrating the meta-data into graph database method due to limitations of the chosen software

FeatureVista: interactive feature visualization

Comprehending and characterizing the spread and interaction of features in a software system is know to be difficult and error-prone. This paper presents FeatureVista, a lightweight tool providing interactive, glyph-based, and iconic visualization concepts designed to visually characterize the feature locations in software assets (source code). FeatureVista supports navigating between software components and features in an equal fashion. Our pilot study indicates that FeatureVista is intuitive and supports comprehending features. It helps to precisely characterize relations among features in large software systems and to contrast explicit software component definitions (e.g., package, class, method) with annotated feature portions---which so far was a largely manual and error-prone activity, albeit essential to get an adequate understanding of a software system. We suggest research directions for true, feature-oriented interfaces that can be used to manage software assets

Behavior Trees and State Machines in Robotics Applications

Autonomous robots combine skills to form increasingly complex behaviors, called missions. While skills are often programmed at a relatively low abstraction level, their coordination is architecturally separated and often expressed in higher-level languages or frameworks. State machines have been the go-to language to model behavior for decades, but recently, behavior trees have gained attention among roboticists. Originally designed to model autonomous actors in computer games, behavior trees offer an extensible tree-based representation of missions and are claimed to support modular design and code reuse. Although several implementations of behavior trees are in use, little is known about their usage and scope in the real world. How do concepts offered by behavior trees relate to traditional languages, such as state machines? How are concepts in behavior trees and state machines used in actual applications? This paper is a study of the key language concepts in behavior trees as realized in domain-specific languages (DSLs), internal and external DSLs offered as libraries, and their use in open-source robotic applications supported by the Robot Operating System (ROS). We analyze behavior-tree DSLs and compare them to the standard language for behavior models in robotics: state machines. We identify DSLs for both behavior-modeling languages, and we analyze five in-depth. We mine open-source repositories for robotic applications that use the analyzed DSLs and analyze their usage. We identify similarities between behavior trees and state machines in terms of language design and the concepts offered to accommodate the needs of the robotics domain. We observed that the usage of behavior-tree DSLs in open-source projects is increasing rapidly. We observed similar usage patterns at model structure and at code reuse in the behavior-tree and state-machine models within the mined open-source projects. We contribute all extracted models as a dataset, hoping to inspire the community to use and further develop behavior trees, associated tools, and analysis techniques

Towards Mapping Control Theory and Software Engineering Properties using Specification Patterns

A traditional approach to realize self-adaptation in software engineering (SE) is by means of feedback loops. The goals of the system can be specified as formal properties that are verified against models of the system. On the other hand, control theory (CT) provides a well-established foundation for designing feedback loop systems and providing guarantees for essential properties, such as stability, settling time, and steady state error. Currently, it is an open question whether and how traditional SE approaches to self-adaptation consider properties from CT. Answering this question is challenging given the principle differences in representing properties in both fields. In this paper, we take a first step to answer this question. We follow a bottom up approach where we specify a control design (in Simulink) for a case inspired by Scuderia Ferrari (F1) and provide evidence for stability and safety. The design is then transferred into code (in C) that is further optimized. Next, we define properties that enable verifying whether the control properties still hold at code level. Then, we consolidate the solution by mapping the properties in both worlds using specification patterns as common language and we verify the correctness of this mapping. The mapping offers a reusable artifact to solve similar problems. Finally, we outline opportunities for future work, particularly to refine and extend the mapping and investigate how it can improve the engineering of self-adaptive systems for both SE and CT engineers