Testing abstract behavioral specifications

We present a range of testing techniques for the Abstract Behavioral Specification (ABS) language and apply them to an industrial case study. ABS is a formal modeling language for highly variable, concurrent, component-based systems. The nature of these systems makes them susceptible to the introduction of subtle bugs that are hard to detect in the presence of steady adaptation. While static analysis techniques are available for an abstract language such as ABS, testing is still indispensable and complements analytic methods. We focus on fully automated testing techniques including black-box and glass-box test generation as well as runtime assertion checking, which are shown to be effective in an industrial setting

HATS Abstract Behavioral Specification: The Architectural View

The Abstract Behavioral Specification (ABS) language is a formal, executable, object-oriented, concurrent modeling language intended for behavioral modeling of complex software systems that exhibit a high degree of variation, such as software product lines. We give an overview of the architectural aspects of ABS: a feature-driven development workflow, a formal notion of deployment components for specifying environmental constraints, and a dynamic component model that is integrated into the language. We employ an industrial case study to demonstrate how the various aspects work together in practic

Susceptibility Modeling and Mission Flight Route Optimization in a Low Threat, Combat Environment

Movement and transportation systems are a primary topic in the study of humans and their relationship with the environment. Only a few modes of transportation allow for nearly full freedom of movement that is unconstrained by rigid nodes and networks. Individual human travel (walking, climbing, swimming, etc.) is one example while rotorcraft travel is another. Although other criteria constrain movement, independence from a network allows for a unique examination of human spatial decision-making and choice behavior. This research analyzes helicopter flight route planning in a low threat combat environment with respect to geography. The particular problem addressed, which ultimately concerns the quantitative representation and mapping of helicopter susceptibility in a low threat, combat environment, is assisted by a Geographic Information System (GIS). Prior susceptibility research on helicopters is combined with the spatial analytical functions of a GIS to cartographically model three dimensional flight corridors and routes across four separate areas. GIS optimized flight routing plans that minimize helicopter susceptibility (maximize capability to avoid threats) are then compared to the conventional routes produced by human flight route planners using existing techniques. Findings indicate that although the GIS routes reduce susceptibility costs, they concomitantly decrease route diversity. There was no significant evidence that experience, expertise, landscape familiarity, age, or the amount of time taken to plan had any effect on the spatial character of the routes. Several spatial similarities between conventionally planned routes and GIS optimized routes were revealed that expose potential perceptual limitations imposed by the conventional flight planning paradigm. Implementation of geospatial technology could help eliminate these restrictions

Operationalized Intent for Improving Coordination in Human-Agent Teams

With the increasing capabilities of artificial intelligent agents (AIAs) integrated into multi-agent systems, future concepts include human-agent teams (HATs) in which the members perform fluidly as a coordinated team. Research on coordination mechanisms in HATs is largely focused on AIAs providing information to humans to coordinate better (i.e. coordination from the AIA to the human). We focus on the compliment where AIAs can understand the operator to better synchronize with the operator (i.e. from the human to the AIA). This research focuses specifically on AIA estimation of operator intent. We established the Operationalized Intent framework which captures intent in a manner relevant to operators and AIAs. The core of operationalized intent is a quality goal hierarchy and an execution constraint list. Designing a quality goal hierarchy entails understanding the domain, the operators, and the AIAs. By extending established cognitive systems engineering analyses we developed a method to define the quality goals and capture the situations that influence their prioritization. Through a synthesis of mental model evaluation techniques, we defined and executed a process for designing human studies of intent. This human-in-the-loop study produced a corpus of data which was demonstrated the feasibility of estimating operationalized intent