Investigating Differences between Graphical and Textual Declarative Process Models

Declarative approaches to business process modeling are regarded as well suited for highly volatile environments, as they enable a high degree of flexibility. However, problems in understanding declarative process models often impede their adoption. Particularly, a study revealed that aspects that are present in both imperative and declarative process modeling languages at a graphical level-while having different semantics-cause considerable troubles. In this work we investigate whether a notation that does not contain graphical lookalikes, i.e., a textual notation, can help to avoid this problem. Even though a textual representation does not suffer from lookalikes, in our empirical study it performed worse in terms of error rate, duration and mental effort, as the textual representation forces the reader to mentally merge the textual information. Likewise, subjects themselves expressed that the graphical representation is easier to understand

Rule-based Test Generation with Mind Maps

This paper introduces basic concepts of rule based test generation with mind maps, and reports experiences learned from industrial application of this technique in the domain of smart card testing by Giesecke & Devrient GmbH over the last years. It describes the formalization of test selection criteria used by our test generator, our test generation architecture and test generation framework.Comment: In Proceedings MBT 2012, arXiv:1202.582

Model-Based Security Testing

Security testing aims at validating software system requirements related to security properties like confidentiality, integrity, authentication, authorization, availability, and non-repudiation. Although security testing techniques are available for many years, there has been little approaches that allow for specification of test cases at a higher level of abstraction, for enabling guidance on test identification and specification as well as for automated test generation. Model-based security testing (MBST) is a relatively new field and especially dedicated to the systematic and efficient specification and documentation of security test objectives, security test cases and test suites, as well as to their automated or semi-automated generation. In particular, the combination of security modelling and test generation approaches is still a challenge in research and of high interest for industrial applications. MBST includes e.g. security functional testing, model-based fuzzing, risk- and threat-oriented testing, and the usage of security test patterns. This paper provides a survey on MBST techniques and the related models as well as samples of new methods and tools that are under development in the European ITEA2-project DIAMONDS.Comment: In Proceedings MBT 2012, arXiv:1202.582

How Courts Adjudicate Patent Definiteness and Disclosure

Section 112 of the Patent Act requires patentees to clearly explain what their invention is (a requirement known as claim definiteness), as well as how to make and use it (the disclosure requirements of enablement and written description). Many concerns about the modern patent system stem from these requirements. But despite the critical importance of § 112 to the functioning of the patent system, there is surprisingly little empirical data about how it has been applied in practice. To remedy the reliance on anecdotes, we have created a hand-coded dataset of 1144 reported court decisions from 1982 to 2012 in which U.S. district courts or the Court of Appeals for the Federal Circuit rendered a decision on the enablement, written-description, or claim-definiteness requirements of § 112. We coded validity outcomes under these three doctrines on a novel five-level scale so as to capture significant subtlety in the strength of each decision, and we also classified patents by technology and industry categories. We also coded for a number of litigation characteristics that could arguably influence outcomes. Although one must be cautious about generalizing from reported decisions due to selection effects, our results show some statistically significant disparities in § 112 outcomes for different technologies and industries—although fewer than the conventional wisdom suggests, and not always in the direction that many have believed. Just as importantly, our analysis reveals significant relationships between other variables and § 112 litigation outcomes, including whether a district court or the Federal Circuit made the last decision in a case, whether a patent claim was drafted in means-plus-function format, and whether a case was decided before or after Markman v. Westview Instruments. Our results showing how § 112 has been applied in practice will be helpful in evaluating current proposals for reform, and our rich dataset will enable more systematic studies of these critical doctrines in the future

Optimal Data Split Methodology for Model Validation

The decision to incorporate cross-validation into validation processes of mathematical models raises an immediate question - how should one partition the data into calibration and validation sets? We answer this question systematically: we present an algorithm to find the optimal partition of the data subject to certain constraints. While doing this, we address two critical issues: 1) that the model be evaluated with respect to predictions of a given quantity of interest and its ability to reproduce the data, and 2) that the model be highly challenged by the validation set, assuming it is properly informed by the calibration set. This framework also relies on the interaction between the experimentalist and/or modeler, who understand the physical system and the limitations of the model; the decision-maker, who understands and can quantify the cost of model failure; and the computational scientists, who strive to determine if the model satisfies both the modeler's and decision maker's requirements. We also note that our framework is quite general, and may be applied to a wide range of problems. Here, we illustrate it through a specific example involving a data reduction model for an ICCD camera from a shock-tube experiment located at the NASA Ames Research Center (ARC).Comment: Submitted to International Conference on Modeling, Simulation and Control 2011 (ICMSC'11), San Francisco, USA, 19-21 October, 201