Using aspects for testing of embedded software: experiences from two industrial case studies

Aspect-oriented software testing is emerging as an important alternative to conventional procedural and object-oriented testing techniques. This paper reports experiences from two case studies where aspects were used for the testing of embedded software in the context of an industrial application. In the first study, we used code-level aspects for testing non-functional properties. The methodology we used for deriving test aspect code was based on translating high-level requirements into test objectives, which were then implemented using test aspects in AspectC++. In the second study, we used high-level visual scenario-based models for the test specification, test generation, and aspect-based test execution. To specify scenario-based tests, we used a UML2-compliant variant of live sequence charts. To automatically generate test code from the models, a modified version of the S2A Compiler, outputting AspectC++ code, was used. Finally, to examine the results of the tests, we used the Tracer, a prototype tool for model-based trace visualization and exploration. The results of the two case studies show that aspects offer benefits over conventional techniques in the context of testing embedded software; these benefits are discussed in detail. Finally, towards the end of the paper, we also discuss the lessons learned, including the technological and other barriers to the future successful use of aspects in the testing of embedded software in industry.     

Incremental application of knowledge to continuously arriving time-oriented data

In our previous work, we introduced a computational architecture that effectively supports the tasks of continuous monitoring and of aggregation querying of complex domain meaningful time-oriented concepts and patterns (temporal abstractions), in environments featuring large volumes of continuously arriving and accumulating time-oriented raw data. Examples include provision of decision support in clinical medicine, making financial decisions, detecting anomalies and potential threats in communication networks, integrating intelligence information from multiple sources, etc. In this paper, we describe the general, domain-independent but task-specific problem-solving method underling our computational architecture, which we refer to as incremental knowledge-based temporal abstraction (IKBTA). The IKBTA method incrementally computes temporal abstractions by maintaining persistence and validity of continuously computed temporal abstractions from arriving time-stamped data. We focus on the computational framework underlying our reasoning method, provide well-defined semantic and knowledge requirements for incremental inference, which utilizes a logical model of time, data, and high-level abstract concepts, and provide a detailed analysis of the computational complexity of our approach.     

Modifying continuous-time random walks to model finite-size particle diffusion in granular porous media

The continuous-time random walk (CTRW) model is useful for alleviating the computational burden of simulating diffusion in actual media. In principle, isotropic CTRW only requires knowledge of the step-size, \(P_l\), and waiting-time, \(P_t\), distributions of the random walk in the medium and it then generates presumably equivalent walks in free space, which are much faster. Here we test the usefulness of CTRW to modelling diffusion of finite-size particles in porous medium generated by loose granular packs. This is done by first simulating the diffusion process in a model porous medium of mean coordination number, which corresponds to marginal rigidity (the loosest possible structure), computing the resulting distributions \(P_l\) and \(P_t\) as functions of the particle size, and then using these as input for a free space CTRW. The CTRW walks are then compared to the ones simulated in the actual media. In particular, we study the normal-to-anomalous transition of the diffusion as a function of increasing particle size. We find that, given the same \(P_l\) and \(P_t\) for the simulation and the CTRW, the latter predicts incorrectly the size at which the transition occurs. We show that the discrepancy is related to the dependence of the effective connectivity of the porous media on the diffusing particle size, which is not captured simply by these distributions. We propose a correcting modification to the CTRW model—adding anisotropy—and show that it yields good agreement with the simulated diffusion process. We also present a method to obtain \(P_l\) and \(P_t\) directly from the porous sample, without having to simulate an actual diffusion process. This extends the use of CTRW, with all its advantages, to modelling diffusion processes of finite-size particles in such confined geometries.     

On tracing reactive systems

We present a rich and highly dynamic technique for analyzing, visualizing, and exploring the execution traces of reactive systems. The two inputs are a designer’s inter-object scenario-based behavioral model, visually described using a UML2-compliant dialect of live sequence charts (LSC), and an execution trace of the system. Our method allows one to visualize, navigate through, and explore, the activation and progress of the scenarios as they “come to life” during execution. Thus, a concrete system’s runtime is recorded and viewed through abstractions provided by behavioral models used for its design, tying the visualization and exploration of system execution traces to model-driven engineering. We support both event-based and real-time-based tracing, and use details-on-demand mechanisms, multi-scaling grids, and gradient coloring methods. Novel model exploration techniques include semantics-based navigation, filtering, and trace comparison. The ideas are implemented and tested in a prototype tool called the Tracer.     

