Aspect-Oriented State Machines

UML state machines are a widely used language for modeling software behavior. They are considered to be simple and intuitively comprehensible, and are hence one of the most popular languages for modeling reactive components. However, this seeming ease to use vanishes rapidly as soon as the complexity of the system to model increases. In fact, even state machines modeling ``almost trivial'' behavior may get rather hard to understand and error-prone. In particular, synchronization of parallel regions and history-based features are often difficult to model in UML state machines. We therefore propose High-Level Aspect (HiLA), a new, aspect-oriented extension of UML state machines, which can improve the modularity, thus the comprehensibility and reusability of UML state machines considerably. Aspects are used to define additional or alternative system behaviors at certain ``interesting'' points of time in the execution of the state machine, and achieve a high degree of separation of concerns. The distinguishing feature of HiLA w.r.t. other approaches of aspect-oriented state machines is that HiLA aspects are defined on a high, i.e. semantic level as opposed to a low, i.e. syntactic level. This semantic approach makes \HiLA aspects often simpler and better comprehensible than aspects of syntactic approaches. The contributions of this thesis include 1) the abstract and the concrete syntax of HiLA, 2) the weaving algorithms showing how the (additional or alternative) behaviors, separately modeled in aspects, are composed with the base state machine, giving the complete behavior of the system, 3) a formal semantics for HiLA aspects to define how the aspects are activated and (after the execution) left. We also discuss what conflicts between HiLA aspects are possible and how to detect them. The practical applicability of HiLA is shown in a case study of a crisis management system

An approach for mapping the aspect state models to aspect-oriented code

Model-driven code generation allows rapid generation of precise code thus reducing the development effort and the delivery time. Consequently, model-driven code generation has been a topic of interest in varying perspectives. While model-driven code generation has been explored well in many domains, its full potential has not been exploited in the context of aspect-oriented software development. The existing approaches have mainly focused on code generation from class diagrams only. Code generation from class diagrams is straightforward as majority of the constructs involved in these diagrams are directly mapped to those in programming languages. However, code generated using class diagrams is limited to skeletons of classes and methods only and does not contain behavior. In this study, we use the state diagrams and propose a mapping of its constructs to AspectJ language. We use the Reusable Aspect Models notation for this purpose. The approach addresses the mapping of both structure and behavior, however, owing to their strength with respect to modeling the system behavior, it essentially focuses on the state diagrams encapsulated in these models. A detailed mapping of different features of the state diagrams is proposed. The approach is illustrated by means of aspect models and corresponding mapped code from an aspect-oriented implementation of a Remote Service Caller example. The implementation shows that the approach can effectively be applied to obtain code for complete structure and behavior modeled using Reusable Aspect Models

Scalable Model-based Robustness Testing: Novel Methodologies and Industrial Application

Embedded systems, as for example communication and control systems, are being increasingly used in our daily lives and hence require thorough and systematic testing before their actual use. Many of these systems interact with their environment and, therefore, their functionality is largely dependent on this environment whose behavior can be unpredictable. Robustness testing aims at testing the behavior of a system in the presence of faulty situations in its operating environment (e.g., sensors and actuators). In such situations, the system should gracefully degrade its performance instead of abruptly stopping execution. To systematically perform robustness testing, one option is to resort to Model-Based Robustness Testing (MBRT), which is a systematic, rigorous, and automated way of conducting robustness testing. However, to successfully apply MBRT in industrial contexts, new technologies need to be developed to scale to the complexity of real industrial systems. This thesis presents a solution for MBRT on industrial systems, including scalable robustness modeling and executable test case generation. One important contribution of this thesis is a scalable RobUstness Modeling Methodology (RUMM), which is achieved using Aspect-Oriented Modeling (AOM). It is a complete, automated, and practical methodology that covers all features of state machines and aspect concepts necessary for MBRT. Such methodology, relying on a standard (Unified Modeling Language or UML) and using the target notation as the basis to model the aspects themselves, is expected to make the practical adoption of robustness modeling easier in industrial contexts. The applicability of the methodology is demonstrated using an industrial case study. Results showed that the approach significantly reduced modeling effort (98% on average), improved separation of concerns, and eased model evolution. The approach is further empirically evaluated using two controlled experiments involving human subjects and results showed that the proposed methodology significantly improves the readability of models as compared to modeling using standard UML notations. Another important contribution of this thesis is an efficient approach for solving constraints (written in Objects Constraint Language (OCL)) on the operating environment of a system, which is mandatory for emulating faulty situation in the environment for the purpose of MBRT. A set of novel heuristics is devised for various OCL constructs, which are required for the application of search algorithms. The heuristics have been empirically evaluated on an industrial case study for robustness testing and the results showed to be very promising and significantly better than the existing works in the literature on OCL constraint solvers. A final contribution of the thesis is robustness test case generation from the models developed using RUMM. Test case generation also includes scripts generation for environment emulation, which is mandatory for automated robustness testing again using an industrial case study. In preliminary experiments, the execution of test cases found one critical, robustness fault in a deployed industrial system

Enhancing UML State Machines with Aspects

Separation of Concerns (SoC) is an important issue to reduce the complexity of software. Recent advances in programming language research show that Aspect-Oriented Programming (AOP) may be helpful for enhancing the SoC in software systems: AOP provides a means for describing concerns which are normally spread throughout the whole program at one location. The arguments for introducing aspects into programming languages also hold for modeling languages. In particular, modeling state-crosscutting behavior is insufficiently supported by UML state machines. This often leads to model elements addressing the same concern scattered all over the state machine. We present an approach to aspect-oriented state machines, which show considerably better modularity in modeling state-crosscutting behavior than standard UML state machines