Using artificial neural networks to detect unknown computer worms

Detecting computer worms is a highly challenging task. We present a new approach that uses artificial neural networks (ANN) to detect the presence of computer worms based on measurements of computer behavior. We compare ANN to three other classification methods and show the advantages of ANN for detection of known worms. We then proceed to evaluate ANN’s ability to detect the presence of an unknown worm. As the measurement of a large number of system features may require significant computational resources, we evaluate three feature selection techniques. We show that, using only five features, one can detect an unknown worm with an average accuracy of 90%. We use a causal index analysis of our trained ANN to identify rules that explain the relationships between the selected features and the identity of each worm. Finally, we discuss the possible application of our approach to host-based intrusion detection systems.     

Transport of testosterone and estrogen from dairy-farm waste lagoons to groundwater

Although concentrated animal feeding operations constantly generate physiologically active steroidal hormones, little is known of their environmental fate. Estrogen and testosterone concentrations in groundwater and their distribution in sediments below a dairy-farm wastewater lagoon were therefore determined and compared to a reference site located upgradient of the farm. Forward simulations of flow as well as estrogen and testosterone transport were conducted based on data from the sediment profile obtained during drilling of a monitoring well belowthe dairy-farm waste lagoon. Testosterone and estrogen were detected in sediments to depths of 45 and 32 m, respectively. Groundwater samples were directly impacted by the dairy farm, as evidenced by elevated concentrations of nitrate, chloride, testosterone, and estrogen as compared to the reference site. Modeling potential transport of hormones in the vadose zone via advection, dispersion, and sorption could not explain the depths at which estrogen and testosterone were found, suggesting that other transport mechanisms influence hormone transport under field conditions. These mechanisms may involve interactions between hormones and manure as well as preferential flow paths, leading to enhanced transport rates. These types of interactions should be further investigated to understand the processes regulating hormone transport in the subsurface environment and parametrized to forecast long-term fate and transport of steroidal hormones.     

Polymorphic scenario-based specification models: semantics and applications

We present polymorphic scenarios, a generalization of a UML2-compliant variant of Damm and Harel??s live sequence charts (LSC) in the context of object-orientation. Polymorphic scenarios are visualized using (modal) sequence diagrams where lifelines may represent classes and interfaces rather than concrete objects. Their semantics takes advantage of inheritance and interface realization to allow the specification of most expressive, succinct, and reusable universal and existential inter-object scenarios for object-oriented system models. We motivate the use of polymorphic scenarios, formally define their trace-based semantics, and present their application for scenario-based testing and execution, as implemented in the S2A compiler developed at the Weizmann Institute of Science. We further discuss advanced semantic issues arising from the use of scenarios in a polymorphic setting, suggest possible extensions, present a UML profile to support polymorphic scenarios, consider the application of the polymorphic semantics to other variants of scenario-based specification languages, and position our work in the broader context of behavioral subtyping.     

Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets

Behavioral neuroscience underwent a technology-driven revolution with the emergence of machine-vision and machine-learning technologies. These technological advances facilitated the generation of high-resolution, high-throughput capture and analysis of complex behaviors. Therefore, behavioral neuroscience is becoming a data-rich field. While behavioral researchers use advanced computational tools to analyze the resulting datasets, the search for robust and standardized analysis tools is still ongoing. At the same time, the field of genomics exploded with a plethora of technologies which enabled the generation of massive datasets. This growth of genomics data drove the emergence of powerful computational approaches to analyze these data. Here, we discuss the composition of a large behavioral dataset, and the differences and similarities between behavioral and genomics data. We then give examples of genomics-related tools that might be of use for behavioral analysis and discuss concepts that might emerge when considering the two fields together.     

GutSelf: Interindividual Variability in the Processing of Dietary Compounds by the Human Gastrointestinal Tract

Nutritional research is currently entering the field of personalized nutrition, to a large extent driven by major technological breakthroughs in analytical sciences and biocomputing. An efficient launching of the personalized approach depends on the ability of researchers to comprehensively monitor and characterize interindividual variability in the activity of the human gastrointestinal tract. This information is currently not available in such a form. This review therefore aims at identifying and discussing published data, providing evidence on interindividual variability in the processing of the major nutrients, i.e., protein, fat, carbohydrates, vitamins, and minerals, along the gastrointestinal tract, including oral processing, intestinal digestion, and absorption. Although interindividual variability is not a primary endpoint of most studies identified, a significant number of publications provides a wealth of information on this topic for each category of nutrients. This knowledge remains fragmented, however, and understanding the clinical relevance of most of the interindividual responses to food ingestion described in this review remains unclear. In that regard, this review has identified a gap and sets the base for future research addressing the issue of the interindividual variability in the response of the human organism to the ingestion of foods.